Factors impacting bike crash severity in urban areas

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

Analyzing the injury severity in single-bicycle crashes: An application of the ordered forest with some practical guidance

This paper investigates the injury severity of cyclists in single-bicycle crashes (SBCs) in the UK. The data for analysis is constructed from the STATS19 road traffic casualty database, covering the period of 2016-2019. A machine learning-based ordered choice model termed Ordered Forest (ORF) is used. In our empirical analysis, ORF is found to produce more accurate class predictions of the SBC injury severity than the traditional random forest algorithm. Moreover, the factors associated with the injury severity are revealed, including the time and location of occurrence, the age of cyclists, roadway conditions, and crash-related factors. Specifically, old cyclists are more likely to be seriously injured in SBCs. Rural areas, higher speed limits, run-off crashes, and hitting objects are also related to an increased probability of serious injuries. While SBCs occurring at junctions, and/or during peak hours (i.e., 6:30-9:30 and 16:00-19:00) are less severe. To achieve the ambition of a step change in cycling and walking put forward by the UK Department for Transport, SBCs deserve more public attention. Lastly, regarding the implementation of ORF in crash injury severity analysis, we provide some practical guidance based on a series of simulation experiments.