Traffic injury prevention · 2026
Identifying the contributory chains and patterns of road facilities in bus-involved crashes using latent class clustering and association rules.
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Nie Chunting, Wang Shunchao, Lin Qinghai
Аннотация
OBJECTIVE: This study aims to investigate the heterogeneity in bus-involved crashes by identifying association rules and contributory patterns of road facilities across different crash types and severities. The goal is to support more targeted and context-specific safety interventions.
METHODS: A total of 14,560 bus-involved crashes that occurred in London, UK, from 2010 to 2019 were analyzed. Each crash record includes attributes related to the driver, vehicle, environment, and road infrastructure. To address crash heterogeneity, Latent Class Clustering (LCC) was used to classify crashes into distinct clusters based on uncontrollable factors such as driver characteristics and environmental conditions. Subsequently, Association Rule Mining (ARM) was applied to identify contributory chains and association patterns of controllable road infrastructure factors within each cluster and severity level. The top ten rules for both slight and serious crashes were extracted in each group.
RESULTS: The findings reveal that typical features and association rules vary significantly across crash clusters and severity levels. On one hand, a single associative chain can result in varying severity levels across different crash clusters. For example, dual carriageways and daylight are contributing factors to both slight and severe crashes. On the other hand, associative chains leading to the same severity level may differ among crash clusters. For instance, yield or uncontrolled junctions, zebra crossings, and daylight are key factors in slight crashes within Cluster 3, but not in other clusters.
CONCLUSIONS: The proposed framework, which integrates LCC and ARM, enables a more nuanced analysis of bus-involved crash data by separating classification and association rules analysis based on controllability. The results provide actionable insights into how specific road infrastructure features contribute to different types and severities of crashes. This can inform the design of targeted safety measures and infrastructure improvements to reduce crash risks in urban bus systems. Moreover, the framework is adaptable to other transportation modes and crash datasets.
METHODS: A total of 14,560 bus-involved crashes that occurred in London, UK, from 2010 to 2019 were analyzed. Each crash record includes attributes related to the driver, vehicle, environment, and road infrastructure. To address crash heterogeneity, Latent Class Clustering (LCC) was used to classify crashes into distinct clusters based on uncontrollable factors such as driver characteristics and environmental conditions. Subsequently, Association Rule Mining (ARM) was applied to identify contributory chains and association patterns of controllable road infrastructure factors within each cluster and severity level. The top ten rules for both slight and serious crashes were extracted in each group.
RESULTS: The findings reveal that typical features and association rules vary significantly across crash clusters and severity levels. On one hand, a single associative chain can result in varying severity levels across different crash clusters. For example, dual carriageways and daylight are contributing factors to both slight and severe crashes. On the other hand, associative chains leading to the same severity level may differ among crash clusters. For instance, yield or uncontrolled junctions, zebra crossings, and daylight are key factors in slight crashes within Cluster 3, but not in other clusters.
CONCLUSIONS: The proposed framework, which integrates LCC and ARM, enables a more nuanced analysis of bus-involved crash data by separating classification and association rules analysis based on controllability. The results provide actionable insights into how specific road infrastructure features contribute to different types and severities of crashes. This can inform the design of targeted safety measures and infrastructure improvements to reduce crash risks in urban bus systems. Moreover, the framework is adaptable to other transportation modes and crash datasets.