Since two and a half week I’m not allowed to use my bicycle. This hurts. But I decided to regard these unfavorable circumstances as an opportunity for trying out two things, which I had had on my list for quite a while. First, I switched to public transport for my daily trips (I cannot and do not want to use the car either). Second, I tried out the fully integrated range of products from ESRI for collecting, hosting and visualizing my trips. I made a map-based mobility diary:
This map is the central feature of my ESRI storymap. Click on the map to get there.
The architecture of the backend behind the storymap is straight forward and consists of the following components:
A hosted line feature layer, set up on ArcGIS online. The layer needs to be approved for public data collection and shared with a group. Editing rights for the layer are assigned by group membership.
This layer, together with some more, is loaded into a ArcGIS online web map. This map serves as main interface for visualizing the collected data. The map has interaction capabilities, such as selecting single tracks and exploring the pictures, which are attached to them.
The ArcGIS Collector app is used for tracking purposes. New features can be added either as points, or as so called streams. The sampling rate is defined in the app settings (in my case 3 seconds). During streaming, pictures or other files can be added to the record. After finishing the data collection, the data set is sent to the server. The map is updated instantaneously.
After one week of “field-work”, I have made enough experiences to be able for a short provisional conclusion. Let’s start with the ESRI environment. While the data collection and hosting is really fuss-free, the opportunities for subsequent analyses are limited. The main issue is the loss of almost all information that is usually contained in GPX files, above all time stamps. Consequently, a laborious workaround is required for simple movement data analyses, such as speed per segment. At least the latter can be done by exploding the line features and using the sampling rate for determining the speed for the segments. However, it would be a really nice feature, if the ArcGIS Collector app would provide an option for exporting tracks as GPX! Probably, there is no easier way for collecting and managing data and for some purposes, the functionalities might be sufficient. However, for everyone who wants to do serious movement data analysis, the ESRI package will not satisfy you.
With regard to travel comfort and efficiency, public transport in Salzburg is a nightmare. Travel time becomes waiting time. My daily trip to work takes me a maximum of 8 minutes on my bicycle. With the bus, I’m glad to make it in half an hour. The poor coordination of lines seems to be the main reason for this. As soon as you are forced to change the line, you loose way too much time. Although some routes might be integrated – at least according to timetables – they are not in fact, because the bus is gridlocked in daily congestion. A collection of my first trips illustrates the issue (have an eye on the red dots):
Analysis of speed levels of my trips. The red dots indicate stops, congestions, signalized intersections and transfers.
I plan to further use the system and add to my collection of trips. The storymap, in which the track collection is embedded, can be accessed via the QR code above or by following this link. See you there!
Authoritative, open and commercial road network data have increased their quality substantially over the past couple of years. Numerous applications have driven data producers to invest into the quality of data. This holds equally true for open data sources, with OpenStreetMap as spearhead, where countless (commercial) services that use these data support communities in their mapping efforts; a brief look at this incredible OSM Apps Catalog illustrates this point.
GIGO holds true for routing applications. But what about the quality of the application?
I used to argue fervently that the quality of routing applications is ultimately depending from the quality of the input data. There is no reason for abandoning GIGO for sure, but I want to raise awareness for quality issues in applications that have been primarily tied to data. We need to take a more critical look at routing applications and the quality of results they are delivering. In many cases, these results are the basis for far reaching decisions, such as the calculation of the commuter allowance in Austria, or they are used for benchmarking purposes, such as in this great housing costs calculator.
The nice thing with open data is that results of routing applications, which are based on these data source, can be critically evaluated. For this purpose, I conducted a little investigation that produced interesting outcomes. At least for the investigated examples, we can conclude that sound data do not necessarily lead to valid routing results.
I was interested in intermodal trips and specifically in the travel time calculation. My hypothesis was that travel times in dense urban areas are systematically underestimated for car trips. Moreover, I wanted to check to which degree routing services regard car routes as intermodal trips, where the origin and/or the destination is not accessible for motorized vehicles. I compared two routing applications, which are based on OpenStreetMap and the Austrian authoritative road graph (GIP) respectively. For OSM-based routing, I requested Open Source Routing Machine (OSRM). The national traffic information and routing application Verkehrsauskunft Österreich (VAO) served as second sample; it is based on GIP data. The VAO routing service is available in different clients. I checked the client for the Salzburg region as well as the client for Eastern Austria. Routes in Salzburg and Vienna were calculated in both applications. Each route starts and ends at locations, which are certainly not accessible by car. The results (you can download all GPX files here) are summarized in the following overview (click to enlarge):
Routing result from OSRM (left) and VAO (center). The exported GPX file from VAO was overlayed with the GIP graph (retrieved from national OGD portal). Edges with access for cars = 0 in both directions are mapped as green lines.
For both cases, OSRM visualizes intermodal routes. From start and end point, the shortest connection to the next accessible edge is mapped as foot path. However, OSRM considers only the car trip for the travel time calculation. This might be due to the fact that the OSRM client is not meant to optimize intermodal routes. In contrast, VAO is a powerful intermodal routing service with lots of options in the parametrization of the routing engine. Interestingly, the two tested clients map point to points routes for the car in both cases, which certainly is in contrast to the underlying data. For the calculation of travel time and the generation of navigation instructions, the two clients deliver different results – which is remarkable! For the Salzburg use case, the routing application optimizes a route with 1.2 km distance and 4 minutes travel time. This is, of course, nonsense. Even if the car was parked directly on the road at the closest accessible link. The details for the Vienna use case are way more realistic. The calculated travel time for 815 m trip distance is 18 minutes. Obviously, the foot path to and from the parking area and the search for free parking space are taken into account.
Google Streetview snapshots of the origins and destinations. All of them are not (legally) accessible for regular cars.
In all cases, the data – be it from OSM or GIP – are sound and contain the relevant attributes. While OSRM makes use of it in the visualization, the intermodal trip characteristics is neglected in the travel time calculation. The funny thing with the VAO is that both clients use the same routing engine in the back, which is based on a common data source (GIP), but still produce different results. Nevertheless, the mapped routes are ambiguous in both cases. And the travel time calculation is a bad joke in the Salzburg case. In this regard, the client for Eastern Austria, is obviously smarter, although the values for the delay emerge from a black box.
In summary, this little, explorative investigation made obvious how big the routing applications’ influence is on the quality and validity of the results. It is certainly possible to produce bad results, although the input data are good. This aspect needs to be considered much more, because the conclusions can be terribly wrong. Imagine you are conducting a travel time comparison in the city of Salzburg. While the city is famous for being gridlocked almost everyday, trips within the city can be efficiently made by bicycle. However, this would not become evident, if you were using the VAO routing service for your analysis. This is why we should not only care about good data, but also about good (routing) applications!
A small and inexpensive piece of street furniture can make life for bicyclists easier and more convenient: handles at intersections. There are different design versions on the market, ranging from foot rests (as in Copenhagen) to simple grab handles (as in Heidelberg).
Different design versions of handles: stand-alone food rests with handle (left, middle) and grab handle mounted on a pole (right).
Recently, we were asked where such handles could be installed in a city. Two requirements are essential for the simple version of grab handles: First of all, a pole (e.g. traffic lights) is needed at the intersection for mounting the handle. And secondly, such street furniture only makes sense if some kind of bicycle infrastructure is already available, which provides enough space for cyclists to rest at intersections. How to find such intersections in a city?
Here is how we used a GIS and solved this within a couple of minutes:
1. Data
The City of Salzburg, for which we did this quick analysis, provides access to Open Government Data (OGD) via WFS. We accessed the layer with all traffic lights. Within the city, 3,233 traffic lights are listed. For road information, we used a processed version of the authoritative network graph, which is available via the national OGD portal. Based on different attributes, we derived information on bicycle infrastructure and built the following categories: bicycle way, bicycle lane, mixed way (pedestrians, bicyclists), opened bus lane.
On the left, the location of all traffic lights in the City of Salzburg is mapped. Roads with any kind of bicycle infrastructure are mapped in light green in the right map.
2. Analysis steps
In order to identify junctions in the network, we extracted all start and end points of the lines (edges) in the road network and collapsed congruent points. The road network is represented as center lines. Thus, start and end points of road segments are located in the center of junctions. Since the position accuracy of traffic lights is very high (see map above), we buffered the point locations with 15m and overlayed these buffer polygons with the location of traffic lights. This resulted in a selection of signalized junctions.
Buffered start and end points (blue) are overlayed with locations of traffic lights (red dots).
In a subsequent step the network links were selected for bicycle infrastructure of any kind. In another simplification step we did not distinguish between driving directions. This sub-network was then overlayed with signalized junctions, resulting in our final layer: signalized junctions with any kind of bicycle infrastructure on at least one of the arms. The result is published as a web map (see below).
A Bicycle Observatory facilitates continuous measurements of various parameters of cycling mobility. We are familiar with this kind of observatory-based science from other domains, such as astronomy or climatology. Harvey Miller (2017) proposed the transfer of observatory-based methods to geographic information and suggested a Geographic Information Observatory (GIO) for gaining holistic views of geographic data and underlying processes. The three major keywords in this context are: observation, experimentation and decision-support.
In a recent research project, we took this concept, applied it to cycling mobility and developed concepts for establishing a Bicycle Observatory (BiObs). Some might wonder why this was interesting or necessary from an academic or application-oriented perspective.
The initial input for doing this research came from multiple conversations with decision makers and cycling advocates. Whenever I asked them, if they would know where, when, why and how many cyclists were on the road in their area of responsibility, I hardly ever received an informed answer. This raised my curiosity for two reasons. Firstly, substantial funding is poured into cycling promotion; apparently on a very weak evidence basis. Secondly, data apologist are constantly claiming that we are living in a data-rich world, in which virtually all questions can be easily answered on the basis of available data.
The need for sound cycling data is evident for anyone who is in touch with cycling promotion at a local or regional level or is conducting research in this field. Planning for the current and future mobility demand as well as monitoring the effect of implemented measures requires timely and valid data. Moreover, data on cycling mobility is crucially important for mobility management, efficient cycling promotion and communication campaigns. In short, whenever decisions should have a more solid foundation than good guesses and practical knowledge, sound data are pivotal. In a previous post, I put together some observations on the current status of available cycling data. Here, I would like to put the focus at a higher level of argumentation.
In an article, which has probably gained less attention than it should, Till Koglin and Tom Rye state with reference to Herbert Marcuse (Koglin & Rye 2014: 216):
… only the measurable aspects count. […] some modes of transport are very measurable.
The authors show, how prevailing concepts, methods and transport models emerged from the theory of modernism, where the private car is/was regarded as indicator for prosperity. Thus, transport models were initially designed for motorized (individual and to a lesser degree public) modes only. The size of transport analysis zones (TAZ) in conventional transport models – just to give an example for how these models are tailored to motorized modes – is typically bigger than the average walking or cycling distance (not to speak of additional spatial difficulties, such as MAUP, see Viegas et al. 2009). Thus, cyclists and pedestrians can hardly be captured adequately in macroscopic transport models. In order to set up and calibrate transport models, lots of data are required. Since the focus lies on motorized modes, traffic data are almost exclusively acquired from car traffic and to a smaller extent from public transport. However, the invisibility of cyclists and pedestrians is perpetuated by these selective measurements. Conversely, the growing availability of data increases the quality of transport models and helps to validate their accuracy. The results of these models is translated into planning paradigms and urban developments. No wonder that during the past 70 years cities and regions have been predominately designed for cars. What we see is a manifestation of the theory of modernism with the car at its economic core.
Things have obviously changed over the past couple of years. A younger generation of planners, who did not grew up with the utopian tech-optimism of the post-WW2 era, is taking over responsibilities in politics and administrations. Even more, man-made climate crisis and serious economic, social and health effects of car traffic, which has gone wild, force decision makers to develop sustainable strategies for contemporary and future mobility systems. But how to plan for cycling and walking without adequate models and a lack of suitable data?
This is exactly, where the Bicycle Observatory comes in. Different data sources, which are relevant with regard to cycling mobility are tapped in order to gain a holistic understanding of what drives people to cycle, and vice versa what keeps them from it. For this, we collected technically sensed data, such as trajectories from mobile apps, counting data, and weather data, statistic data, from mobility surveys to socio-demographic census data, as well as community data, acquired in focus groups, expert interviews, feedback apps for citizens and surveys. Inspired by Miller’s GIO concept, we used the geographic coordinates of these data as common reference and linked them on this basis (Loidl et al. 2020). The collected wealth of data allowed us to derive distinct types of cyclists by a mixed method approach that was built upon community data, social science and statistical cluster analysis (Heym et al. 2020). However, when it comes to fully integrating different data types, several conceptual questions remained open. For instance, it is unclear how to relate trajectory data (linear features) to data from stationary counters (point feature). This and some other research questions will keep us busy in the future!
The main goal of the research on a Bicycle Observatory was to lay the foundations for an increased visibility of cyclists in mobility-related data and derived models. New frameworks, such as the UN sustainable development goals (SDGs) challenge the paradigm of modernism in planning, engineering and research. In order to meet the SDGs and to re-design road spaces and cities accordingly, sound data and integrated models are essential for informed decisions. In stark contrast to what data-optimists are repeatedly claiming, the availability and accessibility of relevant data is still the major bottleneck when it comes to cycling. We conducted an international survey among cycling experts (the publication together with the data is currently under preparation) and found that only 11% (N = 325) have all the data they need for doing their work properly. This is in line with previous findings by Aldred et al. (2019) and Steenberghen et al. (2017). The latter report from a survey at national level that, “The average daily distance cycled per person could be produced for 18 countries [out of 30]. Unfortunately, there are statistics which are not in line with expected values and are harder to interpret.” (p.16).
All these results, together with the need for evidence in planning and decision processes suggest that it makes more sense then ever before to collect data and integrate them into a Bicycle Observatory.
As part of the GISMO research project, we conducted a clinical trial, in which we investigated the health effects of promotion measures. All results of this study are now published as a special issue in the scientific journal Journal of Medicine & Science in Sports (Impact Factor 3,255).
The eight published papers cover the following topics:
A review article that summarizes the evidence that was available prior to the GISMO study (Schäfer et al.)
The evidence from GISMO regarding fitness (Reich et al.)
The results of the interventions, implemented as part of the study, with a focus on cardiovascular risk and body composition (Sareban et al.)
The effects of active commuting on quality of life (Neumeier et al.)
A GIS article, which is dedicated to the extraction of the “mobility dose” from a combination of mobility diaries and data from fitness watches (Loidl et al.)
An investigation of the dose-effect relationship (Schmied et al.)
A summary of the study design, together with a presentation of the effort that was necessary to carry out the study (Fernandez et al.)
The results published in this special issue have been gaining considerable attention and triggered promising follow-up activities, such as the co-authoring of a federal mobility research strategy. “Mobility & Health” is one out of eight research fields, which is going to be pushed over the next years. Based on the experiences from the GISMO project, the necessity for and benefit of cross-section research is particularly stressed in the position paper for this field.
In order to really make progress in cross-section research on mobility and health, at least two bottlenecks need to be removed. First, domain silos and well-established research communities mainly define funding regimes. Currently, it is challenging to acquire funding for integrated approaches. Money is either spent for pure medical research (without considering mobility or technical fields, such as GIS) or for mobility research (without any chance to include clinical research). Consequently, interdisciplinary research activities are largely driven by personal engagement that exceeds every budget plan. Second, data on movement, physical activity and mobility are still not available or accessible in a way that would serve cutting-edge research. Hopefully, this situations is getting better due to rising awareness for open data and open research.
The last post provided a brief summary of the current status of cycling data, with regard to data sources, data availability, accessibility and suitability. I expanded these findings in a lecture, which is available in German language on Youtube and finally discussed the topic in a webinar at last week’s virtual AGIT conference with a number of experts from various domains.
The quintessence of these various takes on cycling data is the following: we see a huge demand for high quality cycling data, but only very few have access to suitable data sources. In an ongoing research project – Bicycle Observatory, we are working on conceptual, technical and organizational aspects of collecting and merging relevant data, in order to gain an integrated perspective on cycling mobility.
Now, we want to investigate the actual status of cycling data usage accross Europe and beyond. The goal is to get a sound evidence base for any further developments. We want to know if and how cycling data are currently used and how available data sources serve the respective purposes. Your help in this investigation is highly appreciated. Please take a few minutes and participate in the survey.
If you like the idea, please forward the invitation to the survey to your peers. The survey is available in multiple languages. The more participants we get, the more useful the data are going to be. And of course, we are going to make the survey data available as open data, as we did it with our last survey results.
Data are a recurring topic on this blog and the necessity to consider data with regard to availability, accessibility, quality and suitability is still increasing. This is simply because of the massive amount of mobility-related data that is constantly generated by mobile devices and stationary sensors. Michael Batty pointed to the fact that it was not the automatization of data capturing, but the miniaturization of sensors that has lead to the ever-growing data stream. Batty stated that the amount of data was scaling up to a level that is not manageable by conventional means. Interestingly, this observation was made seven years ago in 2013. Technology has been advancing significantly since then and there is no indication for a slow down of these dynamics. However, since then, the number of scientifc papers, which suggest that ‘big data’ would facilitate a whole new era of research, planning and management, has been growing substantially. In the broader field mobility, I’m thinking of seminal papers by Kitchen (2014) when in comes to smart urbanism, Miller & Shaw (2015) in the context of GIS-T, or Anda et al. (2017) with regard to transport modelling.
In fact, the ubiquity of sensors, which are connected to the internet, has led to a plethora of new applications and business opportunities, from automated driving to MaaS platforms and many more. Moreover, the paradigm of theory-based research is challenged by data-driven approaches, which are heavily relying on machine-learning and AI respectively.
Against this backdrop, what is the situation like, when it comes to cycling data? If we were following the overall trend towards massive data streams and ‘big data lakes’, we would expect emerging (business) opportunities and new insights into the complex system of cycling mobility.
An excellent, up-to-date review of data sources and applications for pedestrian and bicycle monitoring comes from Lee & Sener (2020):
Classification of pedestrian and bicycle data sources (Lee & Sener 2020). Figure published as Open Access (CC BY-NC-ND 4.0).
Rightly, they point to the fact that cyclists (as well as pedestrians) have specific characteristics and thus, the sensed data can be fundamentally different from motorized transport data. Trips are more sensitive to the environment (infrastructure, weather, topography, …), more variant and commonly shorter. Caused by these particularities and considering current data capturing technologies, Lee & Sener identified the following challenges with regard to data from cyclists:
Mode detection
Data validity in terms of representativeness
Sampling bias
Privacy
Lack of detailed contextual data
Cost of obtaining and utilizing data
According to this list, there is still much research to do. Although numerous voices have been proclaiming that data would help to better understand and manage the entire transport system, things are not that easy, at least with regard to cyclists and pedestrian.
A similar overview can be found in a report by Steenberghen et al. from 2017. There, the authors also investigated the availability of walking and cycling data in all member states of the European Union plus Norway and Switzerland. For this purpose, the authors interviewed representatives of the responsible governmental bodies and found that 18 out of 30 had difficulties with collecting data for cycling and walking. Those countries with structured data acquisition strategies, reported major problems with under-reporting and data completeness. 60% of all investigated countries were not able to calculate the annual average distance cycled per person – at a national level; not to speak about such key performance indicators at a city scale level.
However, cities and regions require detailed data for providing adequate infrastructure and efficiently promoting cycling. Interest in cycling data increasingly comes from the health and environmental sector as well, where the need for quantification of physical activity and emission reduction respectively is a major driver.
Parallel to the technological advances, Batty and others extensively described, a new cultural phenomenon emerged: the quantified-self (Swan (2013)). The readiness to track personal mobility together with several physiological parameters further boosted the production of (cycling) data. Romanillos et al. (2016) see huge potential in these data sets, especially when they are linked to other data sources, such as stationary counters. Among the many fitness and tracking applications, Strava seems to be the data source, which is used most often for cycling-related research. Indeed, Google Scholar returns more than 4,700 references for the search term strava data cycle* today.
Of course, the suitability and quality of data such as Strava needs to be critically reflected. Too often, data from such sources are used in a somehow naive manner. Leao et al. (2017) point to the conceptual difficulty of transforming raw data, which was generated by individuals, into robust databases of collective activity. Griffin et al. (2020) investigated biases in big data for transportation and propose mitigation strategies. The latter is particularly difficult, when it comes to data from cyclists and pedestrians. According to the authors, the datasets – primarily generated by fitness and tracking apps – are heavily biased towards specific user groups. Thus, they suggest to combine various data sources and be careful with conclusions. With regard to the inference of traditional and emerging data sources, Conrow et al. (2018) state, “As a step toward developing a method for conflating conventional and crowdsourced bicycling data, we seek to explore the as yet understudied area of understanding how crowdsourced and conventional data correspond in representing activity.” This is a more than valid point. To date, there is no standardized framework for how to integrate different data sets.
