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

• Injury severity in single-bicycle crashes (SBCs) is analyzed. • A machine learning-based method termed Ordered Forest is employed. • ORF performs better than the traditional random forest in terms of predictions. • Factors associated with the injury severity in SBCs are investigated. • Practical guidance for the implementation of ORF is presented. 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. [ABSTRACT FROM AUTHOR]