Abstract
During rapid growth in non-motorized vehicle (NMV) ownership, crash-oriented assessment methods make biased identification of key traffic safety management areas, leading to unclear analysis of safety problems and limited improvement. To improve NMV regional safety, this study developed an approach to identify hazardous crash and crash-involved rider (CIR) areas and explore the mechanisms of primary macro-level contributing factors by jointly modeling crashes and CIRs. Socio-economic, road network, traffic enforcement, and land-use intensity data were collected as independent variables in 115 towns in Suzhou, China. A Poisson lognormal bivariate conditional autoregressive model (PLN-BCAR) and a proposed four-quadrant classification method based on the potential for safety improvement (PSI) density were developed to identify crash-prone and CIR-prone towns. XGBoost and SHAP (SHapley Additive exPlanations) were applied to examine the importance and effects of contributing factors. Results showed that 49.6% of NMV crashes occurred outside the CIRs’ residence areas. The four-quadrant classification method accurately identified crash-prone and CIR-prone areas rather than crash-determined hot zone identification methods. There were nonlinear relationships between primary contributing factors and key areas. Differences of importance and effects for the contributing factors in different areas provided important insights into reducing crashes and CIRs in those areas; for example, NMV crashes and CIRs were higher in areas with low gross domestic product and high population density, and should be selected to make safety improvements such as traffic safety education at the macro level. The proposed approach can help traffic administrators identify the key areas and contributing factors and provide guidelines for improvement.
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