What we know so far is that the correlation between permanent, stationary counters and crowdsourced tracking data varies, depending on time, location, temporal sampling and spatial tolerance (see for example, Boss et al. (2018) for the time dependent correlation). Moreover, the prevalence of app usage varies among regions and even neighbourhoods, according to Heesch & Langdon (2016). Consequently comparisons over time and across regions need to be done with great care.
We learn from the current state of research that regardless of the common enthusiasm about vast amounts of data, sound cycling data, which represents the total of cycling mobility, is not available yet. Perhaps this is less a question of data availability, but of data integration. For this, not only technical, but above all conceptual research is desperately needed.
In an ongoing research project, we are continuously harvesting cycling-related data from many data sources. Together with an agent-based bicycle flow model, we are aiming to generate an integrated dataset, which adequately reflects cycling mobility at the local scale level. If you were interested in this research, visit our Bicycle Observatory website or drop me a line.
Three years ago, I finished by PhD with a thesis on GIS and cycling safety. A few months later, I submitted a manuscript to gis.Science, in which I had summarized the main arguments and results of my thesis. This is the story of a very, very long publication process:
Manuscript submission: December 2017
Acceptance notification: April 2018
Re-submission of revised manuscript: May 2018
Final acceptance: June 2018
Publication: December 2019
Anyway, whoever is interested in how GIS can contribute to cycling safety research, finds a brief summary of my PhD thesis in this recently published paper.
A shift from motorized individual transport to “green modes” is desirable for multiple reasons, but hard to achieve. We have been working on this topic in the context of the GISMO research project, which revolved around healthy commuting, and collected some valueable insights. However, the effects of mode shift go far beyond individual health benefits.
For this blog post, I did some reading and tried to summarize factors, which are relevant for stimulating a mode shift from car to sustainable modes – interestingly, many of them are linked to geography. The most basic geographic variable in mobility is distance. I am going to highlight the huge potential of short commuting trips being substituted by active modes by presenting some basic analysis results based on mobility survey data.
Car stickiness
In a lab experiment, Innocenti et al. (2013, link) proved that mode choices are rarely made on a rational basis. Instead, they are biased by various individual factors (routine, memory, perception etc.). These biases lead to a preference of the car, although travel time and monetary costs might be higher compared to alternatives. Subjects in the experiment showed a tendency to repeat their initial mode choice, which is the car for a majority, regardless of the costs. In other words, people do not like to change modes and information provision has little influence.
Garcia-Sierra et al. (2015, link) put the car stickiness into a broader context and provide an extensive list of behavioural biases and anomalies, which influence long-term and short-term mobility choices.
Millennials
It is frequently repeated that the car is loosing attractiveness for younger generations and is not an object representing status and prestige anymore. In fact, numbers of car ownership or trips taken are have been going down among millennials for quite a while. However, as Garikapati et al. (2016, link) impressively show, this mobility behaviour fades and millennials are adopting the mobility lifestyle of preceeding generations, but at a later stage in their lifes.
Digitalisation
In contrast to the rather conservative conclusion of Garikapati et al., Canzler & Knie (2016, link) are convinced that the digitalisation of the mobility sector is going to disrupt the way we are moving. In their opinion, flexible, intermodal mobility services, facilitated by the ubiquity of the smartphone, will substitute conventional, private vehicles (cars) entirely. With this, the relation between producers (car manufactorers) and consumers (private car owners) is ultimatively transformed into an interplay of demand (being mobile) and supply (service). Thus, the authors do not argue for a mode shift within the existing system, but for an entire transformation of the mobility system, which will automatically reduce the number of motorized vehicles on the road.
Individual or society?
Many strategies for stimulating a mode switch target the individual and his or her particular behaviour pattern. Clark et al. (2016, link) regard life events as major factors for mobility behaviour change. The authors found residential relocation, change of employer and gaining a driving license as most effective life events in terms of mode switch. Spotswood et al. (2015, link) argue for targeting society instead of individuals. In their (very inspiring!) paper, the authors propse the application of Social Practice Theory (SPT) for changing mobility behaviour towards active modes. The SPT is built upon three pillars: materials (everything external, physical), meaning and competences. With regard to mobility, the first two pillars can be translated to infrastructure and service provision and mobility culture respectively.
Interventions
Types of interventions that are intended to stimulate mode switches from car to public transport or active modes are very diverse. They can be physical (built environment), communicative, legal or economic. Scheepers et al. (2014, link) investigated the effects of various interventions and found an overall positive correlation between interventions and mode shift in an extensive meta-study. In most cases, multiple interventions were simultanously implemented, what increases the overall effect. However, the authors note that the robustness of available evidence is rather weak, since most studies lack of control groups and do not control for statistical significance.
Spatial context
Besides the aforementioned life events, Clark et al. (2016, link) identified distance and public transport service level as additional drivers for mode change. The probability for switching to non-car commuting becomes 9.2 times higher when the commuting distance drops below 4.8 km (3 miles). However, car availability counteracts the effect of distance, according to Scheiner’s (2010, link) analysis of longterm longitudial data from Germany.
Heinen et al. (2015, link) report on effects of new public transport, cycling and walking infrastructure on the modal split among a group of commuters: by building a busway with parallel cycling and walking ways the share of trip chains with active parts significantly increased, while the share of car-only commuting trips decreased. Commuters living within 4 km from PT stops were twice as likely switching modes than the rest. Sallis et al. (2016, link) come to very similar conclusions in their seminal study on the relation between the built environment and physical activity. They identified four environmental key factors, which are linearly related to physical activity: residential density, intersection density, public transport density and the number of parks. Such environments stimulate active mobility and physical outdoors activities and are thus fundamental for public health.
Regardless of the investigated variables, it is of great importance to differentiate between statistical correlation and causal relation. For instance, Schoner et al. (2015, link) point to the fact that a direct, causal relation between the environment and mode choices are hard to be proven statistically. In general, environmental factors are either catalysts (making people switch their mode) or magnets (attracting people who are already prone to respective modes). Thus, the authors designed a model that is able to account for these different effects. Applying this model to a dataset from Minneapolis, Schoner et al. found that bicycle lanes and workplace accessibility contribute significantly to the level of commuting by bicycle.
Two sides of the coin: internal and external factors that are relevant for promoting sustainable, active commuting.
Probably, there are some more factors, which need to be considered in the context of stimulating a shift from car-depended to sustainable modes. However, what became obvious so far clearly indicates that motivators and deterrents for active commuting can be roughly grouped into internal (personal) and external factors. Moreover, these two sides of the coin are interrelated in multiple ways and thus, need to be addressed in integrated approaches.
Many factors can be changed or influenced, either at an individual or at a societal level. However, some factors, such as residential and employment location, require long-term decisions and are related to numerous dependent variables (availability, price, social involvment and obligations etc). Consequently, the distance between place of residence and workplace is given for many employees; at least in the short run.
Taking commuting distance as fixed, the mode choice for commuting trips becomes central. Two fundamental questions emerge in this context: How big is the potential for mode shifts from car to any alternative? What does it take to trigger this shift? In order to answer the first question, I took a closer look to the most recent, available data for Austria, the mobility survey “Österreich unterwegs”).
The following analysis is based on the full dataset, which we are using in the Bicycle Observatory research project. The analysis steps are straightforward and do not consider any correction factors etc., which are used for the official report. Thus, the outcome could diverge a little bit from other results, which are based on the same dataset. However, the magnitude should be correct.
In a first step, I was interested in distances for commuting trips. I selected all trips with the trip purpose work, used the distance classes of the original dataset, and differentiated between federal states. The chart below shows the cumulative distribution of distance classes:
It becomes evident that at least half of all commuting trips are below 10 kilometres. This distance can be regarded as cyclable, especially when e-bikes are taken into account. But despite this potential, it is not reflected in the modal split statistics:
For large parts of Austria, especially for rural regions, the car is the preferred commuting mode. Consequently, short distance commutes remain a theoretical potential for sustainable mobility. In order to activate it, integrated efforts with a mix of pull and push measures are required. The (social) context of companies offers reasonable opportunities for addressing commuters. As we could demonstrate in the GISMO project, it is possible to change mobility behaviour of employees with specific interventions. However, the evidence from literature is clear that the less car-centric the physical and cultural environment is, the more attractive public transport and active mobility become. The analysis results of the national mobility survey leave no doubt: the length of trip distances cannot serve as valid argument for the car as prime commuting mode.
Dublin was a great place to be last week. Not only mild temperatures contributed to the attractiveness of Ireland’s capital, but also this year’s VeloCity conference. A varied program with lots of opportunities for networking brought together international cycling experts and enthusisasts from academia, industry, NGOs and the public sector. After sorting out my notes, pictures and experiences, I am trying to summarize and reflect this super packed cycling week.
The conference
Organized by the European Cycling Federation (ECF), the VeloCity conference series is the annual meeting point for the international cycling community. The mix of academic and practical contributions as well as the expo and a rich side program with workshops, excursions and social events make the VeloCity an event, which has to be highlighted in the conference calendar.
This year, VeloCity took place in Dublin for the second time after 2005. The Convention Centre Dublin at North Wall Quay hosted over 1,000 delegates from around the globe.
VeloCity 2019 was hosted in Convention Centre Dublin (Samuel Beckett bridge and River Liffey in the foreground). Foto: M. Loidl
Each day was framed by plenary sessions, which were dedicated to specific topics. Papers, projects and initiatives were presented and discussed in six parallel tracks between the plenaries. A poster exhibition, technical sessions and a large expo complemented the program.
In total, VeloCity 2019 offered 7 plenary and 78 parallel sessions. The selection of the plenary topics was excellent – relevant fields, from planning to technology, infrastructure, health and tourism were covered. The quality of the presentations in most sessions I attended was very high. However, it happened more than once that time for Q&A was lacking. I know the dilemma of including as much contributions as possible, giving speakers reasonable time and facilitating in-depth discussions. Besides session chairs with their eyes on the watch, I would regard slightly longer coffee breaks as most effective. Further limiting the length of presentations would end up in rather superficial talks.
VeloCity is a comparable expensive conference. In my opinion, the speaker fee is too high, given the fact that it is the speakers, who fill any conference with quality and life. Although it was a great opportunity for sharing and networking and an attractive package (impressive conference dinner in Guinness Storehouse, free bike rental etc.) was offered in Dublin, it (literally) cost me quite a lot to scrape together travel funds from my research projects.
Usually, the VeloCity conference goes overseas every second year. Since Mexico City withdraw its bid, the magnificent capital of Slovenia, Ljubljana is going to host next year’s conference. I’m looking forward very much to this!
Program highlights
The plenary sessions were very well curated: highly relevant topics were presented and discussed by inspiring speakers and panellists.
The very first plenary was probably the one with the largest impact, as it addressed the future of where the majority of people are living. In his keynote “The City of the Future”Philippe Christ, innovation adviser at ITF, deconstructed technology-driven scenarios of future cities (“Smart Cities”) and presented a perfectly balanced, human vision of how to shape cities. Philippe pointed to three aspects, which, in my opinion, should become cornerstones of any discussion on urban development:
Future visions
Philippe referred to widely-used pictures of future cities, which are solely shaped by an efficiency and control paradigm (try your own Google image search). In contrast to this, he reminded the audience that in the past, cities have always benefited from creativity that emerged at the fringe of planned, formal spaces. Thus, the question is, if we would really want to go for sterile, manageable cities, or for cities that offer opportunities for unfolding the potential of all its citizens. The latter requires human interaction, unplanned activity, spontaneity, and unsupervised playing.
Future humans
Although many proponents of smart city initiatives are not that much used to it, the question of what defines and characterizes humans is fundamental for any future development (also see Calzada & Cobo 2015). According to Philippe Christ, humans are active, frictious, social, and free.
Future technology
The following panel discussion often related to this part of Philippe’s keynote. Neither Philippe nor any of the panellists were radically against technology as such. However, they strongly argued for – how Klaus Bondam put it – a future that should be shaped by humans, not by technology. In the past 50 years, the car gained technological monopoly status, which became manifest in how cities are built and organized. The current smart city paradigm pushes the next monopoly technology in cities: code. Of course, the highly interconnected, automated city can be beneficial in several regards, but it also bears the potential to isolate and segregate individuals and communities respectively.
The only question that remained open after this powerful statement for a people-oriented future city was, “What if the auditorium was not full with cycling enthusiasts, but with representatives from car industry and the ITC sector?” I wish that such a message does not only reach the converted (such as VeloCity delegates), but also those who are influencing (political) decisions with their unreflected, narrow tech-optimism.
After the opening keynote, I attended a session on autonomous vehicles and cycling – a perfect follow-up. Ceri Woolsgrove of ECF claimed that autonomous vehicles will partly improve the situation for cyclists, as there won’t be any drunk driving, for example. However, industry is not ready to ensure full safety for cyclists yet and thus, it might take a while until AVs will be common on our cities’ roads. No wonder that Renault’s former CEO, Carlos Ghosn complained over cyclists. In a Forbes article by Carlton Reid, Ghosn is quoted as follows:
One of the biggest problems is people with bicycles. The car is confused by [cyclists] because from time-to-time they behave like pedestrians and from time-to-time they behave like cars. Carlos Ghosn in Forbes
Ghosn might have liked John Parkin’s presentation on cycling-specific outcomes of the Venturer research project, where the interaction between AVs and other road users was investigated:
A session on cycling data, closely related to our current Bicycle Observatory project, took place on Tuesday afternoon. In the German MOVEBIS project, a vast amount of cycling trajectories are collected and further used for analysis purposes. Herbert Tiemens and Ilari Heiska gave an update of current features and applications of the ABM simulation environment Brutus. Finally, Michal Jakob of Cyclers, a young Czech company, addressed the challenge of using tracking data for estimating total amounts of cyclists.
André Muno (Climate Alliance), Ilari Heiska (City of Helsinki) & Herbert Tiemens (Province of Utrecht), Michal Jakob (Cyclers)
The second day of VeloCity started with a plenary on the importance of being happy and healthy. Three short keynote presentations covered a wide variety of topics. Orna Donoghue (Trinity College Dublin) presented outcomes of a huge, longitudinal study on ageing. The Irish Longitudinal Study on Ageing (TILDA), with over 8,500 participants over the age of 50 years investigates several aspects of ageing, including mobility. Accessible facilities, adequate transport options and personal mobility are known to be key factors for happy and healthy ageing. Matthew Philpott introduced the idea of promoting healthy lifestyles in and around sport stadia. And finally, Lucy Saunders gave fascinating insights into the process of implementing the Healthy Streets concept in London.
Lucy Saunders chaired a subsequent session on health in mobility. Victor Macêdo, representative from the Brazilian city of Fortaleza, gave an impressive overview of activities to promote healthy, everyday mobility. Supported by Bloomberg Philanthropies and the World Health Organization, Fortaleza implemented a citywide bike sharing scheme and is building dedicated cycling infrastructure (from 68km in 2013 to currently 260km). With “Bicicletar Corporativo”, the city council promotes cycling among municipal employees. A law that allocates all money from parking management and 1% of digital platform revenues to promoting active mobility insures the sustainability of all these measures..
Victor Macêdo presenting recent cycling promotion measures in Fortaleza, Brasil.
I had the honour to present rationales and results of our GISMO project in this session. Currently, we have nine papers with all the detailed results of the clinical intervention study under review. Please check the project website for updates; we are going to link to the papers as soon as they are out. For now, I can refer to the slides of my presentation:
In the last session before the bike parade on Wednesday, the Austria Cycling Competence network celebrated its 5th anniversary with a special session. Selected members, presented recent projects or gave an overview of their portfolio. I took the chance to argue for a spatial perspective on cycling.
The plenary session on infrastructure was very important – not only with regard to Dublin’s non-existing cycling infrastructure. Burkhard Stork, chairman of the German cycling association ADFC, claimed that infrastructure is the backbone of any cycling infrastructure. It reminded me of a lecture I gave at the Technical University of Vienna in 2017. Back then, I showed how important dedicated infrastructure is for cycling safety. Only a day later, I received an email from a professor who attended the lecture. He urged me to prove scientifically that cycling infrastructure would enhance cycling safety and claimed that vehicular cycling would be safer, cheaper and more efficient. With this story in my mind, I enjoyed Burkhard’s comments on John Forester’s idea of effective/vehicular cycling a lot. Forester’s idea of treating cyclists like any other traffic participants became popular for several decades, especially in North-America and the UK. However, as Burkhard pointed out, Forester developed his idea not based on evidence, but on his own preferences as race biker. It was Roger Geller of Portland, Oregon who questioned the vehicular cycling concept and wrote his ground-breaking article “Four Types of Cyclists”. This fine piece of work wasn’t scientific as well, but Geller made a claim that became fundamental for subsequent cycling policies and a starting point for lots of research in this field:
Riding a bicycle should not require bravery. Yet, all too often, that is the perception among cyclists and non-cyclists alike. Roger Geller
A substantial amount of research that revolves around this statement has been done by one of the most profound cycling researchers, Jennifer Dill. She underlined Burkhard’s critique of vehicular cycling and his unequivocal call for adequate cycling infrastructure with several studies.
Jennifer Dill (Portland State University) provided loads of evidence for the effect of dedicated infrastructure.
The importance of cycling research is also emphasized by the ECF. The European Cycling Federation connects researchers who are working in the wide area of cycling mobility. Throughout the conference, special sessions of the Scientists for Cycling network were organized. One of these academic sessions was dedicated to measuring the impact of cycling. Ray Pritchard of Norwegian University of Science and Technology (NTNU) shared outcomes of two observational studies in which the effect of newly built infrastructure was investigated. In order to properly assess the impact of such measures, it is necessary to differentiate between mode shift (infrastructure attracts new cyclists) and route shift (cyclists change their routes). Ray did this by using GPS trajectories and survey data. In both use cases, in Trondheim and Oslo respectively, route shifts became obvious, whereas no significant mode choice could be proven. The Oslo study was recently published in the Journal of Transport Geography.
Among other objectives, the research project Bicycle Observatory seeks to lay the foundations for assessing the impact of measures in various dimensions. Thus, my contribution to the session perfectly built on Ray’s presentation:
In France, Stéphanie Mangin is leading a project called observation du tourisme à vélo. The aim is to estimate the economic value that is created by cycling tourism. For this, Stéphanie and her team collect data from all permanent counting stations along national routes and combine these data with on-site survey data on average expenses and durations of stay. Monetizing the impact of cycling (tourism) builds a perfect evidence base for pushing public authorities to further build attractive, safe infrastructure.
Parade
Cycling parades are one of the major highlights of cycling conferences. Usually, this event is used to show delegates the host city and to raise awareness for cycling among citizens and politicians. For VeloCity 2019, the organizers chose a different strategy and guided us to St. Anne’s park outside the city. Probably, this was the best-protected (securities at every intersection and private driveway), but also shortest parade ever. The length of the parade might have something to do with infrastructure … The largest part of the parade led along the seafront to St. Anne’s park – one of the very view segregated cycle ways in Dublin. Honi soit qui mal y pense.
On Twitter, the parade was heavily discussed. Here are some examples:
Dublin is a beautiful city with a rich history and charming citizens. It has a lot to offer … but definitely not to cyclists. Although the Lord Mayor and all representatives, who appeared at the parade or gave an address at the conference, tried to give the impression of a cycling friendly city, it did not work out. Dublin is by no ways a cycling city! I captured this everyday street scene on my way to the conference:
During VeloCity, several articles on cycling were published on the newspaper. The Guardian was pretty clear with the headline Dublin disappoints and even the Irish Times titled We’ve lost our way with private cars. On stage, Klaus Bondam was the first who articulated what many experienced in the city:
As Klaus pronounced his critique in the very first plenary session, delegates had enough time to collect evidence for what he had said. These contributions were favourably received by local cycling activists and picked up by local newspapers. Mark Wagenbuur did a great job by collecting some bits and pieces – have a look at his blog post. In order to get an impression of what was going on, I curated some more or less randomly tweets (do not miss the discussions and read the whole threads!). Let’s start with some of my own:
Okay, as it becomes clear, there is a lot to do in Dublin. At least, VeloCity might have increased the pressure on the City Council to really build and improve adequate infrastructure for cyclists. And of course, cycling in everyday clothes, without helmets and not necessarily on sport bikes must make it into the mainstream attitude towards cycling. Since the parade was a disappointment for many delegates, the local cycling advocacy, I BIKE DUBLIN, organized a critical mass from the Convention Centre to the conference dinner at Guinness Storehouse. I decided to walk and witnessed a very funny, but self-revealing situation:
The conference wasn’t cheap and Dublin is no cycling city, but VeloCity 2019 was definitely worth to attend. It was inspiring and fun. I learned a lot and was able to connect to others who are working on similar topics or who gave me valuable input for our further research. I enjoyed the diversity among the delegates – be it in terms of geography or domain background. The excitement for cycling turned out once again to be a very strong common denominator.
