Safety evaluation for regulation changes on commercial vehicle operation using multilevel Bayesian methods

Abstract

Truck-involved crashes cause serious social and economic losses, and the crash severity is higher compared to ordinary vehicles. Driver fatigue is a major crash causation, especially have a larger impact on truck drivers. Therefore, mandating a truck rest-break is one of the road safety management strategies for reducing both truck-involved crash frequency and severity. This paper analyses the change in safety performance according to the revision of the truck rest-break time on highways. Because spatial heterogeneity and homogeneity should be considered to estimate safety effects precisely after executing crash countermeasures, this study estimates the safety effect after the revision of the Trucking Transport Business Act, by developing a model based on the Multilevel Full-Bayes before and after the study. The safety performance function was developed in different section units and distributions, and the crash modification factor was calculated for the most suitable model. As a result of the analysis, the performance of the Multilevel Bayesian Poisson-gamma model was the highest, and it was found that there was a crash reduction effect after the revision of the truck break time. The results of this study can be referred to when preparing measures for road safety regulations.

Keywords:

• Safety performance function
• truck safety
• traffic safety
• multilevel model

Disclosure statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Data availability statement

The data used to support the findings of this study are available from the corresponding author upon request.

Additional information

Funding

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT).(No. 2022R1A2C1093424).