Jointly analyzing freeway primary and secondary crash severity using a copula-based approach.

Area of Science:

• Traffic Safety
• Transportation Engineering
• Statistical Modeling

Background:

• Freeway primary and secondary crashes pose significant safety risks.
• Jointly modeling crash severity and correlations is crucial for effective safety analysis.
• Existing models may not fully capture the complex relationships in crash severity.

Purpose of the Study:

• To develop and validate a copula-based model for jointly analyzing freeway primary and secondary crash severity.
• To identify significant factors influencing the severity of both primary and secondary freeway crashes.
• To compare the performance of the copula model against traditional binary probit models.

Main Methods:

• Development of a copula-based statistical model.
• Jointly modeling the severity levels of primary and secondary crashes.
• Estimation using traffic crash data from Los Angeles County (2016-2019).
• Comparison with a traditional binary probit model.

Main Results:

• The copula model demonstrated superior fitting performance compared to the binary probit model.
• Significant factors for primary crash severity include weather, tow-away status, speed, collision type, road condition, terrain, road weaving, and truck involvement.
• Significant factors for secondary crash severity include collision type, road width, road condition, traffic volume, and vehicle speed.

Conclusions:

• The copula model effectively captures the correlation between primary and secondary crash severities.
• Identified factors provide insights for targeted interventions to mitigate freeway crashes.
• Recommendations for countermeasures include improvements in emergency services, vehicle and roadway engineering, law enforcement, and driver education.