The evidence is very clear: physical inactivity due to sedentary lifestyles accounts for more premature deaths than smoking worldwide (Wen & Wu 2012). A reduction of physical inactivity by 25% could prevent 1.3 million deaths globally per year (Lee et al. 2012). Physical inactivity accounts for an estimated economic loss of 1-3% of GDP (WHO 2018). For adults, the World Health Organization recommends a minimum of 150 minutes of physical activity with moderate intensity per week (WHO 2010). This sounds not too much. However, worldwide, one out of three does not meet this recommendation and the share is worse for high income western countries. Here, 42.3% of the total population is insufficiently active (Guthold et al. 2018)!
In the recently finished research project GISMO (see a previous post or consult the project website in German language for further details), we hypothesized that the daily commute to and from work is a very efficient opportunity for increasing the amount of physical activity. In order to unlock this potential, a behaviour change from passive to active mobility is required. This could be a very hard process, because mode choices are not made on a rational basis (Innocenti et al. 2013).
In order to overcome this barrier, it is necessary to create positve experiences and to provide incentives for more walking and cycling. Companies are key players in this transition process. Through institutional health promotion programs and travel plans, they have the necessary means to influence employees’ commuting mobility. Moreover, companies are social settings, where a pro-activity spin can be created and cultivated (Millonig et al. 2016). Conversely, companies need a sound evidence base on which they could decide on adequate measures. Any decision maker in this context wants to know how much he or she needs to invest and which return can be expected.
A corner stone of GISMO was a clinical intervention study, in which we investigated the health effects of promotion activities that encouraged employees from car to active commuting. In order to come up with a dose-effect relation, accurate data on the subjects’ commute were required. These data also indicate to which degree subjects were willing to follow the instructions and change their mobility behaviour.
We used travel diaries and fitness watches with a GPS and a heart rate sensor for tracking commuting mobility. A methodological paper on travel mode detection was published last year (Stutz u Westermeier 2018). Another paper on how to merge self-reported with technically sensed mobility data is going to be submitted very soon together with a series of papers on the health effects – I will add the reference as soon as the papers are published.
For our GISMO study, 76 subjects, who primarily travelled to work by car, were initially recruited (73 finally participated). Subjects were randomized into an intervention and a control group in a 2:1 strata (Niederseer et al. 2018). Depending on personal preferences, the intervention group was divided into two subgroups. One, in which subjects were asked to switch to cycling and another one, in which subjects were encouraged to walk and use public transport. The baseline characteristics in terms of health and mobility behaviour were comparable across all groups.
First results of an analysis of the collected data were striking in terms of mobility behaviour change:
Besides the loyality of subjects to the recommended transport mode, the low modal share of the car in the intervention groups, compared to the control group, becomes evident. Given the fact that the modal split of all participants was similar prior to the intervention, one can conclude that a vast majority of car trips were successfully substituted in the control group.
The financial investment that was necessary to achieve this astonishing mode switch was comparable low (max. 700 € per subject for a one year intervention period). From a qualitative survey among participants who finished the study, we know that two motivators were particular outstanding. First, the experience was decisive for behaviour change (see this feedback in German language by a subject). Second, the medical investigations at the beginning and end of the intervention period were very positively perceived.
According to the latest available mobility survey, 44% of all trips of the working population are commuting trips from and to work. Considering this high amount of work-related traffic, the high share of the car in the commuting modal split and the comparatively short distances bear huge potentials for healthy, active commuting. Changing established mobility behaviour patterns is a challenge, but as we could show in the GISMO study, it is possible to unlock the potential of active mobility for health promotion.
During the past 2 years, we have been intensely working on an agent-based simulation model, which allows for estimating cycling traffic at the highest possible spatial and temporal resolution. Results were presented among others at the last ICSC in Barcelona. The slides of my presentation and further details on the simulation model can be found on this blog.
The context of this research was the collaborative project FamoS, lead by TU Graz and funded by the Austrian Ministry of Transport, Innovation and Technology.
The simulation model was implemented in a OpenSource software environment called GAMA. A major advantage of this software is its GIS capabilities. GAMA is not specifically designed for agent-based transport modeling and a lot of features had to be programmed from scretch.
We have now shared the entire model with an extensive documentation and a sample experiment on OpenABM platform. You are invited to test the model, improve it or contribute additional features. The code is available on GitHub.
The transport sector accounts for a substantial portion of greenhouse gas emissions and fine particulates. Besides these environmental implications, the current transport system heavily relies on motorized individual transport, which leads to negative economic, social and health effects. Consequently, legislators on all levels are aiming for transforming today’s mobility. Numerous strategies and roadmaps towards decarbonization, sufficiency and efficiency and sustainability reflect this political goal.
At the coming GI Forum conference in Salzburg, I’m going to organize a special session, which is dedicated to sustainable mobility. The session “Spatial Perspectives on Sustainable Mobility” is the fifth in a series on GIS and mobility research. The idea of this series is to highlight the potential of spatial approachs in mobility research. Numerous factors influence mobility behavior and transport systems. The spatial perspective can help to link domain-specific knowledge on the basis of a common spatial reference and derive novel, integrative approaches. Over the past years, this special session series has become a well attended element of the annual GI Forum conference. Besides original research that is presented in this session, it is a perfect opportunity for networking with fellow GIScientists and experts from other domains.
For the next edition of this series, we invite researchers from any domain backround, who are working at the intersection of GIScience and mobility research, to contribute to this special session. Today, the call for contributions got published on the website of the conference. Original research can be submitted until Februrary 1st 2019.
Consider this call as an opportunity for your publication strategy and for project dissemination. Accepted full papers and extended abstracts are going to be published in the Open Acccess GI Forum journal.
I’m looking forward very much to welcoming you in Salzburg next july!
Studying a map with geo-located bicycle crashes might leave you with the impression that cycling must be terribly dangerous. A little bit of rudimentary statistics definitely helps at this stage. Whether something is regarded as dangerous or not ultimately depends on the underlying statistical population. This is a common concept for example in medicine. In a drug’s package insert, the risk for suffering from adverse effects is always related to a population. This helps the consumer (or the medical doctor) to draw informed conclusions. The risk, or incident rate, expresses the probability for a drug to become dangerous. Something similar is still missing for cycling. At least at the local scale level.
Geo-located, reported bicycle crashes between 2002 and 2011. Data source: City of Salzburg. Details are published in Loidl et al. (2016).
Alberto Castro and colleagues published an extensive study on exposure-adjusted road fatality rates of pedestrians and cyclists just recently. Compiling data from most OECD countries, this is the first systematic study of this kind and a huge step forward. However, the calculated fatality rates are based on very highly aggregated statistics. In the authors’ own words, ‘exposure data was found to be generally poorer than for fatality data, as travel distances of active modes are not systematically collected in all countries’ (Castro et al. 2018, p. 8). Now, if the availability of exposure data is poor on at national level, how to interpret local crash data or so called crash blackspots – a challenge planners and authorities are facing in cities on a daily basis?
In fact, the problem is that we do not know where, when, how many and which types of cyclists are on the road in most cases. Consequently, we are lacking required exposure data. Moreover, we can only roughly estimate the demand for infrastructure and the respective capacity, and finally, the effect of interventions remains opaque. Regarding the interpretation of crash data, the map below perfectly illustrates the problem. The message of the mapped bicycle crashes seems to be pretty obvious: on the road along the river, crashes are recorded every few meters (crash data were collected over a 10-year period). It looks like this road was quite dangerous for cyclists. In contrast, on the parallel road no crashes are recorded at all. Is this the safe alternative? Well, a quick look at the attached street views answers the question instantaneously. The road along the river is a highly frequented cycle way, whereas the parallel road is exclusively dedicated to motorized traffic (plus a narrow sidewalk). Because hardly any cyclist is riding there, no crashes occur. The probability of being involved in a crash is, at least in part, a function of traffic volume.
In order to overcome the limitation of missing exposure data, we have been working on an agent-based bicycle flow model with a very high spatial and temporal resolution in the collaborative research project FamoS. This week, I’m going to present results from this research at the International Cycling Safety Congress in Barcelona.
The building blocks of our agent-based simulation model are single trips. We expect flow patterns to emerge from individual mobility behavior, which is determined by multiple parameters. With this approach, it is possible to anticipate the heterogeneity of cyclists. Different to motorized mobility, where the machine levels out different capabilities (elderly people are able to drive at the same speed as youngsters), the variety of behavioral variables and riding styles is huge among cyclists. A 4-year old girl on a bicycle has little in common with a bike courier, just to give an example.
In our model, we can control for different socio-demographic variables, such as age, education or employment status. Additionally, we differentiate between different trip purposes, trip length and accessibility of destinations. After initialization, we simulate agent’s activities, schedules, destinations, mode choice and route choice. Of course, such a model requires many data. In the case of a model we developed for the Salzburg central region, we used:
Topological correct road graph with a rich set of attributes
Census data with a spatial resolution of 100 and 250 meters from Statistik Austria
Central facilities and POIs from OGD portals
Raw data from mobility surveys
Time use statistics from representative surveys
The bicycle flow model was developed from scratch by Dana. She did an excellent job by translating the conceptual model into code. The simulation model runs on GAMA platform and will be freely available at OpenABM soon. Through a very efficient code structure, we are able to initialize and run the simulation model for a 24-hours day, with 150,000 agents, a temporal increment of one second and a spatial resolution of one meter within only five hours. This very fast runtime makes the model perfectly suitable for sensitivity analysis and simulation of various interventions, such as additional connections or changing behavior of travelers.
Comparison of simulated bicycle trips (left) and recorded data from the Bike Citizens app (right).
The model was calibrated with data from six stationary counters. For model validation, we used tracking data from Bike Citizens. In total, we achieved very accurate results. The temporal distribution of bike rides perfectly matches the double peak signature in reference data. The simulated spatial pattern of bicycle flows has a little bias towards the left side of the Salzach river. Reasons for this are expected to be associated with known biases in the input data. In the validation, we also compared the characteristics of simulated and recorded trips. Whereas the mean travel time is much higher for Bike Citizens data (mainly due to a few outliers, generated by long distance leisure cyclists), the average distance and speed distribution resembles perfectly.
To the best of my knowledge, this is the first bicycle flow model at the local scale level with a regional coverage. We use the results for multiple purposes. For example, we could simulate the expected effect of planned bicycle corridors in the city of Salzburg. In the context of safety, the model is well suited to generate exposure data for risk analysis. Referring to the example above, the simulated bicycle flows can be nicely used to calculate incidence rates and subsequently assess the safety of areas and road segments (we published a paper in Safety on this topic). In the particular case presented above, it becomes evident that the road along the river is definitely not dangerous. The recorded crashes can be expected from the number of cyclists. However, it is beyond any discussion that every bicycle crash is one too much. Providing adequate infrastructure is crucially important for attracting cyclists and ensure safe rides. Here again, the simulation model helps to estimate the demand and derive required capacities for dedicated infrastructure.
With the agent-based simulation model, we have made a step forward in providing sound evidence to decision makers and bicycle advocates. Nevertheless, it is still a model and thus, it does not mirror reality, but a generalized representation. In order to further refining the model we are currently improving the input data basis In the research project Bicycle Observatory, we combine quantitative and qualitative data from different sources for getting an integrated perspective on bicycle mobility. This will help us to include even more parameters in the model and hence, provide a more accurate representation in the spatial, temporal and behavioral dimension.
E-commerce is not only changing shopping habits and the stationary retail sector, but has a direct impact on the transport system. A major component in the delivery chain are the countless parcel couriers, who have become an omnipresent part of urban street scenes. Logistics companies, urban planners and researchers around the world are aiming to establish more efficient and sustainable logistics systems in cities.
Past week, I was invited to join a workshop on location optimization for smart lockers. A private company in a mid-sized, but chronically congested town is planning to provide a dense network of smart lockers for tourists, private customers, retailers and CEP services (courier, express, parcel). One of the participants in this workshop provided a striking argument for why to invest in such a smart locker system: according to a study – he claimed – freight traffic accounts for 20-30% of all traffic in a city, but is responsible for 80% of the congestion during rush hours.
Of course, such an impressive number caught my attention. If there was a single variable with such huge systemic impact, the entire energy of urban planners should go into this. But I had my doubts and they didn’t become smaller after a little bit of research (however, the guy’s claim was right, I found the study where these figures are stated).
Five years ago, the topic was covered on the CityLab portal. In this article, the author numbers the amount of time loss due to delivery vehicles parking at the curbside with 947,000 hours vehicle delay in the US. For this figure he refers to a white paper by the US Federal Highway Administration (FHWA), which cites a study from 2004 as source for this number. The provided link to this study is broken, but a little bit of google querying leads to the cited study report, published by Oak Ridge National Laboratory. The authors of this study took observation data from the late 1970s, overlayed them with land use data and derived an estimation model. The result looks like this:
This example shows, how two observation studies from the 1970s fueled an estimation model in the 1990s, which generated results that became more heavy (in terms of reliability) the more often they got re-cited (this reminds me of the famous 80%-of-all-data-are-spatial tale, but that’s a different story). The 80% in the freight transport and congestion story is more the result of sloppy work. Here is what I found after a quick research:
In December 2017 the English language version of the German newspaper Handelsblatt published an article on the relation between e-commerce and congestion. In this article the 80%, mentioned above, pop up. However, the author might have misunderstood the study by PricewaterhouseCoopers he refers to:
Apart from this, it is interesting to take a closer look at the PwC study. The authors claim that freight transport accounts for 20-30% of urban traffic, but is responsible for 80% of the congestion during rush hours. This is exactly what was mentioned in the workshop.
Fortunately, the authors added a reference to this number: Institut für Klimaschutz, Energie und Mobilität e. V. (2017): LowCarbon Logistics, http://www.kiamnet.de/lowcarbonlogistics/ (September 2017). Again, the link is not valid anymore, but was substituted by https://www.ikem.de/portfolio/low-carbon-logistics.
I don’t know why the PwC study (80%) refers to the website of a partner in an EU project at this point, but I checked the website and learned that nowhere on the website anything is said about the responsibility of freight transport for congestion. Maybe the website the authors originally consulted contained more information? However, I tried to find some more information and visited the website of the project Low Carbon Logistics. In fact, this is an interesting project and moreover, it has something to do with the topic at hand. However, there is not a single hint for the 80%. The only trace I was able to find was the modal share of freight transport (and even here, the source was corrupt):
Obviously, the 80% are either an error in the PwC study or have their source somewhere else. I don’t care much about the PwC study, but about the fact how – in both cases – quite bold statements are standing on weak feet. It seems like multiple re-citations can transform weak numbers into solid facts. If they were then used in decision making processes, the damage could be huge.
I’m not an expert in city logistics and there are reports on freight transport and congestion, which are dealing very nicely with facts and data source. In fact, I’m glad to refer to another CityLab article in this context. However, I want to raise awareness among researchers (and journalists) for the responsibility we have, if we provide information and evidence to decision makers!
… and if you have an idea where the 80% came from, please share it!
Spatial information matters for almost all aspects of mobility. This holds true, of course, for people and goods. Distance to the next distribution center, travel time delay because of boarder controls, legal regulations for highway sections, topography impacting the range of an electric vehicle etc. All these examples are either explicitly (such as distance) or implicitly (geo-referenced speed limits, for example) spatial.
Of course, there are many disciplines that deal with certain facets of mobility. The variety ranges from the humanities to natural and technical sciences, from law to planning and communication. In fact, mobility moves many researchers and the body of literature is huge. As a geographer I’m interested in the spatial factors that influence mobility and the spatial impacts of mobility on the social and physical environment. Thus, it is my pleasure that the GIScience sister conferences AGIT and GI-Forum have a lot to offer for researchers and practitioners in the field of mobility. This is my personal schedule for the coming days:
Traffic Management
In the German language track, session A6 deals with spatial analysis and planning in traffic management, today at 1pm. The topics of the paper presentations range from accessibility and public transport quality (by the way, these are two crucial elements of our recent GISMO project) to charging infrastructure and spatial factors that impact the modal share of cycling. I’ll have the honor to chair this session and looking forward to inspiring presentations and a vivid discussion.
Spatial Perspectives on Healthy Mobility
In the late afternoon, I’m going to host another Spatial Perspectives on … session. We have started in 2015 with this series of spatial perspectives on various mobility topics (the first edition of this session mounted in a very nice review paper on transport modelling). Motivated by the research we have done the past to years at the intersection of health and mobility research, we launched a call for papers on healthy mobility in fall 2017. After a rigorous review, four papers made it into today’s session and I’m really looking forward to discussing social as well as technical aspects of healthy mobility.
Authoritative Road Data
Thursday morning is traditionally dedicated to the Austrian integrated road graph platform (GIP), a standard for all authoritative, road-related data. This year several applications, which are built upon GIP data, are going to presented in session A9. I’m especially interested in the bicycle routing application, which was co-developed by a former UNIGIS student.
Autonomous Driving
Three sessions in a row deal with autonomous driving on Thursday at the German language conference. The last years, these session were massively industry-driven. Checking the program, I expect some additional inputs from policy and research this year.
GISMO Expert Workshop
On Thursday afternoon, we are going to host an expert workshop, where the major outcome of the GISMO project is going to be presented and evaluated by experts. The head of the department for sports medicine at Salzburg’s medical university, Josef Niebauer, is going to provide a session keynote on healthy mobility. We will also welcome representatives from the Austrian Ministry of Transport, Innovation and Technology and many experts from industry and administration, who are engaged in corporate mobility management and health promotion.
There are going to be more sessions, which are relevant for mobility research or which deal with it in one or the other way. Dana, for example, is going to give a presentation on simulation platforms for modeling bicycle flows. There are two sessions on Smart Cities and planning and one on Urban Geoinformatics. Moreover, many exhibitor in the expo area do businesses in the mobility sector. So, there is a lot to do, to learn and to enjoy the next three days! If you are around, I’d be happy to connect in reality. For those who couldn’t make it to Salzburg this year, follow us in the social media realm (#AGIT30, #GIForum2018 or #AGIT2018 or check the social media wall).
Modelling bicycle flows at a reasonable scale is complex and not very well established yet. However, knowing when, where how many cyclists are on the road is crucially important for mobility management, transport planning and cycling promotion.
Before this background, a nationally funded research project called FamoS is dedicated to develop and evaluate two different modelling paradigms. Our partners at TU Graz expanded their intermodal four-step-model and integrated cycling. We at Z_GIS were responsible for testing an agent-based approach.
After 18 months of research and development – primarily driven by Dana, who is writing her PhD on ABM in transport modelling – we are able to present first results these days. At this year’s GEOSummit, the major GIS conference in Switzerland, I presented an introduction of how spatial information is employed in an agent-based modelling environment:
At the upcoming GI_Forum conference, Dana is going to give a presentation on her evaluation of ABM platforms for the purpose of bicycle flow modelling. The simulation model itself is going to be published in the Open ABM repository. A journal paper about the model is on the way.
You see, there is more to come! Stay tuned or get in contact with us right away.
You have come across the claim that cycling is on the rise in cities all over Europe for sure. However, if you are looking for the statistics behind it you will be disappointed. Just try it and google for modal split development and cycling.
In their seminal paper Data driven geography, Miller and Goodchild state that “The context for geographic research has shifted from a data-scarce to a data-rich environment […]”. Thinking of the huge amount of data generated by an unprecedented number of sensors, this observation is absolutely true. Still, the story is a little bit different when it comes to the geography of cycling. Although the data volume is growing there as well – mainly due to the quantified-self-movement and the vast number of fitness and tracking apps – we are still in the situation that we cannot answer fundamental questions such as:
How many cyclists are on the road?
Where and when do they move through space?
How did the modal split develop over the past ten years?
The lack of adequate data and derived information is serious for a number of reasons:
As long as the status-quo of cycling cannot be described by valid data, the demand for supportive policies, cycling friendly planning and funding does not become as obvious as it deserves to be. Cycling still suffers from a comparably low attention in the public discourse. And I dare to reason that the invisibility is partly due to the absence of hard facts.
Although there is still room for improvement, authorities invest in cycling infrastructure and promotion. But in most cases they are unable to asses the effect of their interventions. Sometimes punctual counts and surveys are done, but the systemic effects remains hidden in most cases. Chris Rissel and colleagues provided a nice example in 2015 for how local interventions impact punctual investigations, but tend to have a rather low systemic effect (click here for the whole study).
Without knowing when, where and which cyclists are on the road, it is hard to efficiently influence and manage cycling traffic. Even more important, as long as reasons for why persons do not cycle and local or temporal particularities remain unknown, it is impossible to target these persons and promote cycling among them. In other words, we desperately need qualitative survey data in addition to quantitative data, such as GPS trajectories, if we want to acquire an integrated picture of cycling mobility.
Interestingly, the situation has been anticipated on EU level for several years. In early 2017, Thérèse Steenberghen and colleagues published an extensive report on data availability for active modes. However, even on the country level, they diagnosed a lack of comparable data about walking and cycling, not to speak of the local level.
