Bicycle flow model on OpenABM and GitHub

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 internet.
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 internet. 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 internet. You are invited to test the model, improve it or contribute additional features. The code is available on GitHub internet.

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GIS and sustainable mobility

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 internet 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 internet 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 internet.

I’m looking forward very much to welcoming you in Salzburg next july!

Is cycling dangerous?

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 internet).

Alberto Castro internet 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 internet, 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 internet. This week, I’m going to present results from this research at the International Cycling Safety Congress internet 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 internet. 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 internet 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 internet. 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 internet 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 internet, 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.

Data, freight transport and congestion

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 internet. 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 internet 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 internet, 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 internet). 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 internet 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 internet 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 internet. 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 internet 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!

AGIT & GI-Forum Conferences: GIScience and Mobility

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 internet and GI-Forum internet 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 internet 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 internet) 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 internet). 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 internet 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 internet), 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 internet. I’m especially interested in the bicycle routing application, which was co-developed by a former UNIGIS internet student.

Autonomous Driving

Three sessions in a row deal with autonomous driving internet 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 internet is going to be presented and evaluated by experts. The head of the department for sports medicine at Salzburg’s medical university, Josef Niebauer internet, 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 internet on simulation platforms for modeling bicycle flows. There are two sessions on Smart Cities and planning internet and one on Urban Geoinformatics internet. 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 internet, #GIForum2018 internet or #AGIT2018 internet or check the social media wall internet).

Bicycle flow modelling

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 internet is dedicated to develop and evaluate two different modelling paradigms. Our partners at TU Graz internet expanded their intermodal four-step-model and integrated cycling. We at Z_GIS internet were responsible for testing an agent-based approach.

After 18 months of research and development – primarily driven by Dana internet, who is writing her PhD on ABM in transport modelling – we are able to present first results these days. At this year’s GEOSummit internet, 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 internet, 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 internet. A journal paper about the model is on the way.
You see, there is more to come! Stay tuned or get in contact internet with us right away.

We need more data!

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 internet, 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:

  1. 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.
  2. 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 internet for the whole study).
  3. 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 internet 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 internet, 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 internet if you are interested in sharing your ideas, data, questions or examples!