What question did this study set out to answer?

To analyze bus-involved crash data using a framework that separates classification and association rules based on controllability.

January 23, 2026

Identifying the contributory chains and patterns of road facilities in bus-involved crashes using latent class clustering and association rules

Key Points

To analyze bus-involved crash data using a framework that separates classification and association rules based on controllability.
Integrating latent class clustering (LCC) and association rules analysis (ARM)
Analyzing crash data based on road infrastructure features
Identifying different types and severities of bus crashes
Identified key infrastructures contributing to crash types
Provided insights for targeted safety measures
Framework adaptable to various transportation modes and datasets

Abstract

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.

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