Yes indeed, we need more data. But before lots of data, which have minor relevance or do not contribute to answering the questions raised above, are acquired, fundamental issues need to be tackled:
Which kind of data do we need to get a holistic image of cycling mobility, to describe influential factors and to identify interdependencies between them?
Which data sources do already exist and how can additional data be efficiently acquired?
How can the availability and accessibility of data be increased in order to make them useable?
How can heterogeneous data be harmonized with regard to different data models, technical specifications and semantics?
What are efficient ways to establish monitoring systems in order to generate time series?
What are appropriate scale levels for data acquisition and analysis?
With regard to these issues, it becomes evident that we do not simply need more data, but more data that are relevant and additionally, more data intelligence. We have therefore recently launched a 30 months research project called Bicycle Observatory, in which we aim to develop an integrated perspective on cycling mobility and to further differentiate between the very different preferences and behavior patterns among cyclists.
In order to achieve these research goals we are currently evaluating existing data sources and will eventually complement them with additional data sources. A special focus lies on qualitative data from social research. The idea is to connect them to quantitative data on the basis of a common geographic reference.
Although the consortium covers a broad range of competencies and the partners bring in extensive networks, we are more than open for collaborations. Please drop me a line if you are interested in sharing your ideas, data, questions or examples!
Back in 2016, I’ve presented a review study on bicycle routing portals and safety as routing criteria at the International Cycling Safety Congress in Bologna (ICSC).
Now, 16 months later, I’m happy to announce that an extended version of this contribution has been published in the Open Access journal Safety (follow this link to read the paper). Together with Hartwig Hochmair from the University of Florida, we updated and extended the study and derived recommendations on how to better address prevalent safety concerns in trip planners.
Safety concerns are still a major barrier for a larger bicycle mode share in everyday mobility (see e.g. Wegman et al. 2012). In this article, we hypothesized that current online trip planners do not sufficiently consider safety as decisive criteria for route choice. Given the fact that many cities around the world are still lacking of adequate, connected infrastructure, we consider trip planners as a potential element to bridge the gap between safety concerns and the built environment. Routing optimization could be used to highlight and recommend safe routes.
However, we have learned in our review study that virtually none of the investigated portals explicitly provides safety as a routing criteria. On the one hand this might be due to liability reasons, but on the other hand there are a few examples, which optimize routes in terms of safety (see this example, where the recommended route is explained as safest connection).
Factors that contribute to the routing criterion “safety” according to Hochmair 2005 (orange), and general routing criteria (green). Both classes are mapped to the analyzed portals in the matrix (right).
People’s mobility, and thus bicycling, is spatial by its very nature. Being mobile by bicycle means to ride from one location to another in a given environment. Fundamental geographical characteristics, such as neighborhood, accessibility or distance, determine mobility to a certain decree. However, these interdependencies are often neglected in bicycling research, planning and politics. The consequences of non-spatial approaches become evident in many cities: the environment (neighborhood) of bicycle ways is not considered and thus often unattractive or not suitable, central facilities are poorly linked to bicycle infrastructure (accessibility) or not straightly connected (distance). The graphs below show the increasing distance travelled by commuters in Austria. The proximity between place of residence and workplace directly affects the mode choice.
Klick on the image to open the interactive view.
In order to explicitly consider the spatial nature of bicycling mobility and to relate multiple perspectives on the environment, Geographical Information Systems (GIS) are increasingly employed in bicycling research and promotion. GI systems are capable to model and digitally represent all relevant physical objects (road infrastructure, facilities, land use etc.) and moving subjects, including quantitative and qualitative attributes. Using the geographical coordinate as common denominator, all entities, together with domain-specific attribution can be related to each other. This way, additional insights and new information about the multifaceted system of bicycling mobility can be gained.
Such integrated approaches are especially beneficial in the context of bicycling, where not only rational, but also subjective (for example with regard to safety) factors, together with interests of various stakeholders need to be considered. Facing and adequately addressing this complexity is also relevant in bicycle-related research. Explicitly geo-spatial approaches leverage existing domain knowledge and contribute to better results. Representing, modeling, analyzing and visualizing different perspectives on bicycling in a spatial framework leads to new knowledge and a strong evidence-base for informed discussions, participation processes and policies.
At this year’s POLIS conference I’ll present three case studies, which proof the integrative power of geography and the contribution of GIScience to bicycling research:
1. FamoS
Preliminary result of an agent-based bicycle flow simulation.
To strengthen active forms of mobility, it is necessary to adapt the road network in a way which allows optimal usage in spatial as well as temporal respect. The research project FamoS, started in September 2016, investigates the potential of traditional demand based traffic models (“4-step-model”) and of agent based simulation models to estimate the volume of bicycle traffic for entire cities at a maximum detailed scale level. These models are then fed into a novel planning tool, which facilitates evidence-based decisions in the process of planning and (re) organizing public space for active mobility.
2. GISMO
The research project GISMO, started in October 2016, integrates domain-specific know-how from various disciplines, namely GIScience, sports medicine and mobility management. As part of the project, the health effects of several interventions that promote sustainable, active mobility are investigated in a clinical study. These data are then combined with spatial models and analysis routines in a comprehensive map-based information platform, where the spatial characteristics of commuting trips and expected health effects are considered in mobility recommendations on an individual level. For a brief project update see my last post here.
3. Planning a Bike Sharing System
In order to transfer existing knowledge on Bike Sharing Systems (BSS) and parameters to a specific urban setting and to provide an evidence base for decision makers, we applied a generic spatial framework to the city of Salzburg (Austria), which merges spatial analysis results, expert knowledge and feedback from citizen participation processes. With this approach the potential demand could have been estimated for any location in town. Moreover, the contribution of each station location to the entire system was spatially modeled and optimized.
The spatial framework will be published and presented at next year’s TRA conference in Vienna.
In all thress presented cases solutions emerged that would have not be possible in the respective domain silos. However, the geographical space (concepts from geography and GIScience) is an efficient facilitator for cross-domain collaboration and knowledge generation. Domains (such as health science and medicine) and applications (such as transport modeling) which are often disconnected from bicycling research and promotion are integrated on the basis of common geographical coordinates. Consequently, the complexity of bicycling mobility can be better addressed when various perspectives on bicycling and respective interdependencies are explicitly considered.
Started in fall 2016, the ongoing research project GISMO (Geographical Information Support for Healthy Mobility) is the first of its kind – at least here in Austria. It brings together domain expertise from very different fields in order to generate an evidence base for companies that seek to improve their employee’s health. Medical doctors from sports medicine and cardiology, GI scientists, planners, traffic engineers and mobility consultants collaborate in a highly inter-disciplinary setting. The research project is funded by the Austrian ministry for transport, innovation and technology in the program MdZ.
Concept of the GISMO project.
The project’s main idea is the following: commuting to work is time-consuming and if done by private car bad for many reasons: congestions, noise, air pollution, space consuming, expensive and inactive. The project aims to tackle the last aspect and provides highly detailed information for companies about which return they can expect from investing into employee’s active mobility. It is important for employers to get an idea how effective different interventions are. On the other hand, employees can only be motivated to change well established commuting routines when the alternatives are realistic and attractive.
This is why we (a) started a clinical intervention study, (b) developed advanced routing algorithms and spatial models and (c) pack all the information into an intuitive, interactive information platform.
Lots of activities have been going on during the first twelve months:
A clinical intervention study with 70 subjects was designed. The study was approved by the responsible ethic board, before it was implemented in a large company.
The study design is the following: 70 car commuters are randomized either into two intervention groups or into a control group. In one intervention group subjects are motivated to switch to bicycle commuting. Subjects in the other intervention group switch to public transit and walking. All subjects are medically investigated before and after the intervention. Additionally, all subjects are required to document their commuting mobility in a diary. In order to validate this documentation and to derive estimations for the energy turnover, the subjects wear GPS fitness watches for two weeks in the beginning and another two weeks towards the end of the intervention.
The aim of this study is to estimate the health effect of active mobility interventions, which can be implemented in any company.
In order to recommend realistic routes for active commuting, we developed a sophisticated routing workflow, which makes use of a national, multi-modal routing service (VAO). The routes are optimized in terms of health (minimum distance for walking or bicycling) and travel time.
Together with the routing recommendations users of the platform are provided with spatial information about the quality of the environment. For this, we have developed spatial models that calculate walkability, bikeability and PT quality indices and map them at a very high spatial resolution.
Walkability and bikeability index for the federal state of Salzburg. The spatial resolution is 100x100m.
Currently, the project partner TraffiCon is developing the concept for the web-based information platform. A first proof-of-concept will be presented at next year’s TRA conference in Vienna. The platform will provide detailed information on health effects of active commuting, recommendations for individually optimized routes and information on potential interventions for companies.
Two master students at Z_GIS have started to analyse the GPS and heart rate data from the first data collection phase. First results look very promising with regard to mode detection and trip parameters.
The clinical study is going to run until May 2018 and first results are expected to be available soon afterwards.
As project leader I’m happy to say that the collaboration with partner from very different domains is extremely fruitful. Actually, we learn a lot from each other and it becam obvious that there are a lot more common interests (“What have GI scientists have to do with cardiologists?!”) than we had expected!
In order to share our experiences and to learn from others who are doing research in similar settings, we will organize a special session at the GI-Forum conference 2018 in Salzburg. The call for papers has already opened – you should definitely have a look at it.
Modal split for Austrian kindergartens and schools.
A recent publication of the national klimaaktiv program caught my attention last week. Following this report, 55% of all kids in Austria are brought to kindergarten by car. The share of kids cycling to kindergarten is below 5%.
One of my daughters on her way to kindergarten. We had a great winter season in 2016/17!
As the figures are for Austria, I don’t know if they are representative of my home town. Unfortunately, I haven’t found any statistics for Salzburg. My personal experience is that the number of kids on bicycles is very low – at least in my kids’ kindergarten. Most of the time we are the only one coming by bike.
Independently from the availability of statistics, open (government) data allowed me to do a bit of spatial analysis on the accessibility of kindergartens in Salzburg. The results are striking: there is virtually no need to bring kids to kindergarten by car in terms of distance or travel time. However, the environment of kindergartens is not always pedestrian- and bicyclist-friendly and of course, this impacts the mode choice for bringing and picking-up kids.
For the analysis I used the following data and settings:
Address data are available from the federal Bundesamt für Eich- und Vermessungswesen. The dataset can be downloaded from their website.
The location of kindergartens are published as OGD by the city administration. The most direct way to the data is to use this WFS. I need to add a disclaimer here: I’m not sure whether all kindergartens are included in this data set. Probably, not all private facilities are on the list.
For the network analysis I used authoritative road data (GIP), published as OGD and enriched this dataset with additional information, which is also available via the national OGD portal.
I used the routing engine from ArcGIS Desktop 10.4 for performing the network analysis.
For the optimization of bicycle routes I employed the impedance model we have developed a few years ago for a bicycle routing service (see here and here for details). An average speed of 15 km/h (which might be a little bit too high) and global turn impedance at intersections were used as input parameters. For the pedestrian routes the shortest path was calculated and an average walking speed of 0.8 m/s was assumed.
The walkability and bikeability index were modelled in the context of the GISMO research project; details are going to be published soon.
The average walking time to the next kindergarten, considering all address points in Salzburg is 8.14 minutes with a standard deviation of 6.10 minutes. The median is 6.86 minutes. 74% of all address points (16,741 of 22,694) are within 10 walking minutes from the next kindergarten. This means that 3 out of 4 kids could walk to kindergarten within a reasonable time.
These figures are even more striking when kids cycle to kindergarten. The average travel time is 2.90 minutes (!) with a standard deviation of 2.21 minutes. The median is 2.42 minutes. From 89% of all address points in Salzburg the next kindergarten could be reached in less than 5 minutes. This figures rises to incredible 98% for a maximum travel time of 10 minutes. Plotted on a map these figures look like this:
Travel time to the next kindergarten calculated for each address point in Salzburg. The left map shows travel times for pedestrians, the right one for bicyclists.
Although these numbers are striking, obviously, there are good reasons for parents not to let their kids walk or bicycle. Interestingly, the scientific evidence on influential factors on the mode choice for kindergarten-related trips is weak (not to say nonexistent). Studies of school pupils’ commuting trips reveal a significant impact of parent’s perception of the suitability of the environment on the mode choice (see for example Timperio et al. (2004) or Bringolf-Isler et al. (2008)). The impact of parents’ perception and behavior might be even bigger for kindergarten kids. In any case, the quality of the environment needs to be high in terms of pedestrian- and bicyclist-safety if active mobility should be further promoted. That this is currently not always the case, becomes obvious in the following maps:
Quality of the environment for pedestrians and bicyclists. Not all kindergarten locations are well-connected to a network of suitable (safe and comfortable) roads.
Last year, results of a large study on the relation of urban environments and level of physical activity were published in the prestigious medical journal The Lancet. The authors found that approximately 50% of the WHO recommendation of 150 minutes physical activity per week can be stimulated by an adequate environment.
Thus, the provision of safe and comfortable infrastructure has a huge effect, far beyond kids walking or cycling to kindergarten. Usually, this effect is quantified in terms of health, environment or economy. But apart from these important dimensions there is another one when it comes to kids. As Moran et al. (2017) proved earlier this year, the mode choice influences navigation skills and (spatial) knowledge of the neighborhood. As a geographer I’m tempted to conclude that these findings are a sufficient argument for further promoting active commuting to kindergarten and school!
Today, I had the honour to chair another special session that dealt with GIS and mobility research at this year’s GI-Forum conference. The session “Spatial Perspectives on Active Mobility” was the third in a series (see here for a review of the 2016 and here for the 2015 session).
Of course, we will have a “Spatial Perspectives on …” session in 2018 again – the call will be published in December this year. So, consider this as an option for your publishing and dissemination strategy (by the way, the GI-Forum journal is open access!)
This year’s special session was a paper session with four speakers, who all went through a rigorous review process. The diversity of the contributions was high, demonstrating the wide range of mobility research where GIS plays a crucial role:
Irene Fellner from Vienna University of Economics and Business opened the session at the very local scale. She presented her work on landmark-based indoor navigation. Although the applied ILNM (“indoor landmark navigation model”), an extended version of Duckham’s et al. (2010) LNM, performed well, Irene pointed to two major challenges: first of all, the ILNM requires very detailed data, which are not always available and secondly, the visibility of landmarks from the perspective of the user is not always given or unknown.
Irene’s paper emerged from her master thesis at the University of Salzburg, where she successfully finished the UNIGIS MSc study program. Dr. Gudrun Wallentin, UNIGIS program director, regarded this special session as perfect stage to hand over the UNIGIS International Association (UIA) award for excellent master theses. Congratulations!
Ulrich Leth (Vienna University of Technology) presented the findings of a recent study where they investigated the impact of a bike sharing system on public transit ridership in the city of Vienna, which is famous for its extensive and well-performing public transit system. In total, Ulrich and colleagues analysed 1 million Citybike trips from 2015. Different to the expectation the title provoked, they found that the bike sharing system virtually has no impact on PT ridership, simply because of the huge difference in size and capacity. However, some details in their results were interesting and probably of relevance for other BSS: a) Citybike trips primarily substitute short and inconvenient PT trips, b) most bike sharing trips are made when the travel time ratio compared to public transit is 0,5 and c) the most popular OD relations are typical student trips (between transport hubs and university and student dormitories and transport hubs or universities).
Tabea Fian, a student from Georg Hauger’s (lead author of the paper) group, also from Vienna University of Technology, presented a spatial analysis of urban bicycle crashes in Vienna. Interestingly, the data were very similar to those I’ve extensively used in my PhD (see this paper). In a purely exploratory study design Georg has tried to identify blackspots in the network and tested for their significance. However, as it became evident in the discussion, final conclusions are hard to draw without a statistical population.
The last presentation was given by Anna Butzhammer from RSA iSpace. She presented parts of her excellent master thesis, in which she analysed the inter-modal accessibility of central places. For this, she developed a model that facilitates door-to-door travel time calculations with different modes. Her findings are especially important for planning and optimizing public transit systems, which can be regarded as backbone for sustainable mobility.
Tomorrow, the German-speaking sister conference, AGIT, will host a special forum on autonomous driving and on Friday I will chair another session on advances in GIS-T. Well, there will be a lot to discover, learn and discuss; if you don’t have the chance to be there physically, follow me on Twitter and stay updated.
Take all relevant research institutions, planners and consulters, interest groups, authorities and manufacturers that are engaged in bicycling – voila, what you get is “Cycle Competence Austria”, an association of researcher and practitioners, who joined forces for the sake of further pushing the current bicycling boom and making knowledge available.
Klick on the picture to open a short Storify summary of the session.
The world’s biggest bicycling summit – Velo-city – takes place in Arnhem-Nijmegen, in the Dutch province of Gelderland these days. Today the Cycle Competence Austria had the nice opportunity to present bicycling knowledge “Made in Austria” to a broad audience.
Being a nation with still a lot of potential for a larger bicycle mode share, but quite exhaustive experiences and a growing body of knowledge, Austria can serve as front runner for so called climbing nations. In this session, six members of the Cycle Competence network presented their respective contribution to a prospering bicycling environment.
Martin Eder, the national bicycle advocate, started the series of presentations with an overview of national activities for bicycle promotion. He paid special attention to the second edition of the national masterplan, in which the official goal of 13% in the modal split by 2025 is published. In order to reach this, several national initiatives, such as the research funding program “Mobility of the Future” are launched and supported.
After Martin, Andrea Weninger from Rosinak & Partner shared here extensive experience in bicycle masterplan creation processes. She came up with a list of six points, which she regards to be essential for successful planning processes. Two of these success factors are to go for user-tailored masterplans (instead of copy-pasting elements from elsewhere), which are inspired by locals.
Andreas Friedwagner (Verracon) went on with a GIS-based analyses of accessibility and travel time analysis in the federal state of Vorarlberg. His beautiful maps clearly indicate which areas are well-served in terms of bicycle infrastructure and where improvements need to be made in order to motivate people to switch from car to active mobility. Interestingly, Andreas found in his studies that speed limits for cars (30 km/h within residential areas) have the most direct impact on overall bicycling safety.
Currently we are in an interesting transition phase from data scarcity in bicycle promotion to a data deluge (one of Andrea’s argument was that not everything that could be measured really contributes to a better understanding). However, the colleagues from BikeCitizens with their CEO Daniel Kofler do a great job in packing routing and navigation, promotion with gamification components and bicycle intelligence into a single app: the BikeCitizens app.
The session was completed by two contributions from research institutions. First I gave an overview of three current research project and argued that the spatial perspective facilitates joint efforts across domain boundaries:
After my presentation, Markus Straub from AIT presented two projects, each with a spatial optimization component: the EMILIA project seeks, among others, to optimize parcel deliveries in cities. In order to so the last miles from central distribution hubs to the consumer should be done by cargo-bikes. Markus and his colleagues have developed a route optimization algorithm for the delivery bicyclists. In the BBSS project a spatially explicit planning tool for optimizing the location of bike sharing stations was developed. This tool allows planners to estimate the potential demand for any location in a city.
Got interested in what happens in Austria in terms of bicycling research and promotion? Leave a comment here, visit the Cycle Competence Austria association booth at Velo-city or you can use Twitter or e-mail anytime.
Our colleagues from Salzburg Research (SR) are very active in the field of floating car data generation, management and analysis. Among others, this real-time traffic status service is fed by their data.
In order to establish a community of researchers, authorities and companies around the topic of floating car data, SR hosts the annual “FCD Forum” in Salzburg. This year, I had the honor to contribute to the program. Since we have been working a lot with bicycling data over the last years, I was asked to evaluate the potentials of a conceptual transfer from FCD to “Floating Bicycle Data”. Well, a very fundamental finding in my research is that the term “Floating Bicycle Data” is not established yet in the scientific literature. Thus, the term is to be regarded as a word game derived from the forum’s agenda. However, I think it makes perfectly sense to invest some efforts in this context.
In my presentation, I started my argumentation from the fact that a) bicycle traffic is a relevant element of urban mobility, b) the modal share is likely to increase in the next years and c) a sound evidence base is required for future investments in bicycling infrastructure.
Currently, very little is known about the spatial and temporal distribution of bicycle traffic within cities. Comparably few permanent counting stations, sporadic, punctual counting campaigns and irregular mobility surveys do not provide sufficient and reliable data to support evidence-based policies on the local scale level. On the other hand, the popularization of the “humans as sensors” concept (Goodchild 2007) has opened new possibilities to acquire data on bicyclists’ movements in urban networks. When talking about floating bicycle data, I used it as a catchy term, which summarizes all kind of geo-located movement data from bicyclists; they don’t need to be necessarily in real-time.
