Tagged: cycling safety

Spatial information and bicycling safety

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 internet 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 internet and – even more preferably – in face-to-face communication!

Bicycle routing portals

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 internet) 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.

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 internet).

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 internet. So let’s get started …

Mapping Bicycle Crash Risk Patterns on the Local Scale

safety2016-screenshotEarlier this year we published a very detailed spatial (and temporal) analysis of bicycle crash data from Salzburg (Austria) in Transport Geography internet. 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 internet).

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 internet) 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.

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 internet of the paper (full text internet), which was published in a special issue of the OA journal “Safety” internet:

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 internet or get in touch with me via the contact form.