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!
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%.
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:
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:
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!
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.
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) total population 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 .
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.
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.
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 .
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.
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!