Children on wheels: Identifying crash determinants using cluster correspondence analysis

Abstract

Child bicyclists (14 years old and younger) are among the most vulnerable road users, facing significant risks of crashes that often result in severe injuries or fatalities. This study aims to identify key factors influencing child bicyclist crashes and uncover distinct crash patterns using a dataset of 2,394 crashes in Texas from 2017 to 2022. Employing a hybrid approach through machine learning models XGBoost and Random Forest, and Cluster Correspondence Analysis (CCA), the research identified six clusters characterized by unique crash factors and patterns. Moreover, SHAP analysis was conducted on each cluster to further investigate the impact of factors on crash severity. Intersection-related crashes were driven by driver behavior at stop signs and signalized intersections, while urban crashes highlighted risks at marked lanes and driveway access areas. Crashes in residential and rural areas revealed vulnerabilities due to limited traffic control and infrastructure, with rural areas further increased by higher vehicle speeds. Crashes under adverse weather conditions and poor lighting emphasized the role of environmental hazards in increasing crash risks. This study provides countermeasures, including intersection redesigns with protected crossings, expansion of separated bike lanes, and enhanced driveway management. Improved lighting, high-friction road coatings, and weather-specific safety campaigns are recommended to address environmental risks. From a policy perspective, the findings highlight the need for equitable infrastructure investments, stricter enforcement of traffic laws, and educational programs targeting both child bicyclists and drivers. This study also developed an interactive visualization tool that allows users to explore crash locations and related factors. By addressing these challenges, this research offers a framework for improving child bicyclist safety and advancing safer road environments.