Estimating Factors Contributing to Frequency and Severity of Large Truck–Involved Crashes

Abstract

Understanding the factors that contribute to crash frequency and severity will assist better highway design and develop appropriate countermeasures for hot spots, thereby improving the safety of the road system. This study explores the influences of risk factors on frequency and severity of large truck–involved crashes. Multinomal logit (MNL) and negative binomial (NB) models are proposed to analyze crash severity and frequency, respectively. The explanatory factors include characteristics of the vehicles, drivers, traffic, environment, and roadway geometric design features. To obtain better parameter estimation results, the maximum likelihood (ML) method and Bayesian method are employed. The results show that the MNL and NB models have a better goodness of fit under the Bayesian estimation framework. Using Bayesian MNL and NB models, factors that significantly affect crash frequency and severity outcomes are analyzed. Some critical factors that contribute significantly to both crash severity and frequency estimation of large truck–involved crashes, including truck percentage, annual average daily traffic (AADT), driver condition, and weather condition, are identified and discussed. Driver age, speed limit, and location type are found to have significant effects only on the frequency of large truck–involved crashes. Seat belt usage, light condition, and terrain type are found to have significant effects only on the severity of large truck involved crashes. The results from this study will be valuable in transportation policy making, improvement of carrier operation, and crash-cost reduction.