Identify Risk Pattern of E-Bike Riders in China Based on Machine Learning Framework

Abstract

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e. non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.

Impact of Helmet-Wearing Policy on E-Bike Safety Riding Behavior: A Bivariate Ordered Probit Analysis in Ningbo, China

“…According to the estimation results of the BOP model, the evening peak is an important reason for crash involvement ( β = 0.370), which may be because, at that time, users are in a state of fatigue after a day’s work, which leads to a lack of attention during cycling, thus leading to crash involvement. Our findings are consistent with those of [ 60 , 61 ]. The crash involvement of shoppers is also relatively high ( β = 0.217), which may be caused by people’s lack of attention while leisure shopping.…”

The Analysis of the Factors Influencing the Severity of Bicyclist Injury in Bicyclist-Vehicle Crashes

“…It should be noted, however, that in many cases the limitation of the conducted research was the possibility/inability to obtain detailed or specific data for the analysis. On the other hand, taking into account the mathematical apparatus used in the analysis, it should be stated that despite the research work with the use of machine learning models to identify significant factors affecting bicyclists' injuries [26][27][28], the dominant works are those in which the authors used in their analyzes discrete choice models (i.e., logit models [29,30], mixed logit models [16,31], probit models [7], or polynomial logit models [32][33][34][35]). Discrete choice models are more often selected for analysis of factors affecting bicyclists' injury, as the model inputs, as well as the outputs, are usually discrete.…”

Electric Bicyclist Injury Severity during Peak Traffic Periods: A Random-Parameters Approach with Heterogeneity in Means and Variances

“…Electric bicyclists represent a sizable population of commuters [ 8 , 9 , 10 ]. By 2019, there were 59 electric bicycles for every 100 households in China [ 11 ].…”