As I’ve shown in my presentation, there a numerous application examples where floating bicycle data would make perfectly sense. However, there are several conceptual challenges, which need to be considered (most of them are also relevant for floating car data):
When floating bicycle data are harvested through crowd-sourcing applications the data are not necessarily representative for the entire population. I referred to participation inequality or the 90-9-1 rule (see Nielsen 2006) in this context. Additionally, different apps are used for different purposes. Thus, the data might be biased for example towards leisure trips (as it is the case with Strava data in Salzburg).
Currently, there is no common data standard and the heterogeneity of bicycle mobility data is huge. Good news in this context were published earlier in this year by the European Commission (see this report from the COWI project).
Since there is no obligation to register bicycles, the (spatial distribution of the) totalpopulation is unknown. Consequently, it is hard to estimate the total bicycle traffic volume from samples. In contrast to that, cars are registered and at least the car holders’ address is known.
In order to further process movement data (GPS trajectories), a sound and very detailed reference graph is required for map matching. In most cases network graphs are not available at this level of detail (this holds true for authoritative data as well as for OSM). Consequently, GPS trajectories can only be matched to center lines at the moment.
Although this selection of challenges might be regarded as obstacle for a broader engagement (I prefer to interpret them as research opportunities), I expect the topic of floating bicycle data to emerge in the coming years for a simple reason: the market for floating bicycle data is definitely smaller than for floating car data. But, bicycle traffic is already a major element in urban traffic and its share will become even more substantial in the next years. As a consequence, cities need to invest in adequate infrastructure and these investments will hardly be made without a sound evidence base. Floating bicycle data could close a significant gap in this regard.
If you are already working with floating bicycle data (but haven’t used the term yet), have ideas on how to further push the topic or simply want to comment on the concept, please do not hesitate to contact me! I’m happy to learn from your expertise.
For those who are about to write a thesis in this or a related context, have a look at this proposal.
After several months of setting the stage and doing lots of preparatory work, we are currently entering the ‘core phase’ in two research projects at the GI Mobility Lab. In this context we provide the opportunity to Master’s students to participate in the projects and write their thesis in GIScience (or related fields).
FamoS Our part in the FamoS project is, among others, to develop an agent-based bicycle flow model for an entire city. In this context we offer two topics:
Exploring geoprocessing, geovisual analytical and mapping functionalities of GAMA (description)
GISMO Experts from sports medicine, GIScience and transport planning and management are collaborating in the GISMO research project in order to provide a sound evidence basis for the promotion of active commuting. Part of the research is a clinical study, in which we document the subject’s mobility by different means. For the analysis of this data we offer the following two topics:
Analysis of movement data from fitness watches (description)
Linking travel diaries and GPS trajectories (description)
The topics are primarily offered to local and UNIGIS students. However, I’m also open to any other form of supervision and collaboration, given we find a sound format for it.
Since the VeloCity conference took place in Vienna in 2013, the Institute of Transportation (Vienna University of Technology) hosts an annual lecture series on bicycling and active mobility in general.
This semester, 80-100 students from various planning domains (urban, transport, regional planning) are attending the weekly lecture on “Active Mobility”. Yesterday I had the privilege to present parts of my current research and provide an overview of potential contributions of spatial information to an enhanced bicycling safety situation (slides in German language):
Although some of the students have already worked with GIS, none of them employe GIS in the context of mobility or transport research (at least nobody raised his/her hand when I was asking). Thus, I was happy to serve an appetizer for introducing the spatial perspective to a rather “technical” planning community.
Originally, this blog was intended to document the progress of my PhD research. Mhm, this goal has been successfully reached yesterday …
Successfully defending my doctoral research (pictures by R. Wendel)
I finished my doctoral studies with a thesis on Spatial Information and Bicycling Safety and yesterday’s defense. The thesis is based on five peer-reviewed, published papers and aims to strengthen the spatial perspective in bicycling safety research.
The thesis is motivated by the fact that bicycling safety research is dominated by non-spatial domain experts, e.g. with backgrounds in trauma medicine, psychology, bio-mechanics, sociology, epidemiology, engineering, planning, law and some more. Interestingly, the spatial perspective on bicycling safety is hardly ever considered in these domain-specific approaches. This holds especially true for bicycle crash analyses, where basic geographical concepts, such as nearness, spatial autocorrelation and topology, are hardly ever considered.
Neglecting location as a co-determining attribute of safety is remarkable for a very simple reason. Mobility of people – and thus bicycling – as such is spatial by its very nature. Consequently, bicycling safety (from the physical environment to crashes to individually experienced safety threats) has spatial facets, which can be modeled and analyzed accordingly in order to gain relevant information for safer bicycling.
The primary hypothesis of my doctoral thesis is that spatial models and analyses contribute to a better understanding of certain aspects of bicycling safety and provide relevant results, which support measures to mitigate safety risks for bicyclists. Specifically I argued that:
Geographical Information Systems (GIS) facilitate holistic approaches for improving the bicycling safety situation. The spatial perspective is relevant for virtually all stages of the implementation of bicycling safety strategies.
Model-based approaches have a great potential in safety assessment and can form the basis for a number of applications – from status-quo analysis to planning and route optimization.
The spatial analysis of bicycle crashes reveals significant and safety-relevant patterns and particularities, which remain hidden in common, non-spatial or highly aggregated approaches.
The spatial perspective is crucial for advanced (simulation) models, which are necessary for reliable risk estimations on the local scale. Furthermore, the spatial implications of risk mapping on the local scale must be made explicit.
The thesis is structured in three elements. The first paper demonstrates the contribution of GIScience to bicycling safety research and is intended to set the stage for the remaining papers. Two of them primarily deal with spatial models in the context of road space assessment and transport modeling, while the rest is about spatial analysis of bicycle crashes.
Structure of the thesis
Although the completion of my doctoral studies is a huge, personal milestone, there is still a lot of research work in this context to be done. Besides the further development of the spatial models, the applied statistical methods and analysis routines, I see research gaps in the context of data (from static to dynamic real-time data and data streams), information (e.g. what are the effects of information provision on decision process or on individual and collective behavior?) and cross-domain collaboration.
The amount of work that still lies ahead motivates me to further blog on some of our research activities and to connect with anyone who is interested in spatial information, bicycling safety, urban mobility etc. I’m looking forward to learning, reading and hearing from you in virtual and – even more preferably – in face-to-face communication!
OpenStreetMap is much more than a free map of the world. It’s a huge geo-database, which is still growing and improving in quality. OpenStreetMap is a great project in many respects!
But because it is a community project, where basically everyone can contribute, it has some particularities, which are rather uncommon in authoritative data sets. There, data is generated according to a pre-fixed data standard. Thus, (in an ideal world) the data are consistent in terms of attribute structure and values. In contrast, attribute data in OpenStreetMap can exhibit a certain degree of (semantic) heterogeneity, misclassifications and errors. The OSM wiki helps a lot, but it is not binding. Another particularity of OpenStreetMap is the data model. Coming from a GIS background I was taught to represent spatial networks as a (planar) graph with edges and nodes. In the case of transportation networks, junctions are commonly represented by nodes and the segments between as edges. OpenStreetMap is not designed this way. Without going into details, the effect of OSM’s data model is that nodes are not necessarily introduced at junctions. This doesn’t matter for mapping, but for network analysis, such as routing!
In 2014 I presented and published an approach that deals with attributive heterogeneity in OSM data. Later I joined forces with Stefan Keller from the University of Applied Sciences in Rapperswil, Switzerland and presented our work at the AAG annual meeting 2015 in Chicago.
Since then Stefan and his team have lifted our initial ideas of harmonized attribute data to an entire different level. They formalized data cleaning routines, introduced subordinate attribute categories and developed an OSM export service, which generates real network graphs from OSM data. The result is just brilliant!
Two maps with very different scale made from the same data set.
The service can be accessed via osmaxx.hsr.ch. There, a login with an OSM account is required. Users can then choose whether they go with an existing excerpt or define an individual area of interest. In the latter case the area can be clipped on a map and the export format (from Shapefiles to GeoPackage to SQLite DB) and spatial reference system can be chosen. The excerpt is then processed and published on a download server. At this stage I came across the only shortcoming of the service: you don’t get any information that the processing of the excerpt takes up to hours (see here).
However, the rest of the service is just perfect. After “Hollywood has called” the processed data set can be downloaded from a web server.
OSMaxx interface.
The downloaded *.zip file contains three folders: data, static and symbology. The first contains the data in the chosen format. In the static folder all licence files and metadata can be found. The latter is especially valuable, because it contains the entire OSMaxx schema documentation. This excellent piece of work, which is the “brain” of the service is also available on GitHub. Those who are interested in data models and attribute structure should definitely have a look at this!
The symbology folder contains three QGIS map documents and a folder packed full with SVG map symbols. The QGIS map documents are optimized for three different scale levels. They can be used for the visualization of the data. I’ve tried them for a rather small dataset (500 MB ESRI File Geodatabase), but QGIS (2.16.3) always crashed. However, I think there is hardly any application context where the entire content of an OSM dataset needs to be visualized at once.
Of course, OSMaxx is not the first OSM export service. But besides the ease of use and the rich functionality (export format, coordinate system and level of detail), the attribute data cleaning and clustering are real assets. With this it is easy, for example, to map all shops in a town or all roads where motorized vehicles are banned. Using the native OSM data can make such a job quite cumbersome.
I have also tried to use the data as input for network analysis. Although the original OSM road data are transformed into a network dataset (ways are split into segments at junctions), the topology (connectivity) is invalid at several locations in the network. Before the data are used for routing etc., I would recommend a thoroughly data validation. For the detection of topological errors in a network see this post. Maybe a topology validation and correction routine can be implemented in a future version of OSMaxx.
In the current version the OSMaxx service is especially valuable for the design of maps that go beyond standard OSM renderings. But the pre-processed data are also suitable for all kinds of spatial analyses, as long as (network) topology doesn’t play a central role. Again, mapping and spatial analysis on the basis of OSM data was possible long before OSMaxx, but with this service it isn’t necessary to be an OSM expert and thus, I see a big potential (from mapping to teaching) for this “intelligent” export service.
We contribute spatial information to the design and optimization of a city-wide BBS.
150 participants from 23 countries gathered on November 30th in Rotterdam to attend the VeloCittà bikesharing conference, which was held in conjunction with the annual POLIS conference (450 participants, according to the organizers). While the VeloCittà conference was exclusively dedicated to bikesharing, the POLIS conference offered a broader perspective on sustainable transport. I was in Rotterdam primarily for the POLIS conference because I had a presentation, but it was also a great opportunity to get an impressive update of recent bikesharing practice and research. Lot’s of what I’ve learned can be directly linked to our current involvement in the planning of a bikesharing system in Salzburg, Austria.
All presentations of both conferences can be found on the respective websites. Thus, I will focus only on two topics I’ve found especially relevant for our research and project work.
Willemijn Lambert (@WM_Lamber ) captured the essence of the VeloCittà bikesharing conference.
Success factors for bikesharing systems
In a very interesting session at the POLIS conference on sharing systems, Sebastian Schlebusch from Nextbike gave some insights into the company’s history. Several years they were treated quite harshly by public transit operators who feared for their business. However the break through of bikesharing systems (BSS) came. In accordance with Sebastian’s talk the following success factors occurred in various presentations at both conferences:
Cologne’s bikesharing system (KVB Rad) is integrated in the city’s public transit service.
Political support. Obviously this seems to be the most decisive factor for successful BSSs in any country.
Integrated systems. An increasing number of cities regard bikesharing systems as an element of public transit services. This is reflected in the planning of the network, pricing and promotion. Cologne’s BSS is a good example for a large, integrated system.
Robust business models. This factor becomes important when initial subsidies fade out. Alberto Castro, one of the keynote speakers at VeloCittà, demonstrated how fast BSSs without sound financial (and operational) basis disappear.
Appropriate planning. Nicole Freedman, keynote speaker at VeloCittà, made a compelling case for the importance of realistic projections and tailored BSS design. Cities are comparable only to a certain degree and thus, BSSs cannot be simply transferred. Specific (mobility) characteristics of cities, from PT service level to topography, need to be taken into account.
User-tailored, easy solutions. The needs and expectations of users must be addressed in every aspect: from intuitive interfaces for initial registration to the ease of handling the hardware.
To know and consider people’s reasons for not using BSSs is especially valueable when systems should be improved. In many cases the barriers for BSS usage can be lowered or removed with small adaptions.
Visibility in public space. In order to raise awareness for bikesharing it is necessary that the system is visible in public space. This visibility can be achieved by an appropriate station design, but also with art in public space.
Make it beautiful. Directly associated to the latter point Nicole Freedman strongly argued for aesthetically pleasing, beautiful bikes and infrastructure. Way too often BSSs are shaped by technicians and technology. With a good design of hard- and software people can be made curious; once they are attracted to the system, the possibility is high for turning prospective into active users.
At both conferences lots of case studies were presented. At least two of them were really remarkable:
Krakow (~ 760,000 inhabitants) initially launched a system with 30 stations and 300 bikes, which turned out to be not that successful. Thus, the city relaunched the entire system under a new name (Wavelo) and with 1,500 bikes at 150 stations, which is above the average bikes per people ratio in Europe (ref. OBIS handbook)!
A much smaller, but very successful BSS can be found in Pisa (CICLOPI ). Marco Bertini presented the city’s strategy to make people in Pisa love their bikesharing system: “Bikesharing is note a service for citizens, but part of the community.” With this approach Pisa achieved impressive key figures: 5-8 rides per bike and day, virtually no vandalism and not a single bike stolen in 4 years.
Road Safety
More people are killed in road crashes than by malaria or tuberculosis, according to a recent OECD report that calls for a paradigm shift in road safety. Before this background and with a special focus on the role of large cities the International Transport Forum (ITF) launched the Safer City Streets project, which was presented by Alexandre Santacreu. The aim of this project is to provide an environment for exchange of data, experience and knowledge. What I regard as an asset of this project is the drive to publish data as OGD.
Alex pointed to the difficulty of comparing data from various sources, especially when crashes of vulnerable road users are investigated (different reporting procedures, classification, under-reporting etc.). Of course, this is nothing new, but my impression is that the limited comparability of data is mostly neglected in analyses of global data (I’ve demonstrated an aspect of this in this post).
While the Safer City Streets project operates on the global scale, the Netherlands have launched a national project where cities can learn from each other with respect to crash prevention and safety measures. Charlotte Bax from SWOV presented this benchmarking project that is built upon the three elements comparing – learning – improving. Two aspects caught my attention: (1) None of the data are made public because the involved city administrations fear the pressure that might be put on them after publishing crash details. (2) Even in the Netherlands’ city administrations struggle to make use of their data; Charlotte referred to cases where responsible departments were not able to tell how many kilometers of bicycle infrastructure they had.
Benchmarking on the very local level was at the core of Eric de Kievit’s presentation on the development of a compound road safety assessment. For this, two approaches were combined. Firstly, a network safety index, which consists of an enormously detailed description of the road space (every 25 meters the road profile was investigated based on street view photos). And secondly, a safety performance indicator that focuses on road user’s behavior. Both perspectives are then used as basis for targeted infrastructure measures, law enforcement, education and communication campaigns.
My own contribution to the session on road safety was about spatial analysis of bicycle crashes on the local scale level. The presentation was a synthesis of two of my latest journal papers (JTRG and Safety):
In both conferences it became evident that there are lots of innovative and creative solutions for promoting sustainable mobility in urban environments. However, there is no philosopher’s stone that solves all problems immediately, but cities all over Europe have to work hard to make progress.
I have the strong impression that the discussion and collaboration across domains and institutions is a key for sustainable solutions for cities. Urban environments are complex and thus require multifaceted strategies. In any way, we are ready to contribute spatial expertise for the good of our cities and their citizens.
Vienna’s newly designed Mariahilfer-Straße gives priority to pedestrians and bicyclists (image source: Christian Fürthner/MA 28)
In 2015 we organized the first special session on GIS and transport at the GI Forum conference in Salzburg (Austria). Since the event was a full success in 2016 as well, we will prolong the series in 2017 and call for contributions.
Since the promotion of active mobility has become a central element of virtually any urban planning and development strategy, health issues force societies to get physically active again and the amount of research has skyrocketed, it is time to gain a “spatial perspective” on the topic.
Research on active mobility is of course a multi-disciplinary field and lots of, partly very specific studies contribute to the growing body of literature. However, it is interesting that a substantial share of recently published studies from non-spatial domains have geographical elements at their core. The latest series on urban design, transport and health in the medical top journal The Lancet is only one of several prominent examples.
Before the background of our own research (see one of my last posts) and the relevance of the topic, we organize another special session – hopefully with your contribution!
During the 2017 GI Forum conference we will collect, present and discuss spatial perspectives on active mobility. The call for papers is also available on the conference website:
There are many good reasons to promote active mobility: road congestions, limited space resources, public health issues, air pollution and noise emission, just to name a few. Consequently, various institutions and research domains have active mobility at the core of their activities. The geographical space can serve as common denominator that brings together the multiple approaches towards active mobility. Geographical Information Systems (GIS) hereby serve as integrative platforms that combine, model and analyze the variety of perspectives and data. The overall aim is to facilitate holistic approaches and to extract relevant information for stakeholders and decision makers.
The 2017 GI-Forum special session will be the third of a series that deals with relevant research topics at the intersection of GIS and mobility. We invite researchers from any domain to submit original research, which has spatial information at its core. Relevant topics are (but not limited to):
Spatial data acquisition for active mobility research (OGD, VGI etc.)
Spatial models and simulations for pedestrian and bicycle traffic
Spatial analysis of barriers for active mobility (safety, accessibility, attitudes and behavior)
GIS in planning and decision support systems for active mobility promotion
Showcases from all disciplines (sports science, environmental psychology, transport science, planning etc.) that build on spatial information
Contributions can either be submitted as full paper, extended abstract or poster. Any contribution needs to be submitted via the conference submission website and will be object to the double-blind, peer-review process. Authors of accepted full papers are going to be invited to present and discuss their paper (15’+5’) in the special session. Authors of extended abstracts and posters are going to be invited for an elevator pitch (5’). Full papers and extended abstracts will be published in the GI-Forum journal (Open Access).
Besides the special session, which will be organized as paper session, we will provide opportunities for further exchange, project drafting or discussing potential joint publications in an informal workshop format.
Although the impact of information on mode and route choice is disputed, the number of bicycle routing and navigation applications is constantly growing.
For this year’s International Cycling Safety Congress (ICSC) we have investigated 30 current bicycle routing portals with a specific focus on “safety”. The study is limited to web applications with a desktop version and without obligatory registration. Mobile apps, which are increasingly standalone products (or environments) were not considered.
Click on the picture to download the conference paper with all details of the study.
The central hypothesis of our study was that existing bicycle routing portals don’t address prevalent safety concerns explicitly. We further argue that bicycle routing portals might contribute to the promotion of safe(r) routes and consequently to an overall perception of the bicycle as safe mode of transport.
With this study we take a first step towards a better exploitation of information applications’ potential to promote (utilitarian) bicycling. Based on our evaluation, bicyclists’ expectations and the role of routing information in their mobility routines should be investigated in more detail. This would allow for the formulation of design guidelines for future information products for bicyclists.
However, we are totally aware of the fact that information as such can never improve the safety situation – this can only be done by adequate infrastructure. But we see the potential of bicyclist-specific (routing) information to bridge the gap between the current, mostly sub-optimal safety situation and a perfect environment. Geographical Information Systems (GIS) allow for the identification of optimal routes in terms of safety. Depending on the infrastructure, recommended connections might not be perfect, but the best possible solution in the given situation. We have made quite good experiences in this regard with the bicycle route planner we have developed for Salzburg (see Radlkarte.info).
I know of many highly innovative bicycle routing and navigation applications and I’d be more than happy to learn from your experiences and expertise. I guess we could make a step forward and provide better, user-tailored information if we joined forces. As an invitation to further work on this topic we make the data of our study fully available. You can access the evaluation spread sheet via this link. So let’s get started …
This post is an update of current research projects I’m involved in as member of the GI Mobility Lab. The nice thing is that all three projects allow us to work with domain experts from very different fields: public transit planners, medical doctors, transport engineers etc. And although the contexts of the featured projects are diverse, they all have two things in common: (1) the bicycle is in the focus and (2) we add a distinct spatial flavor to the overall research approaches.
Bike Sharing
The city of Salzburg is definitely not a front runner when it comes to bike sharing. However, the city is currently pushing the topic. In order to achieve a better evidence base for future decisions, our lab was invited for a study on the expected user potential of bike sharing in Salzburg.
For this study we developed a study design that on the one hand incorporates existing findings from literature and on the other hand explicity considers the spatial configuration of the city. Additionally we launched an open online survey with which we aimed to better understand the needs of potential users.
Different to most of the existing planning approaches we used spatial, socio-demographic data to estimate the number of potential users on the local scale. We extracted the most relevant socio-demographic determinants of bike sharing usage from literature and mapped them. These maps nicely represent the character of the city (e.g. the distribution of academics or the spatial patterns of work places). Based on structural analysis of the city we calculated different scenarios of bike sharing penetration levels for every single census block.
