Cyclist–Motorist Crash Patterns in Denmark: A Latent Class Clustering Approach

Abstract

The latent class clustering approach provided a comprehensive and clear map of cyclist-motorist crash patterns. The results are useful for prioritizing and resolving safety issues in urban areas, where there is a significant share of cyclists potentially involved in multiple hazardous situations or where extensive bicycle sharing programs are planned.

A Spatial Analysis of Land Use and Network Effects on Frequency and Severity of Cyclist–Motorist Crashes in the Copenhagen Region

“…Crash reports contained information about characteristics of the crash (e.g., crash type, injury severity, time of day, day of the week, infrastructure characteristics, land use, light conditions, weather conditions), involved vehicles (e.g. make, model, maneuvers prior to the crash, registration date, collision point), and injured persons (e.g., injury severity, demographics, alcohol or drug use, seat belt or helmet use, license status) (for details, see Kaplan and Prato, 2013;Kaplan and Prato, 2014). Crash injury severity reporting of these types of crashes in police records in Denmark as well as other countries (see, e.g., Aertsens et al 2010;Elvik and Mysen 1999;Juhra et al 2012;Veisten et al 2007).…”

Factors contributing to bicycle–motorised vehicle collisions: a systematic literature review

“…Concerning the former, a couple of studies revealed that increasing levels of van, large automobile, and truck traffic were associated with higher collision risks (Ackery, McLellan, & Redelmeier, 2012;Vandenbulcke et al, 2014). Compared to cars, buses and heavy vehicles have more blind spots and cyclists in the blind spot of a huge goods vehicle entailed higher a risk of collisions (McCarthy & Gilbert, 1996), especially when buses and heavy vehicles turned right at intersections (Kaplan & Giacomo Prato, 2013;Vandenbulcke et al, 2014). Larger size of the vehicle contributes to increase the risk of BMV collisions because of a more cumbersome manoeuvrability as well as and increased presence of blind spots.…”

Infrastructure and spatial effects on the frequency of cyclist-motorist collisions in the Copenhagen Region

“…Crashes were extracted from the National Crash Database that the Danish Road Directorate compiles from police reports. These reports enclose information about the characteristics of the crash, the injured persons and the involved vehicles (for details, see, e.g., 34,35). Most importantly, these reports contain a georeferenced location of the crashes that enables matching them with the road network.…”