Network-Based Point Pattern Analysis of Bicycle Accidents to Improve Cyclist Safety

Abstract

Examining the spatial distribution of bicycle accidents under different conditions and in different periods is an important issue for increasing cyclist safety. A point pattern analysis methodology of 1,437 bicycle accidents that resulted in injury or death in the city center of Vienna, Austria, between 2012 and 2014 is described. Network-based kernel density estimation was used to examine the hot spots of bicycle accidents, and the network-based nearest-neighbor distance was taken into account to check the significance of the hot spots. Moreover, the global cross nearest-neighbor distance was used to test the effect of urban components on the distribution of bicycle accidents. An understanding of the temporal and conditional differences was obtained by analyzing the accident data in terms of four classifications: all accident data and then the accident data classified according to season, light conditions, and precipitation conditions. It was concluded that the bicycle accident hot spots varied in space according to season, light, and precipitation conditions. Also, these detected hot spots were significant for the pattern of accidents, no matter what classification was used. Besides these points, at the .95 confidence level, bicycle accidents tended to cluster by signalized intersections, bus and tram stations, subway stations, and city bike stations. As a result, a systematic framework was proposed for spatiotemporal analysis of bicycle accidents for the built environment. The framework can serve as a guide to determine effective strategies for cyclist safety in urban areas.

On cycling risk and discomfort: urban safety mapping and bike route recommendations

“…Future work includes the expansion of the risk and discomfort estimation with exposure and vibration measures [17,4], the study and comparison of several other cities [9], the influence of other traffic in cycling safety as well as the design of traffic simulation models for the participatory multi-objective optimization of traffic flows [16,1,12].…”

“…However, there is evidence that bike accidents are rising as well: While cycling traffic has increased by 35% since 2013 in Zürich, reported bike accidents have increased 1 by 60%. Cyclists' accidents are 5 to 6 times higher per travelled kilometer than that of car occupants [9] in Norway. It is predicted that the accident rate of cyclists is almsot 20 times higher than that of car occupants, when unrecorded bike accidents are considered [9].…”

“…Cyclists' accidents are 5 to 6 times higher per travelled kilometer than that of car occupants [9] in Norway. It is predicted that the accident rate of cyclists is almsot 20 times higher than that of car occupants, when unrecorded bike accidents are considered [9]. Almost half million cyclists die every year in traffic accidents [11].…”

“…One of these layers compiles and displays accidents involving bikes between 2011 and 2017. The data are collected by the Swiss Federal Roads Office from electronic police reports 9 . Together with the localization of the accidents, this layer provides an accident specification that includes the date and time, severity, cause, and street type.…”

“…No continues risk estimation is provided and all such route features are treated and counted equally. Spatio-temporal analysis frameworks of bike accidents are earlier introduced based on network-based kernel density estimation [3,9]. For instance, applicability in the city center of Vienna, Austria, reveals that bike accident hot spots vary in space according to season, light, and precipitation conditions, while these hot spots cluster by intersections and bus/tram/subway/bike stations [9].…”