Currently we are working on the final report – results will be published on our website.
FamoS
The project FamoS (Fahrradverkehrsmodelle als Planungsinstrument zur Reorganisation des Straßenraums) aims to establish a sound data base for transport models, develop bicycle flow models, and implement these models into planning tools for the evidence-based re-organisation of the road space. The project (FFG #855034), which is led by the Technical University of Graz, is funded by the Austrian Ministry of Transport, Innovation and Technology under the “Mobilität der Zukunft” program.
The background of the research project ist to strengthen active forms of mobility and to provide an evidence base for targeted interventions. For planning and (re)organization of public roads and places, suitable data and innovative planning tools must exist for these user-groups. Widespread analyzing, planning and simulation tools already exist for motorized forms of mobility, but to introduce evidence-based measures and politics for active forms of mobility, still considerable information- and planning barriers exist.
Our role in this project is to establish a consolidated data base for transport models and to develop an agend-based model for bicycle flows in Salzburg. It gives us the opportunity to further improve a first ABM-based bicycle flow model for Salzburg and for Gothenburg. Methodologically the project partly builds on one of my recent papers on GIS in transport modeling.
GISMO
At a first glance there seems to be little overlap between sport medicine and GIS. Nevertheless we recently kicked-off a project, which is located at the intersection of medicine, mobility management and GIS. GISMO – Geographical Information Support for Healthy Mobility (FFG #854974) is also funded by the Austrian Ministry of Transport, Innovation and Technology under the “Mobilität der Zukunft” program. The project is coordinated by our department. We cooperate with five partners from Vienna, Zurich and Salzburg (a German language overview of the consortium can be found here).
GISMO aimes to support healthy mobility in the application context of corporate mobility management initiatives. As part of the project, the health effects of several interventions that promote sustainable, active mobility are investigated quantitatively. These data are then combined with spatial models and analysis routines in an integrated information platform which is subsequently evaluated.
The overall research goal is to estimate the health effect for each mode of transport for the individual, spatial relation between place of residence and working place. With this approach existing lines of argument that primarily focus on mobility and environmental effects as well as on efficiency, are complemented with components addressing employee’s health and health prevention. The drafted information platform serves as innovative solution for evidence-based planning, consulting and information.
For the projects FamoS and GISMO we are currently looking for an additional researcher. In cases I have raised your interest and you want to join us, have a look at the job advertisement.
I see many, many links to similar, existing projects and studies. The body of literature on bike sharing, transport modeling and healthy mobility is huge. Nevertheless, a lot of work still lies ahead. GIS and the spatial perspective on bicycle mobility are capable to leverage existing approaches to a next level and to generate additional insights.
Which links and overlaps do you see to your work? Feel free to comment on this post or use the contact form – I’m happy to learn from your experiences and ideas!
Earlier this year we published a very detailed spatial (and temporal) analysis of bicycle crash data from Salzburg (Austria) in Transport Geography. In this paper we demonstrated the additional benefit of an explicit spatial perspective on crash reports. However, one of the major objections was, that meaningful conclusions from such an analysis can only be drawn when an exposure variable is introduced. This objection stems from the well established methodology of risk calculation in bicycle safety analysis (the quality of commonly used exposure variables is a whole different story as I’ve exemplified in an earlier post).
Because of the lack of sound exposure variables on the local scale – this is the scale I’m especially interested in – most bicycle risk analyses are done on a highly aggregated level. Last year we were, at least partly, successful in overcoming this shortcoming. With an agent-based simulation model (Wallentin & Loidl 2015) we estimated the traffic flow for every road segment in an urban road network. This model allowed us to take the final step now: bicycle risk estimation on the local scale.
Theoretically we are able to calculate incident rates (commonly used synonymously with “risk”) for each and every road segment. However, thanks God, bicycle crashes are relatively rare; and officially reported ones are even rarer. Consequently the statistical robustness of calculated incident rates is weak, leading to analysis results that are potentially biased by random effects. Thus, we defined and investigated different spatial reference units, which served as spatial aggregation levels:
Choosing the adequate spatial reference unit is a trade-off between detail and reliability (statistical robustness). Shape and size (level of aggregation) of the spatial reference units are expected to impact the analysis results.
Whenever point incidents are spatially analyzed, two well-known and still challenging phenomena need to dealt with: spatial heterogeneity and the modifiable areal unit problem (MAUP).
Although the Geography literature on these two implications is full, they are hardly ever anticipated in (bicycle) crash analyses. We therefore regard our paper not only as a presentation of our analysis results, but also as an example for how to adequately deal with geo-located data.
Here is the abstract of the paper (full text), which was published in a special issue of the OA journal “Safety”:
Currently, mainly aggregated statistics are used for bicycle crash risk calculations. Thus, the understanding of spatial patterns at local scale levels remains vague. Using an agent-based flow model and a bicycle crash database covering 10 continuous years of observation allows us to calculate and map the crash risk on various spatial scales for the city of Salzburg (Austria). In doing so, we directly account for the spatial heterogeneity of crash occurrences. Additionally, we provide a measure for the statistical robustness on the level of single reference units and consider modifiable areal unit problem (MAUP) effects in our analysis. This study is the first of its kind. The results facilitate a better understanding of spatial patterns of bicycle crash rates on the local scale. This is especially important for cities that strive to improve the safety situation for bicyclists in order to address prevailing safety concerns that keep people from using the bicycle as a utilitarian mode of (urban) transport.
Crash locations (left); Risk calculations for the whole city of Salzburg and census districts (right). Each risk map is supplemented with a map that shows the 95% confidence interval of the incident rates (= indicator for statistical robustness of results).
With this analysis we have successfully demonstrated that mapping bicycle risk patterns on the local scale reveals relevant information for policy makers and authorities, which aggregated approaches would not have been able to uncover. To our current knowledge this is the first study, which calculates crash rates on the local scale. However, with the increasing amount of available data and improved (spatial) models, we are quite sure that many more analyses like this one will follow – for the good of bicyclists and building blocks for evidence-based safety strategies.
As the number of geographers dealing with bicycle safety and crash analysis is rather small, I’d be more than happy to read from you. Do you have any questions, ideas for further studies, data or just a comment – feel free to leave your note below, connect on Twitter or get in touch with me via the contact form.
The twin conferences AGIT and GI-Forum took place in Salzburg three weeks ago, complemented by the German language FOSSGIS conference. This fully packed conference week had a lot to offer (see my Twitter diary) and definitely was an inspiring week. With a short time lag in between I’d like to reflect on a topic that popped up at various occasions and is very relevant to my PhD project: geography’s contribution to mobility and transport (research).
To make it very short, these are my key take home messages:
The geo-space is very powerful in integrating various data/information layers and facilitating holistic approaches for research, planning and operation.
Technology driven arguments are annoying. It’s always about people.
Geography supports system thinking, which is required in any mobility and transport topic.
Harvey Miller from Ohio State University opened the GI-Forum conference with his keynote on “Big Data for Healthy Places”. Referring to Pollocks article in Nature, Harvey made a strong case for how the built environment affects mobility and subsequently public health. In his keynote Harvey identified two major challenges in the context of healthy cities: firstly, cities, which are human systems, are complex systems and secondly, policy interventions can have unclear or even counter-intuitive outcomes. In order to tackle these challenges, Harvey proposed what he termed Geographical Information Observatories (GIO), which facilitate opportunistic GIScience. A GIO is a way to constantly monitor certain areas or phenomena and link the sensed data to other data or information sources. Here, the geographical coordinate plays a central role as common denominator for all data or information layers (‘spatial index’). So called urban dashboards (such as CURIO), which are fueled by GIOs, are the basis for opportunistic GIScience, a framework for spatial science which is able to adapt to spontaneous events, combine real-time with historic data and to simulate planned interventions in a virtual environment. This way, complex systems can be studied, monitored and influenced in a naturalistic setting and intended measures can be tested for their effect on the whole system prior to implementation.
Some of the keynote’s topics had already been discussed before in an interesting panel discussion on the relation between GIScience and Data Science, organized by Peter Mandl. Besides Harvey, Petra Staufer-Steinnocher and Josef Strobl discussed as panelists.
Peter argued for the integration of recent developement in GIScience, namely linked data, open data and semantics, into “Spatial Data Science”.
Harvey made two crucially important points: Data scientists tend to go for correlations (predicting and control paradigm) instead of focusing on causalities in complex systems; for the latter domain experts are needed who interpret correlations in the respective (spatial) context and transform data into information. Conceptually related to this observation, Harvey pointed to the fact that not all decisions should be made quick and purely data- or algorithm-based (the reference to the Jevons paradox is highly interesting in this context). This critical statement is often missing in Smart Cities debates!
Similar to Harvey, Josef made a few conceptual statements, which are often overlooked in “data-positivistic” discussions. In his opinion, correlations and pattern detection are only ways to make sense of massive data (streams); they have little value for themselves but act as filters and hypothesis generators. Again, he underpinned the role of domain experts, who are indispensable when exploratory studies are lifted to explanatory ones. In analogy to this conceptual difference and referring to the relation of GIScience and Data Science, Josef stated, “Data leads to explorations, science leads to findings”.
Being affiliated with the Vienna University of Economics, Petra put a focus on Business Analytics (which has, of course, a lot in common with Data Science!) and called for a tight coupling of data driven approaches to theory-based science. In her opinion, Business Analytics is currently too often only about dehumanizing people (clients) and turning them into data.
On Wednesday Anita Graser kicked-off the German language AGIT conference with her keynote on “OpenSource, OpenData and OpenScience”. In the afternoon I first attended a session on Urban Geoinformatics (I co-authored one of the presented papers), which was nicely wrapped up by Joao Porto. He stated very clearly that Urban Geoinformatics is the intersection of people (urban), technology (informatics) and place (geo). This rather simple definition is blanked out ways to often in current discussions!
After that, this year’s special session on GIS-T (“Spatial perspectives on transport systems”) took place with three excellent presentations and lots of discussion. The session was opened with a session keynote by Harvey Miller, who provided an overview of the role of GIS in transport (research). Referring to his article from 2015, Harvey talked about the fast changing environment of our discipline (presentations slides are available here):
Data availability and computational power have been increasing constantly over the last years.
Despite the predicted abolition of space through the Internet, progressive urbanization is changing the human sphere radically (urban metabolism).
The success of the smart phone, which is constantly connected to the Internet, facilitates new applications, methods for data capturing and business models; most of them are location-based.
The other two contributions to the session were rather technical: Mario Dolancic, the winner of this year’s student paper award, presented an approach for lane detection from floating car data. Mario’s motivation for his work, which is part of the LaneS project, was humorous, “I’m a student and don’t have the money. But I want a realistic road graph.”
Anita Graser provided insights in current algorithms for realistic pedestrian routing across open spaces and presented an efficient approach for OpenStreetMap data (for more information visit the PERRON project website).
Thursday was like a roller coaster ride. The day started and closed with sessions on authoritative spatial (transport) data. I had never expected to attend a GIP forum where the majority of contributions discussed how authoritative data can be made available to the public. The digital road graph can be completely downloaded via data.gv.at. This rich dataset can be nicely coupled with national address data that were made available just recently. In the afternoon OGD strategies on various administrative levels were discussed in a GeoTalk (the presentation slides have been made available on the organization’s website, scroll to GeoTalk #10), organized by the local GIS cluster.
Wasting time on congested roads. Is this all autonomous driving has to offer?
In between these sessions I attended a special forum on autonomous driving. Although some of the contributions where innovative and relevant (for instance Benno Bock’s presentation on car sharing patterns), the forum was dominated by automotive lobbyists who demonstrated a very narrow perspective on mobility. It was a bit frustrating to see how much money is put into R&D with an exclusive focus on the car. There is little effort to completely re-think mobility as a system. Here is just one example: Graham Smathurst from VDA was asked how to understand BMW’s slogan “Freude am Fahren” (pleasure in driving) in times of autonomous vehicles. His answer spoke volumes: On Monday when he drives to work and roads are congested (!) he prefers the autonomous mode, while on a sunny Sunday afternoon he enjoys to drive himself. There was not a single trace of rethinking commuting patterns or mobility behavior. Nothing. Similarly, Christian Kleine from Here presented the company’s ambitions and technology, illustrated with a picture of a self driving car in a massive congestion.
The sessions I attended on Friday nicely demonstrated the potential of the spatial perspective and GIS technology in models, applications and participatory planning processes:
Nikolaus Krismer gave a presentation on his PhD project about multimodal isochrone calculation.
Stefan Herbst demonstrated the Mobility Optimizer, a multi-layer information and analysis tool for evidence-based (planning) decisions.
In the very last presentation of the conference, Dennis Groß presented his thesis where he combined bio-physical sensor data with locations and produced maps of increased stress for cyclists.
What all these contributions have in common, is the added value of an explicit consideration of spatial information. And because transport systems and mobility are spatial by their very nature, geography has a lot to contribute to a better understanding of these complex and dynamic systems. This is why we will definitely organize another GIS-T session for the GI-Forum conference next year. It would be great if you could consider this in your publication and dissemination plans for 2017 (the CfP will be published in December 2016).
P.S.: all papers are published as open access: AGIT, GI-Forum
I had the pleasure to spend two days in Austria’s “far east” (map), attending this years’s national bicycle summit (“Radgipfel 2016”). For the sake of ease, I’ve curated my personal summary with Storify:
The last three days I spent in Bern (Switzerland), attending this year’s GEOSummit. Primarily I represented UNIGIS at our Swiss cooperation partner’s booth (HSR). Wearing a second hat, I also had the opportunity to contribute with a presentation on OGD and innovation (similar to this one) to the “Smart Cities” track, organized by former UNIGIS student Yves Maurer. It were quite busy days, but as always, I really enjoyed being in Switzerland!
The GEOSummit is the prime GIS conference in Switzerland bringing together professional associations, companies, academic institutions and GIS experts from various domains. Parallel to the conference, companies showcase their latest products and services in the exhibition hall.
There, at least two trends (I don’t regard UAVs as a trend anymore) could have been identified which will have an impact on how we communicate geospatial information:
3D printers seemed to be everywhere. These formerly huge machines have evolved to office-sized gadgets, facilitating the production of endless variations of physical models. Of course, my favourites were small topographic models with an enormous level of detail.
It’s not only about augmented reality, but also about augmented maps and models. These applications are intriguing examples of how the virtual and the physical space can be fused. I can imagine numerous application examples, especially in the context of awareness building and participatory planning approaches. Two exhibits were especially fascinating.
Students from the University of Applied Sciences Nordwestschweiz (FHNW) developed an augmented map, which allows to overlay a printed map with real-time information like cloud cover and weather information or the position of airplanes. Of course the programing in the back is sophisticated and should be greatly acknowledged. However, the value of this augmented map application lies in its ability to “spatialize” the thinking of recipients.
Based in Luzern, GeoLabor projects dynamic information, such as agent based simulations, on a highly accurate 3D city model. This is not only nice to watch, but a valuable setting for detecting and discussing spatial and spatio-temporal relations of urban phenomena.
Not a trend but a nice prolongation was the high demand for postgraduate, on the job GIS training, as it is offered by UNIGIS. Consequently I had lots of conversations with domain experts who all aim to extend their portfolio with GIS competence. At several occasions I was supported by enthusiastic current students or alumni who shared their insider experience – I bet, this was by far the best marketing feature.
Today I had a presentation in the “Smart Cities” morning session on how OGD can trigger an innovation cycle with a manifold win situation. Based on the experiences we have made in the bicycle routing projects I could show how the potential of authoritative data can be leveraged when data are made available: additional value can be generated by academic institutions (through models and analyses that turn data into information) and private companies (who turn generated knowledge into market-ready applications), who are then able to provide relevant tools and services to end users. Of course, I’d be more than happy to see much more of such examples, be it in Austria, in Switzerland or elsewhere!
After the conference is before the conference and thus, I’m looking forward to the triple-conference, with FOSSGIS, AGIT and GI-Forum, taking place in Salzburg. There will be plenty of mobility-related sessions with my personal highlight on Wednesday afternoon … See you there!
I’m pleased to announce that our paper “GIS and Transport Modeling – Strengthening the Spatial Perspective” has been published in the International Journal of Geo-Information (Open Access). This paper is the result of a very fruitful collaboration between researchers from four different institutions: Gudrun, Eva and myself from the Department of Geoinformatics, Z_GIS (University of Salzburg), Rita from the Institute of Transport Research (German Aerospace Center, DLR), Anita from the Mobility Department (Austrian Institute of Technology, AIT) and Johannes from the Institute of Geodesy (Graz University of Technology).
The initial idea for this review and outlook paper stems from last year’s GI-Forum conference, where we gathered to discuss the implications of space for transport models. This paper is the attempt to structure and summarize the discussion and to embed it into the current body of literature. Although we touch other dimensions – such as time or human behavior – in this paper as well, the focus clearly lies on the role of space and the effect of a more explicit consideration in transport models. Consequently we regard GIS as integrated platform for all stages of transport modeling:
Abstract: The movement and transport of people and goods is spatial by its very nature. Thus, geospatial fundamentals of transport systems need to be adequately considered in transport models. Until recently, this was not always the case. Instead, transport research and geography evolved widely independently in domain silos. However, driven by recent conceptual, methodological and technical developments, the need for an integrated approach is obvious. This paper attempts to outline the potential of Geographical Information Systems (GIS) for transport modeling. We identify three fields of transport modeling where the spatial perspective can significantly contribute to a more efficient modeling process and more reliable model results, namely, geospatial data, disaggregated transport models and the role of geo-visualization. For these three fields, available findings from various domains are compiled, before open aspects are formulated as research directions, with exemplary research questions. The overall aim of this paper is to strengthen the spatial perspective in transport modeling and to call for a further integration of GIS in the domain of transport modeling. more …
Capabilities of a GIS as integrated environment for transport modeling
The paper itself is the last step of a very creative and iterative writing process. Different to previous publications, this was the first time for me to write a joint paper with so many co-authors, each with a different background and a specific perspective. However, I think the result shows that the effort for finding a common language and for the coordination of all inputs and ideas was worth it. Here are the lessons I’ve learned in these respects:
Multi-disciplinary collaboration and paper writing means more work but better results. The reason for this is simple: in the process of finding a common language everyone needs to shape his or her argument more precisely. Additional multiple perspectives on one and the same topic enrich the common picture.
The spatial dimension has a strong integrative power. It allows overlaying and relating various domain-specific approaches and thus, not only serves as common denominator in the literal, geographical sense, but also in the “domain-space”.
Good communication is the starting point of any collaboration and an absolute necessity throughout the paper writing process. We all came together, more or less by chance, at a conference where we had the opportunity to exchange ideas and discuss future research paths. This “real-world communication” has been subsequently transferred to the virtual space, using various collaboration and communication tools.
Shared enthusiasm fuels and accelerates a fruitful collaboration. This paper was entirely written outside any project context. It’s the result of unpaid work of dedicated (young) researchers. However it was a stimulating experience to (better) get to know each other and by the way, it is an excellent starting point for further – hopefully funded – joint research activities.
It is fun to work with fellow researchers who are all dedicated to a common goal: strengthening the spatial perspective.
The number of location-based apps with tracking function is constantly growing. In conjunction with smart wearables, social media and a prevalent fitness boom a huge amount of digital, geospatial traces is generated.
Regarding tracks from bicycling, data from Strava are probably the most extensive one. Nevertheless, for several regions – especially where the number of Strava users/contributors is rather low – the data are biased towards leisure traffic. In the case of Salzburg, the route to the top of ‘Gaisberg’, for instance, is significantly over-represented. No wonder – it’s one of the most popular sportive routes.
An alternative source of bicycle tracks comes from Bike Citizens’ routing app. This app, which is fueld by OpenStreetMap data, is primarily intended to support utilitarian bicyclists. Thus, the recorded tracks better represent the overall bicycle traffic, especially in cities. Bike Citizens use the tracks, amongst others, to produce incredible beautiful heatmaps. However, until now, these maps are only visual overlays and not ready for further spatial analysis.
The latter point is exactly where GIS comes in. A bunch of questions in the context of bicycle research and planning could be answered when bicycle flows in cities are (1) known and (2) related to other data layers.
Several, very well established map matching algorithms, which reference GPS tracks to a digital road network, already exist. Quddus et al. (2007) provide a comprehensive overview. Most of these algorithms are mainly designed for GPS tracks from cars (as a side note: in this year’s GI-Forum transport session, Mario will present a kind of reverse map matching algorithm, where a detailed network graph is constructed from FCD GPS tracks). Because most of these map matching algorithms aim (of course for good reasons!) for most optimal solutions, they can become quite sophisticated.
However, for most of our questions we are rather interested in “good guesses” about collective bicycle flows. This is why we tried to develop a prototype of a simple map matching algorithm that requires as little additional data as possible and still produces reasonable results. As a test sample we used nearly 2,000 GPX trajectories, which were provided by Bike Citizens (thanks a lot!).
