Including Accident Information in Automatic Bicycle Route Planning for Urban Areas
Alex D. Singleton; Daniel J. Lewis (2012). Urban Studies Research, 2011(1). DOI: 10.1155/2011/362817
Abstract
Between 2005 and 2007 there were 9071 traffic accidents involving bicycles within London and this paper demonstrates the utility of Geographic Information Systems as a tool for analysing and visualising these occurrences. Through linkage of these spatial locations to a street network dataset it was possible to create a variety of intelligence about the types of street infrastructure where accidents predominantly occur. Additionally, a network routing algorithm was adapted to account for the frequency of accidents within a series of proposed journeys. This pilot routing application compared the quickest route with an accident avoidance weighted route between a series of origins and destinations. The results demonstrated that the routes avoiding areas of high accident volume did not increase journey length significantly; however they did provide a “safer” route based on empirical evidence over the volume of accident locations.
Extended Summary
This study examines whether Geographic Information Systems (GIS) can be used to create safer bicycle routing applications by incorporating historical accident data into route planning algorithms. The research analysed 9,071 cycling accidents in London between 2005 and 2007, using spatial analysis techniques to map accident locations and link them to the city’s street network infrastructure. The investigation employed the Integrated Transport Network (ITN) dataset to create detailed visualisations of accident hotspots across London’s transport network. Rather than simply mapping accident locations as points, the study linked each incident to specific road segments, enabling more precise identification of dangerous areas. Multiple visualisation methods were tested, including grid-based representations at different scales and network-based mapping that highlighted accident frequencies along individual roads. The analysis revealed that cycling accidents occurred predominantly on single carriageways and A roads, with The Mall and Newington Causeway identified as the most dangerous roads, each recording 20 accidents during the study period. The research developed a pilot routing algorithm using Dijkstra’s shortest path method, modified to incorporate accident frequency data as a weighting factor alongside distance. This safety-optimised routing system was tested against traditional quickest-route algorithms across 1,650,095 origin-destination pairs throughout Greater London. The safety-weighted routes successfully reduced accident exposure in 96.9% of journeys tested, whilst only increasing travel distances by a median of 436 metres for all trips. For more realistic cycling distances of 8 kilometres or less, the median increase was just 111 metres, with 70% of journeys extended by only 100 metres. Two specific case studies demonstrated the system’s effectiveness: routes avoiding the dangerous Elephant and Castle roundabout, and alternative Thames crossings using Lambeth Bridge instead of the accident-prone Vauxhall Bridge. The research highlights the potential for GIS-based tools to improve cycling safety through evidence-based route planning. By providing cyclists with empirically safer alternatives that add minimal journey time, such systems could encourage more people to cycle whilst reducing their exposure to high-risk locations. This work demonstrates how spatial analysis can transform raw accident data into practical safety interventions, offering a technological solution to urban cycling safety challenges that could be implemented in online mapping services.
Key Findings
- Analysis of 9,071 London cycling accidents revealed hotspots predominantly on single carriageways and major A roads, with roundabouts being particularly dangerous locations.
- Safety-optimised routing reduced accident exposure in 96.9% of tested journeys whilst increasing travel distances by only a median of 111 metres for realistic cycling trips.
- The Mall and Newington Causeway were identified as London’s most dangerous cycling roads, each recording 20 accidents between 2005-2007.
- Geographic Information Systems successfully linked accident data to street networks, enabling precise identification of high-risk road segments for route planning applications.
- Pilot routing algorithm demonstrated that cyclists can avoid high-accident areas without significantly increasing journey times, potentially encouraging safer cycling behaviour.
Citation
@article{singleton2012including,
author = {Alex D. Singleton; Daniel J. Lewis},
title = {Including Accident Information in Automatic Bicycle Route Planning for Urban Areas},
journal = {Urban Studies Research},
year = {2012},
volume = {2011(1)},
doi = {10.1155/2011/362817}
}