Bayesian networks for identifying causal effects of factors on crash injury severity at signalized intersections.

Abstract

Signalized intersections are the areas where traffic crashes with severe injuries frequently happen. Although existing studies have explored the factors affecting crash injury severity at signalized intersections, intricate causal relationships between factors often fail to be captured. Thus, usage of Bayesian network reveals factors contributing to injury severity and the causal relationships between them, with the use of crash data extracted from the Crash Report Sampling System in 2021. The K2 algorithm and Expectation-Maximization algorithms are adopted for structure learning and parameter learning in Bayesian networks, respectively. The results indicate that 1) factors such as speeding, drunk driving, and use of airbags can significantly affect the injury severity, 2) causal relationships exist between distraction, running the red signal, collision type, and crash injury severity, and 3) compared to the random parameter logit model and random forest, Bayesian network has better accuracy in predicting the crash injury severity. The findings can serve to propose effective traffic safety intervention measures to reduce the injury severity of crashes at signalized intersections.