As a reference network graph we used authoritative data, which are available as Open Government Data (OGD). Additionally we set up a routing engine, which calculates shortest paths. The principle idea of the map matching algorithm is the following:
For performance reasons we restrict the whole analysis to the immediate surrounding of a track.
Accordingly, the network is reduced to a minimum.
In order to represent the areal characteristic of the road (which is abstracted to a line in common network graphs) a buffer around the road center line is created.
Buffers are calculated around the GPS track’s vertices (waypoints) in order to compensate position errors.
The buffered road network and the buffered vertices are overlayed.
Track vertices which can be unambiguously assigned to an edge are selected. Conversely, vertices around intersections, which could be assigned to more than one edge are excluded.
The selected vertices are snapped to the respective edge.
In order to interpolate between the assigned vertices, they are fed into a routing engine as stops.
After the route is calculated, it can be matched to the reference network.
Although this approach is naïve in some respects, it generates acceptable solutions for an estimation of collective bicycle flows (click on the image to enlarge it).
With the GPS track matched to the reference network, several analysis can be done: the number of bicyclists traversing a segment can serve as population for risk analysis, it can be used to calibrate and validate simulations (such as Wallentin & Loidl 2015), the flows can be related to infrastructure data or route preferences could be derived and fed to route choice models etc.
Nevertheless, the algorithm has limitations, which should be mentioned as well. I just want to focus on two issues:
1. Roads and shortcuts might not be represented in the reference graph. In this case, the presented, naïve approach fails.
2. In cases of highly distorted GPS signals (biased GPS tracks) and a dense road network, the map-matching algorithms might produce false positives. In the example on the left side, the tracks were assigned to an edge which was actually not traversed.
Both sources of errors – and there are some more – need to be considered whenever the map matched data are used for subsequent purposes.
However, for a first “good guess” of the spatial distribution of bicycle traffic in a city, the map-matching algorithm produces adequate results. In contrast to a simple visual overlay of track bundles (as it is done for Strava’s and Bike Citizens’ heatmaps), map-matched data are an enormously helpful data source for geospatial analysis. Besides potential pitfalls that arise during the map-matching, the suitability of the track data as such needs to be critically reflected. As I’ve shown, popular data sources, such as Strava, are heavily biased towards leisure traffic. In the near future a lot more data sources, similar to floating car data, might be available from utilitarian traffic (from apps such as Bike Citizens’). In order to exploit this (future) data wealth, map matching the raw data definitely is the initial step to take.
Last year’s GI-Forum special session on “Spatial Perspective on Transportation Modelling” (read a brief review here) was a kind of trial balloon, as we weren’t able to foresee the demand for transdisciplinary exchange at the interface of GIScience and transport research in the context of GI-Forum.
Traditionally, GIScientist gather at GIS conferences and transport researchers at transport conferences. Actually there is not as much overlap between the two domains as there could be – think of groundbreaking contributions from geographers (Hägerstrand) to transport research and vice versa. Maybe this is the reason for why I enjoyed the session and all the successive conversations so much. Actually, several participants from this special session worked hard to condense the contributions and discussions into a review/position paper which will be (hopefully!) published soon – the manuscript is currently under review; this is why I’ll provide more information on a later occasion.
In succession of last year’s premiere we are going to organize a GI-Forum special session dedicated to GIS and transport again. Both keynote speakers (Harvey Miller and Anita Graser), from GI-Forum and AGIT (German language twin conference) will contribute to the special session “Spatial Perspectives on Transport Systems” on Wednesday afternoon (July 6th, 5pm)! Here is what is planned for the session:
1. Harvey Miller (Ohio State University): Geographic Information Systems for Transportation in the 21st Century
The session will be opened with a session keynote by Harvey Miller, who currently holds the Reusche Chair in Geographic Information Science at Ohio State University. Harvey will provide a comprehensive overview of GIScience in transport research, similar to his latest paper on the topic (Miller & Shaw 2015).
2. Johannes Schwer (University of Augsburg): Spatial Decision Support: Small-Scale Site Selection Model for Carsharing Services Johannes, who is currently writing his dissertation at the University of Augsburg, will present a spatial decision support system for the selection of car sharing pods. In his analysis he combined demand and supply parameters, such as public transit connections, central facilities, population distribution, socio-demographic and behaviour criteria.
3. Mario Dolancic (University of Salzburg): Automatic lane level road network graph generation from Floating Car Data
Mario is on his last mile of his Master’s studies (Applied Geoinformatics, University of Salzburg) and works for an innovative traffic consulting company in Salzburg. He will present an approach that derives lane center lines from GNSS trajectories using KDE and distance relations. With this method, very detailed road graphs can be generated, which are a prerequist for ITS-applications and autonomous driving.
4. Anita Graser (AIT Austrian Institute of Technology): Integrating Open Spaces Into OpenStreetMap Routing Graphs for Realistic Crossing Behavior in Pedestrian Navigation Anita will start and finish this conference day. After her AGIT keynote in the morning (“Offen und dynamisch – OpenSource, OpenData & OpenScience”), she will give a presentation on two more Open* aspects. Anita is going to provide a brief review of common algorithms for dealing with open spaces in routing and navigation applications, before she introduces a visibility graph approach, which is capable to model realistic routing behaviour based on OpenStreetMap data.
Of course, there will plenty of time for discussion during the session and for further exchange and networking afterwards. As this special session is the last one on this conference day, we will have the chance to smoothly fade into the AGIT Expo Night with snacks and beverages.
If you are not registered for the conference yet, early bird rates are available until May 25th. By the way, this special session is only one highlight for those who are interested in GIS and transport/mobility research (for instance a whole-day track on autonomous vehicles is scheduled for Thursday).
If you won’t make it to the conference, have a look at the conference’s social media channels to stay updated or follow me on Twitter. Research papers of the conference will be published in GI-Forum Journal for GIScience (open access) – you will find Johannes’, Mario’s and Anita’s contribution there.
Since today our paper on spatial patterns and temporal dynamics of urban bicycle crashes is available online. It’s published in the Journal of Transport Geography and can be accessed via the publisher’s website.
The main argument of the paper is that spatial patterns and temporal dynamics on the city-scale level are relevant for any further analysis. An explorative analysis prior to an explanatory approach helps to focus on relevant regions and time intervals. Thus the demand for exposure data and the extent of further in-depth investigations can be cut down tremendously. In order to facilitate the environment for detecting patterns and dynamics we designed a multi-staged workflow with the delineation of spatial reference units and a measure of relative difference between regions and time intervals at its core.
Here are three aspects of the study, which might be of interest in connection with the paper:
1) Data
For our study we used authoritative data from the city administration. This crash data base is fed by police reports and cleaned and managed by the responsible department. Although the quality of the data and the level of detail is comparable high, the data suffer from severe underreporting. It is known from studies (e.g. Schepers et al. (2014)) that the share of singe-bike-crashes is much higher than reflected in common crash statistics. Additionally there is a relation between probability of reporting and crash and the severity of material or human damage, according to a recent OECD report. Consequently the conclusions drawn from any study based on incomplete data need to be handled with great care. Of course, the same holds true for this study.
2) Exposure data and risk calculation
It was quite a challenge to provide sufficient and unambiguous arguments for our idea to focus exclusively on absolute numbers of crashes. We did not aim to provide risk estimations but to develop a workflow for the detection and description of spatial and temporal variabilities of crash frequencies. Apart from conceptual reasons for this strategy, adequate exposure data were simply not available at the time of this research. Meanwhile we’ve developed first prototypes of bicycle flow models that cover a whole city on the finest scale (see Wallentin & Loidl (2015)) and will publish first results of risk calculations on the local scale soon.
3) Limitations
Results of this study suffer from some limitations, which can be also regarded as issues on the research agenda. From a methodologically point of view, I’d say that the yet unsolved modifiable areal unit problem (MAUP) is the most serious limitation of this and every other study that relies on fixed spatial reference units. The main argument for not defining reference units data-driven, e.g. through cluster detection analysis or kernel density estimation (KDE), is comparability over time. It would be impossible to grasp temporal effects, such as the seasonality described in the paper, without fixed spatial reference units.
In the meantime we’ve made significant steps forward to tackle aspect 2) and 3). Apart from methodological progress, I hope that our research contributes to a better understanding of where and when crashes occur. As far as I can oversee the literature on bicycle crash analysis, there is a large body of literature about causal factors (one of the most extensive study in this respect is Teschke et al. (2012)). However, the spatial character of bicycle crashes is often neglected or only regarded as bias for statistics. As bicycling takes place in physical space and thus is a spatial phenomenon by its very nature,I’m convinced that spatial (and of course temporal) analysis can be a small, but yet important piece of information that contributes to the whole mosaic of better understanding bicycle safety. Based on this evidence, adequate, targeted measures can be taken in order to improve the situation for vulnerable road users.
This is only a quick note on a recent observation I’ve made while using bicycle routing portals on the web. However, the relevance of data quality and implemented model routines becomes obvious very nicely. And because I’ve been struggling with these issues for quite a while now and things don’t necessarily turn to the better, I’m curious about your ideas on the following examples.
Imagine an absolutely normal situation in your daily mobility routines. You are at location A and you need to go to location B. Because you are a good guy, you choose the bicycle as your preferred mode of transport. What do you do? Of course you consult a routing service on the web, either via your desktop browser or mobile app.
But which service do you trust, which recommendations are reliable and relevant to you? Give it a try.
For many people the big elephant Google Maps is their first choice . Whether you like it or not, Google has made a big leap forward with their bicycle routing service.
Because you love OpenstreetMap and the GIScience group at Heidelberg University did a great job, you try the bicycle version of OpenRouteService. What you get is what you know from Google.
If you consult another routing portal that is based on OSM data, you might get surprised. Naviki suggests the following route:
So far we’ve tried a commercial service and two platforms which are fueled by crowd-sourced, open data. Let’s turn to authoritative data now. The goal of the federal routing service VAO is primarily the provision of a multi-modal routing service, with a focus on public transport. The bicycle version gives you this recommendation:
The bicycle routing portal for the city and federal state of Salzburg, Radlkarte.info, is designed for the specific needs of utilitarian bicyclists. The data base is identical to the VAO service, but the result differs significantly.
The intention of this blog post is not to assess the quality (validity, reliability, relevance) of the routing recommendations as such. What I want to point to is the fact, that three different service, with different data sources in the back result in exactly the same routing recommendation, whereas services that are built upon the same data result in significantly different suggestions. That’s really mysterious. And it tells me, that the data and data quality is only one side of the medal. Obviously the parametrization of the routing engine and implemented model routines have a huge impact on the result. By the way, for all five examples, I’ve used the default settings.
Following the argument of the impact of parametrization and modelling, one can conclude that it is not so much about the data (they seem to be of adequate quality in all three cases), but about how well you know the user’s specific needs and preferences and turn this knowledge in appropriate models and services. Thus the next consequent step is to offer users the possibility to influence the parametrization of the routing engine in order to get what he or she expects to get: routing recommendations that perfectly fit their preferences.
Do you know routing services on the web that a allow for a maximum personalization (not only pre-defined categories)? To which degree would users benefit from personalized routing? And finally, would bicyclists use it at all? Let me know what you think and share your ideas!
Last year at the end of November I’ve published a call for contributions to a special session on transport modelling at the GI-Forum conference. Back then, we were not sure about the resonance and to be honest, my expectations were not too high. All the more I was surprised by the quality of contributions (the papers are published as Open Access) and the number of participants in the special session.
Because of this year’s positive experiences and feedbacks, we are going to push the topic further and call for contributions for next year’s conference:
Transport systems are spatial by their very nature. They rely on physical infrastructure, they connect locations and they facilitate the mobility of goods and people. However, the spatial dimension is not always considered explicitly in research and application. We think that concepts and tools rooting in geography and GIS have a lot to offer to better understand transport systems at various spatial and temporal scale levels and to foster holistic approaches! At the GI-Forum conference 2015 a number of emerging fields of research was identified (see here for some details). For the 2016 conference we aim to carry these aspects forward and thus invite researchers and practitioners to elaborate on the following or related topics:
Data Requirements, characteristics, quality, availability and accessibility of (spatial) data for research and application in the field of transport systems.
Models Conceptual approaches and paradigms of transport modelling, spatial and temporal levels of scale and aggregation, validation frameworks and methods.
Visualizations The role and design of (geo-) visualizations for the exploration, interaction and communication of transport system data, models and analysis results.
Contributions can either be submitted as full paper, extended abstract or poster. Any contribution needs to be submitted via the conference submission website and will be object to the double-blind, peer-review process. Authors of accepted full papers are going to be invited to present and discuss their paper (15’+5’) in the special session. Authors of extended abstracts and posters are going to be invited for an elevator pitch (5’). Full papers and extended abstracts will be published in the GI-Forum journal (Open Access).
We are really looking forward to your contributions and to the session opening. Prof. Harvey Miller (Ohio State University) will provide a broad overview of the intersection and relation of transport research and GIScience in his session keynote.
If you have any questions or remarks, feel free to get in contact! All information concerning the conference can be found on the website.
Returning from Brussels, I’m sitting in the train for 8 hours now and because the ICE is delayed since Stuttgart and I’m going to miss my connection train in Munich, I’ll have another 3 hours* until arriving in Salzburg. I’ve spend most of my “train-time” wrestling with a research paper which I need to rework for resubmission. Now it’s time to do something else. For example reflecting my 2 days at this year’s POLIS conference.
First things first: the conference was an awesome event at a very, very cool location. The conference organization was perfect. The same holds true for the opportunities to exchange, both face to face and in the Twitter sphere. The mix of participants from city authorities, researchers and practitioners resulted in a stimulating atmosphere with lots of inspirations, information and examples to learn from.
The overall topic of the conference was “Innovation in Transport for Sustainable Cities and Regions”. However, I’d say the conference (or to be fair, the sessions I’ve attended) was very much about how better data could help to better understand the complex phenomenon of urban mobility and how these insights lead to better services (not only apps!) for citizens. Right from the first session on the data topic was omnipresent: Dovile Adminaite from the ETSC pointed to the fact that risk calculations for vulnerable road users (VRU) are still hard to do because of the absence of sound exposure data. Well, this is a topic we are working on for quite a while. And as I’ve learned today, a recently started H2020 project, FLOW, deals exactly with this issue.
In a very insightful workshop session, chaired by Yannis George from the Technical University of Athens, the data issue was at the center again. Alexandre Santacreu from TfL nicely showed how crucial the choice of exposure variables is for the interpretation of bicycle accidents. He came to the conclusion that only the distance travelled allows for sound risk calculations; inhabitants are crap, number of trips is tricky. Apart from the exposure variable Alexandre elaborated on how the level of spatial aggregation decides on the emerging risk patterns. My personal highlight in his presentation was the hexgrid map with disaggregated risk calculations for London – they reminded me of my own maps which I’ve recently presented in Hannover at the ICSC. The following presentation by Eric de Kievit (City of Amsterdam) also had a lot in common with what we have been doing for more than five years now. He presented a Safety Performance Indicator (SPI) which is used for the assessment of road networks. As Eric said, such modelling approaches are especially valuable when the data situation (accidents, exposure variables) is suboptimal. In turn – and we spend some time discussing this issue – it is hard to validate models and calculations in the absence of sound data. Véronique Feypell from the International Transport Forum finally presented the IRTAD database. Under the umbrella of the OECD data portal safety-relevant data are collected in a standardized way and subsequently harmonized. I’m looking forward to the updated and improved data resource!
What would be a conference these days without discussing smart cities? Actually this was the case in the opening plenary session. Commissioner Jyrki Katainen mentioned the special role of cities as driving forces for growth and innovation. This is exactly where Commissioner Katainen linked smart cities to smart citizens who are engaged in life-long learning (to be honest, I’ve never connected UNIGIS with smart cities, but maybe we should think about it …). After the welcome addresses a panel dealt with several aspects and connotations of smart cities. A recurring statement was that the wheel should not be invented multiple times and that we don’t need more technology and more research, but island solutions must be fused in order to generate values. Well, I clearly see the argument, but I think we need much more research! Maybe not necessarily on technology, but definitely on the social and ethical implications of the digitalization of the human sphere!
The last session of the first conference day was dedicated to data as an asset. It was opened by a brilliant contribution from Madrid. Sergio Fernandez shared EMT’s (Madrid’s PT operator) experiences with a radical open data approach. They publish all generated data as open data and currently witness how these data fuel a punch of newly developed, cool applications. The value generated by publishing data as Open (Government) Data was the take home message of my presentation which I gave in this session. In case you are interested, here are my slides:
The second conference started with a fireworks of best practice examples at the interface of ICT and active mobility. I got especially excited by the Beat my Street project from London, which is tightly connected to the Switch Project. The idea behind the project is rather simple, but the impact is huge. What I take home as key for a successful implementation is the move from a pure public health project (although this is exactly what it is) to a participatory, integrated community project, with fun and not health as the main promotion argument.
This project from London maybe illustrates best what became evident throughout the conference: cities and regions do have the capacity to make cities livable places and they are the driving forces for societal and technological transformations towards sustainability. But they need to have visions and the organizational and financial environment that stimulates the big leaps forward.
On a personal level, I’ve learned that several ideas we’ve been working on would perfectly correspond to past or currently running projects. Thus I can only say that I’d be more than happy if we could participate and contribute in the future. Please, don’t hesitate to use the contact form, get connected on Twitter or simply have a look at our department’s website.
In two weeks from today this year’s POLIS conference will take place in Brussels. The conference topic is “Transport innovation for sustainable cities and regions” and I’ll have the opportunity to present some conclusions from our long-term involvement in the Radlkarte project(s).
In my talk I’m going to show how open minds and open data together with a fruitful cooperation across domains and institutions lead to significant innovations. My demo example is the history of the bicycle routing portal Radlkarte, which is currently available for the federal state of Salzburg and four adjacent municipalities in Bavaria.
First version of the Radlkarte bicycle routing.
When the first version of the portal was launched in 2012, one had to fear to cycle off the edge when leaving the city of Salzburg. The routing portal was exclusively fed with data from the city administration which naturally covered only the administrative area of Salzburg.
This situation has changed dramatically since the city administration started to adopt the national standard for road-related data (GIP). Moreover, the federal state of Salzburg – now hosting the harmonized road data set – has started to publish a wide variety of data as Open Government Data (OGD), among them all road-related data. This development has facilitated a much larger coverage of the bicycle routing portal and fast update and innovation intervals. We are currently in the 4th year of continuously improving the Radlkarte. The success formula behind this developement is: Openness + Cooperation = Innovation.
These are the lessons we have learned so far and I’m going to talk about in my presentation:
Enthusiasm for a common topic – in this case bicycle promotion – is the most efficient facilitator for cooperation across departments, administrative bodies and domains.
Long-standing collaborations and an open communication atmosphere are driving forces for change.
Worthwhile applications can act as trigger for data standardization, harmonization and quality improvement. Seeing the immediate impact of data motivates to invest in standardization, harmonization and quality improvement. This effect has also become obvious in the context of the Basemap project.
Publishing data as OGD fosters efficient know-how generation and application development. Costs for data and the cumbersome negotiations and handling of license agreements become obsolete which frees resources for prolific things.
OGD can stimulate value creation in all of the following domains: researchers benefit from the availability of rich data sets, which help them to develop new models and analysis routines. Transferring this know-how to the private sector, allows companies to develop tools and applications without additional costs for data. Finally public authorities can use these products in order to reach specific planning goals.
Although there is still plenty of space for improvement, the Radlkarte case nicely shows several aspects of openness (from open minds to open data) and cooperation (from different departments within administration to academia to the private sector) and how these ingredients contribute to innovative information provision for bicyclists.
I’ll provide my presentation slides along with a conference report later. Nevertheless I’m more than happy receiving your questions and remarks right now; just use the comment function or the contact form.
Today and tomorrow the “4th International Cycling Safety Congress” (ICSC) takes place in Hannover, Germany. This is an excellent opportunity to learn from and connect to experts from various domains. The common denominator is, as the conference name indicates, bicycle safety, but the approaches presented so far are rather diverse.
Today’s presentations ranged from medicine (which body parts are most often injured in different types of crashes), to legal aspects (single- or bidirectional cycle paths), to hardcore technology (automatic analysis algorithms for videos from naturalistic studies) to planning (optimal road design, especially at intersections) and to social and psychological aspects (by which values and attitudes are ‘cyclists’ driven).
What was largely missing so far was the spatial perspective on bicycle safety. Hence I’m looking forward to add a nice, little piece to the multi-disciplinary mosaic tomorrow.
My argumentation starts from the fact, that bicycle accidents are spatial by their very nature. They don’t occur in the nowhere, but in geographical space. Thus they can be analyzed in Geographical Information Systems (GIS). Through geospatial mapping and analysis geospatial patterns and dynamics become obvious, as the figure below demonstrates.
