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Modeling and analysis of influencing factors of primary accidents to secondary accidents based on structural equation

Xiaoran Gong¹, Runze Liu¹, Jinjun Tang¹

¹Smart Transport Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha, China.

Traffic Injury Prevention

|January 22, 2026

Abstract

Objectives:

Secondary accidents generate a serious threat to road safety and traffic efficiency due to their compounded impact on congestion and emergency response. Due to the higher risks and difficulty for prevention of secondary accidents, the study aims to compose a method to mining the potential relationship of the occurrence of secondary accidents.

Methods:

This study quantifies the influence of primary accidents factors on the secondary accidents occurrence by using Structural Equation Model (SEM). Based on the Statewide Integrated Traffic Records System (SWITRS) accident record and roadway geometry data from OpenStreetMap (OSM), secondary accidents are first identified using a fixed spatiotemporal threshold approach. Then, a multidimensional dataset is constructed by integrating variables related to driving behavior, environmental conditions, and roadway features. SEM includes a measurement model for primary accidents, which defines five latent variables: driving behavior, environment, spatiotemporal features, primary accident features, and high-risk indicators. The measurement model for secondary accidents is developed as well, which defines the latent variable representing secondary accidents. Finally, a structural model is developed to analyze the relationships among the latent variables across the two measurement models.

Results:

The research results show that "PCF_viol_category" (0.9767), weather (0.7657), direction (0.9781), MVIW (0.847) and "State_hwy_ind" (0.5968) of primary accidents, have the highest positive impact value among the factors. While collision time (0.9947), weather (0.9018) and curve (0.3355) are positively significant among the factors in secondary accidents. Besides, high-risk (-0.4995) and driving behavior (-0.1222) have negative impact value, indicating that proactive interventions in high-risk scenarios and safe driving behaviors effectively mitigate the probability of secondary accidents.

Conclusions:

The findings highlight the critical roles of driving behavior regulation and freeway accident management in preventing the escalation of secondary accidents. Environmental conditions and roadway geometric features substantially influence secondary accident risk, underscoring the need for targeted safety management strategies.

Keywords:

Secondary accidents influencing factors structural equation model traffic safety

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