Spatial dynamics of bicycle accidents in the city of Salzburg between 2002 and 2011.
From bicycle crash frequency to risk estimation.
Although the spatial patterns and dynamics that emerge from simply putting the crash locations on a map are interesting and relevant for multiple application contexts, the estimation of risk is even more helpful. The problem here is, that exposure variables are hardly ever available beyond the city scale.
In order to account for the variation of crash occurrences and the corresponding risk within the city we made use of the recently developed (and published) bicycle flow model for the city region of Salzburg. Relating the city’s reported crash locations to the simulated bicycle traffic volume results in an estimation of risk at the smallest possible scale.
Besides the analysis of (historical) crash data, Geographical Information Systems are also capable to model and simulate potential safety threats. For my presentation I’ve selected three use cases in which such a geospatial model (Loidl & Zagel, 2014) is employed in decision support and planning tools:
The first use case is about the quality of accessibility of central facilities, such as university buildings. The introduced assessment model allows for an evaluation of the immediate environment of the respective facility. Based on this status-quo analysis, existing corridors and barriers as well as potential connections can be identified.
In the second use case, the assessment model is fed into an interactive, simulation environment (see Wendel, 2015 for further details). Through a web interface the effect of infrastructural or legal measures on the overall safety index can be tested very intuitively.
As a third prototypical field of application, besides status-quo analysis and simulation, the assessment model is employed in a bicycle routing service. Operated by the city and the federal state of Salzburg, the web portal radlkarte.info recommends the optimal (safest, most comfortable) route for utilitarian bicyclists.
What all examples, shown in the presentation, have in common is that they make use of the spatial characteristics of bicycle crashes and the transport network respectively. Geographical Information Systems can thus be considered as well-performing integration platform for multiple perspectives on the complex system of bicycle safety and as facilitator for innovative planning and information tools.
Have you got any comments, ideas or critical remarks? Feel free to write a comment, use the contact form or simply get connected on Twitter.
The number of available data sets published as Open Data (OD) and Open Government Data (OGD) is constantly growing. That’s incredibly cool, because you can do analyses that were impossible a few years ago. Today I’d like to show you how you can use building footprints from OpenStreetMap and census data from an OGD portal to generate a population grid with any spatial resolution.
Here is the reason for why it’s worth to go through a few analysis steps instead of using what’s available anyway. At least in Austria (I know, the situation is quite different in the US) nationwide census data are only freely available on the level of municipalities. Now, everyone is aware of the fact that the population is commonly not equally distributed within rather arbitrarily defined administrative units; especially in the case of large, rural municipalities. Instead, the population is more or less spatially clustered.
For many analyses population data on the level of municipalities are way to coarse. Take for example the calculation of service areas for central facilities in order to estimate the potential coverage (“How many people live within 5 driving minutes?” etc.). Until recently you were forced to buy expensive statistical data from the federal bureau of statistics, Statistik Austria, in order to answer such questions. What you get there are aggregated census data in 250, 500 or 1000 meter raster grids.
Fortunately, enough data are published today as OD and OGD to bypass this limitation. Of course, the resulting population raster from the approach presented below, is only an approximation (similar to dasymetric maps). But for a first estimation it’s enough and it is for free!
Basic idea behind the disaggregation approach: population data are distributed proportionally to the location and size of the building footprints.
Here is how you can generate disaggregated population grids based on OSM data and demographic OGD:
Disaggregated census data for the city of Braunau (Upper Austria).
Download administrative boundaries, including available census data. For Austria you’ll find everything via the national OGD portal.
Download building footprints from OpenStreetMap. I prefer QGIS and the QuickOSM plugin for this task, because OSM data are immediately converted to a geospatial dataset (e.g. Shapefile).
Transfer all datasets into a projected coordinate system; the calculation of areas is more convenient this way.
Select (building = *) all building footprints that are not used for residential purposes and remove them from your analysis layer.
Calculate the share (r) of the total building footprint area for each building:
Select all buildings within the respective administrative unit and multiply the population data with the share of each building.
Assign the estimated population data of the building footprints to each grid cell.
Done. What you have is a rough estimation of the population distribution.
Although the results are fairly reliable, at least two issues negatively affect the result. First, building footprints don’t account for multi-storey buildings. Theoretically the number of storeys can be tagged in OpenStreetMap, but this is hardly ever done. Second, data inaccuracies bias the result. In OSM many buildings are not adequately tagged (e.g. commercial buildings should be tagged as such) and even worse, some buildings are not mapped yet. Nevertheless for many questions the approximation is sufficient.
Building footprints (from OSM) are used for the disaggregation of freely available census data (OGD).
This simple piece of GIS analysis demonstrates the power of GIS on the one hand and the large benefit of Open (Government) Data on the other. Try it yourself – I’m looking forward reading about your experience!
Last week the twin conferences AGIT and GI-Forum took place in Salzburg, Austria. Once again it was a very intensive but stimulating event with great conversations, new contacts, nice social events and of course the everlasting struggle to choose the right session from an extensive offer of attractive parallel tracks. Whereas the general tenor of the keynotes was the increasingly tight relation between GIS and IT, my personal conference focus lay on spatial modelling and analysis in the context of transportation.
Searching the web you’ll find lots of personal reviews (this one by Anita is a great example!) and social media snippets (#AGIT2015#GIForum2015). Nevertheless here is a list of links you might find useful:
My conference week was dominated by the impressions from two keynotes I could attend (unfortunately I missed the other ones due to overlaps in the program) and my involvement in a double-session on transportation modelling (have a look at my recent post), the OpenStreetMap special forum and the track on Austria’s harmonized road graph, GIP.
In Tuesday’s keynote Ingo Simonis from OGC talked about the role of standards in the context of smart cities. His motivation to argue for establishing geospatial intelligence (… and with this standards) in enormously fast growing urban agglomerations is the correlation between size and opportunities/challenges: “The bigger a city, the more of everything is there.” A geospatial framework of connected devices is thereby regarded as part of sustainable solutions that turn these vibrant, urban hot spots into smart cities. As in nearly every presentation on smart cities Songdo in South-Korea served as role model and poster child of Ingo’s argumentation (a reference I personally find not that convincing – but this would be an entirely different discussion on liveable vs. smart cities).
What I found really intriguing was Ingo’s elaborations on the “social” aspect of standards. Until recently standards were more a bone dry threat than anything else to me. But Ingo made a very important notion on that: he illustrated how standards are, as he put it, the distilled wisdom of people with expertise in their respective field. In other words, standards don’t necessarily define in advance how things have to be done, but are recommendations or a framework for activities that are already established … Standards are about a common understanding and language of domain knowledge and practise.
The second keynote on Wednesday morning came right from the opposite spectrum of the handling of large data amounts, or better data stream. Manfred Hauswirth gave an inspiring overview of what is currently going on in the field of linked data and what’s the role of GIS in the never-ending stream of data, semantic relations and interdependencies. He spoke of the internet of everything where the most relevant thing (above all in terms of business models) is to extract useful information from data; something Manfred called a rather untapped resource. Four take home messages made it into my notepad:
Linking is the new (Is it really new? Actually this is how our human brains have worked for millennia) paradigm in the handling of data sets/streams.
Data are increasingly dynamic. This is why the whole processing needs to be designed adaptable.
As geonames are central to the semantic web, geospatial data and knowhow are of great importance.
Privacy is gone. The latter point was of course not revolutionary or new. But it was the first time I heard this statement explicitly and without any dilution in a keynote on a GI conference (probably because the keynote speaker has a background in computer science) – normally we hear bloomy mantras such as: “GIS helps to make the earth a better place.” blablabla. Maybe the organizers of next year’s GI-Forum could invite a philosopher as keynote speaker, talking about the responsibility we have in science and IT!
As the years before, a highlight of the German-speaking conference, the AGIT, was the OpenStreetMap special forum, organized by TraffiCon. This year I had the chance to contribute actively; it was a great honor to got invited for a presentation on the suitability of OSM and OGD data for network modelling and analysis. Here are the slides of my presentation (sorry, German language), which I think are self-explanatory and don’t need any further comments:
Speaking at the OSM special forum the day before, it was a somehow exotic experience to give another presentation in a session dedicated to authoritative road data on Thursday morning. Since 6 years (with several more years with preparatory projects) all administrative bodies in Austria edit and manage their road-related data in the so called Graphenintegrationsplattform, GIP (engl. harmonized road graph). This standard allows for nationwide applications and prevents from cost-intensive data redundancies within administrations.
We’ve been working for quite a while with GIP data in the context of bicycle routing. Currently the web application http://www.radlkarte.info is based on authoritative road data. Over the last two years the quality of the GIP data has been significantly increased. But still, there are some critical issues that become evident when the data are used in an operative environment. This is why we have developed several quality control routines considering above all topology and attributes. The latter is important for (spatial) modelling approaches with which the data are interpreted and fitted into the specific application context. With this parallel approach – quality testing plus modelling – the reliability and robustness of the data could have been significantly increased, as I demonstrated in my presentation:
Any comments and questions? I’m looking forward to read and learn from you!
Transportation modelling is a well established domain with dedicated experts and sophisticated software packages. Still, we thought it could be worth to take a closer look on it from an explicit spatial perspective. This is why Gudrun and I have organized a special session entitled “Spatial perspective on transportation modelling” at this year’s GI-Forum conference (http://gi-forum.org).
We had a session with five short presentations and an extended joint discussion and a workshop session. This very brief summary simply serves as a reminder of some of the major issues that were raised.
The paper session on Wednesday was a real personal highlight. Not only the presentations were inspiring, but the audience was big and active. We had presentations from various fields, covering quite a broad range of topics (all papers are online as open access):
1) Gudrun provided insights into a first version of an agent-based bicycle flow model, where she demonstrated how aggregated flows emerge from the individual behaviour of numerous agents in space and time. One of the major conclusion was that while the model as such seems to generate feasable results, the validation is rather tricky since the necessary data are hardly available.
2) Christoph gave an excellent presentation on how to link the abstract model space with the geographical space and the model steps with a temporal continuum. Additionally he presented his approach to speed up the model performance when it contains routing functionalities. With an intelligent network simplification he was able to run the simulation 12 times faster than with the initial network graph.
3) Somehow connected to the preceding two presentations, Johannes gave an introduction to cognitive agents as counterparts of selfish agents, which are assumed in most routing and navigation applications. With regard to current transportation models, Johannes estimated that those models might be more accurate and thus more meaningful when “smart” agents are incorporated.
4) Leaving the field of agent-based models, Rita answered the question what geographers could contribute to transportation modelling in a very beautiful (literally!) way. Working on the TAPAS traffic model she emphasized the role of visualization for the validation and communication of the model results. Especially the spatial context of a map helps to make sense of what the model calculates and how it actually works.
5) In the last presentation of this session the award winner of the AGEO student award, Daniel Steiner presented parts of his master thesis where he worked with real-time data from public transit. What became very clear in this presentation was, that it is hard to find PT companies that provide real-time data and that it is even harder to use these data in models and analyses because of quality issues.
In a second session, that was organized in a workshop format, three topics that were raised in the presentations and the joint discussion were further worked on:
In the very active small working groups, it quickly turned out that we as geographers do have something to contribute to the domain of transportation modelling and that there is still a lot of work to do!
In the context of data for transportation models these points were – among others – briefly discussed:
There are lots of static data available, mostly following an established standard. Although the number of sensors is skyrocketing they are less likely accessible; at least in many parts of the world. Additionally there are numerous standards for all kinds of sensor data, what makes it cumbersome to integrated data from different source in one and the same model. Beside measured data there are also calculated or estimated data, such as interpolations. For such data hardly any standard exists; most often these data are a kind of black box where you don’t know how they were generated.
The latter factor directly leads to the urgent need of sound metadata for transportation data and derived products. It is of crucial importance to know under which circumstances and for what purpose data were captured. For the interpretation of derived data (e.g. flow volumes) it is necessary to know how they were calculated etc. Without providing such information the reliability of modelling results suffers enormously.
An interesting observation was that whereas most often spatial data are used as inputs for transportation models, the models themselves are non-spatial, meaning that the relation between the model objects is abstract and not geographically defined.
Concerning the scale and aggregation level of data a rather pragmatic rule of thumb emerged: data availability, the availability of tools, processing power and the research question decide on what data are being used.
From the group working on ABM and cognitive agents a rather straight forward research agenda was drafted. The group started from three distinct characteristica of agent-based models: exploration of cause-effect relations, non-intuitive phenomena at system level, local scale. From there, the group identifed three areas of research.
How to shift between scales and model types (top-down vs. bottom-up)?
How does ‘smart’ behaviour of cognitive agents impact traffic flows on a broader scale?
How can the performance issue be dealt with in a reasonable way?
The third group worked on the role of geovisualization and came up with a nice paradigmatic (in the cartography community) conclusion: maps and geovisualizations are not only for communicating (one way) results but they serve as capable interface for the exploration of and interaction with the data and the model. Besides, maps and map-related visualizations put transportation models into an explicit spatial context. Thus the model and the results can be related to the environment what on the one hand can explain results and on the other hand generates new hypothesis for further investigations. At least two issues were regarded as yet unsolved:
How to determine the appropriate trade-off between complexity (information load) and simplicity in geovisualizations?
How to design visualization environments that are flexible and adaptable to facilitate real multi-perspective approaches?
Some of the aspects we were working on are documented on these flipcharts.
Of course there is lot more to work on. And that’s exactly what we are going to do now. If you want to contribute or have comments on the few points raised here, just leave me a note. I’d be more than happy to learn from you and extend the group of geographers and GIS experts that strive to contribute their spatial know how to transportation models. Such an interdisciplinary approach is, from my point of view, especially valuable were established transportation models have fallen short so far and that is in the field of active transport.
The official name of our department at the University of Salzburg reads a bit cumbersome: Interfaculty Department of Geoinformatics. Now, there is an administrative reason for this (for details have look at our website). But by far more important is the philosophy behind the prefix interfaculty. It means that GIS is regarded as cross-sectional tool- and mindset.
If you’re interested in one of the many outcomes of such inter-disciplinary work, you might join one of our workshop sessions (“Spatial perspective on transportation modelling”) at the GI-Forum conference in Salzburg next week. It’s organized by my colleague Gudrun Wallentin and myself. Gudrun is an ecologist by training and an expert in spatial simulation. An ecologist working together with a geographer on transportation issues – can there be any relevant outcome? Well, I’d like to give you an example from my current PhD research …
On several occasions I’ve already pointed to the benefits of a geospatial analysis of bicycle accidents. Knowing where (and of course when) accidents happen is a crucial information for targeted counter measures. As long as the analysis exclusively focus on accident frequency, you are fine with geocoded accident reports (apart from data quality issues and severe underreporting). But when it comes to risk calculation it becomes tricky. Here are two examples how risk calculations are commonly done:
1) Accidents per inhabitants per census district.
This migth be a valid approach if large areas were compared with each other, but on the city level useless results are produced. Have a look at the map of Salzburg below. On the left side the number of accidents per inhabitants is calculated for each census district. High risks are indicated at the periphery although the absolute number of accidents is comparably low. This if of course due to the fact that relatively few people live in this areas (the aerial image of the city gives you a perfect overview) while they are frequently traversed by commuters and leisure bicyclists.
High risks are indicated in sparsely populated areas of the city.
2) Accidents per distance travelled per census district. Yiannakoulias et al. (2012), for example, use this approach. They estimate the total distance travelled from commuting data extracted from the Canadian census. While the presented results look reasonable, they don’t allow for a downscaling to the street level. Apart from this, the data availability is not always that good. Consequently the total distance travelled – independently from the scale of the reference units – is subject to numerous assumptions and estimations.
There are some more approaches which pop up from time to time (recently a reviewer suggested to me to relate bicycle accidents to LULC data …), but in the end we always face the problem that we don’t have a glue how many cyclists are actually on the road. For motorized traffic sophisticated traffic flow models exist. They are based on huge amounts of data from an extensive network of counting stations and on board sensors.
With an equivalent for bicycle traffic sound risk calculations for each road segment and different points of time would be possible. Currently two major drawbacks (at least in most cities) make it impossible to simply transfer MIT models to bicycle traffic: there is no obligation to register bicycles (thus we don’t know the statistical population) and very few counting stations. The latter issue is partly met by VGI data, such as data from the fitness app Strava (see Griffin & Jiao (2015)). But these data are neither representative for the whole traffic (the focus lies on leisure trips) nor for the whole population (the app is used by a non-representative fraction of the bicyclists).
Discussing these issues with Gudrun (over several interfaculty cup of coffee) brought us to the idea to test the applicability of agent-based (ABM) models for simulating bicycle traffic flows in an urban network. Using ABM in the transportation modelling domain is a real “minority program”. There is a very inspiring overview paper by Bazzan & Klügl (2014), but apart from this very few literature actually does exist. To my current knowledge ABM has never been used for the simulation of bicycle traffic flows.
At the above mentioned workshop session at the GI-Forum conference Gudrun and I are going to present the results of our first try (pre-print of the paper). To be honest: I didn’t expect such nice results. While there are several issues which need to be improved, the results definitely push us to further work on this topic. And of course, to use the simulated bicycle traffic flows for risk calculations.
As a result from the ABM we have simulated bicycle flows for every road segment and for every point of time. This allows for a risk calculation (or to be more precise: risk estimation) on the most detailed scale level. For the risk estimation the reported bicycle accidents for the years 2002-2011 from the city of Salzburg (Austria) are used. The bicycle traffic flow (number of trips per segment) is the averaged sum for one year. The following analyses use a regular hexgrid as reference unit. Alternatively the single road segments could have been used.
From bicycle accident frequency to risk estimation.
In the left map the total number of accidents within the 10 years of observation are related to the reference units. Accident hot-spots along the main bicycle corridor along the Salzach river become obvious. Relating the accident occurrences to the total network length (center) offers little additional explanation. Spatial clusters of bicycle accident occurrences emerge along the most frequented roads. Both maps indicate hot-spots in the city center, what could lead to the misinterpretation, that these are dangerous places for bicyclists.
Only from the right map information about dangerous places, that are segments with a high risk, can be deduced. Compared to the other two maps the image flips: the risk along the Salzach river is much lower than in the periphery. Risk hot-spots emerge where the quality of the bicycle infrastructure is comparably low and the MIT volume is high.
From this simple example several conclusions about accident risk for bicyclists can be drawn. But the point I want to make here is to demonstrate how useful ABM is in this context. It helps to gain a rough idea of the spatial and temporal distribution of bicycle flows and it tackles the constant problem of data (and information) shortage. Compared to aggregated statistics ABM allows for analysis on a much more detailed level. Once having risk estimates further analyses and reasoning are possible. For example the correlation between infrastructure and risk can be investigated. Or the question to which degree the number of bicyclists on the road increases (or decreases?) the overall risk can be answered. You see, we see lots of work ahead!
If you have any ideas how the merge of ABM and GIS can be further used in the transportation domain, if you have suggestions for improvements or if you are an expert in any related domain that wants to discuss over some more interfaculty cups of coffee – please feel free to use the comment or contact function! And if you are the GI-Forum conference anyway, join us on Wednesday, 8th July, at 1pm in room 413 (first floor). I’m looking forward to inspiring conversations!
TL;DR
First, an interfaculty department is great – it brings together an ecologist and a geographer to work on transportation modelling.
Second, ABM helps to simulate bicycle traffic flows, which can serve as input for risk estimations.
. See you there!
Data are a recurring topic on this blog and the necessity to consider data with regard to availability, accessibility, quality and suitability is still increasing. This is simply because of the massive amount of mobility-related data that is constantly generated by mobile devices and stationary sensors.
Spatial information matters for almost all aspects of mobility. This holds true, of course, for people and goods. Distance to the next distribution center, travel time delay because of boarder controls, legal regulations for highway sections, topography impacting the range of an electric vehicle etc. All these examples are either explicitly (such as distance) or implicitly (geo-referenced speed limits, for example) spatial.
Modelling bicycle flows at a reasonable scale is complex and not very well established yet. However, knowing when, where how many cyclists are on the road is crucially important for mobility management, transport planning and cycling promotion.
Today, I had the honour to chair another special session that dealt with GIS and mobility research at this year’s 
Another particularity of OpenStreetMap is the data model. Coming from a GIS background I was taught to represent 
The downloaded *.zip file contains three folders: data, static and symbology. The first contains the data in the chosen format. In the static folder all licence files and metadata can be found. The latter is especially valuable, because it contains the entire OSMaxx schema documentation. This excellent piece of work, which is the “brain” of the service is also available on 
This is only a quick note on a recent observation I’ve made while using bicycle routing portals on the web. However, the relevance of data quality and implemented model routines becomes obvious very nicely. And because I’ve been struggling with these issues for quite a while now and things don’t necessarily turn to the better, I’m curious about your ideas on the following examples.
gicycle 