Risk Assessment of Commercial dangerous -goods truck drivers using geo-location data: A case study in China. (March 2020)

• Record Type: Journal Article
• Authors: Niu, Shifeng; Ukkusuri, Satish V.
• Is Part Of: Accident analysis and prevention. Volume 137(2020)
• Publication Date: 2020-03
• Subjects: 00-01 -- 99-00; Traffic risk -- Survival analysis -- Commercial dangerous-goods truck -- Risk exposure factors; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2019.105427
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Analyzing the CDT driving risk of a specific route using historical location data. Weather, traffic flow, travel time and average velocity have significant impact on CDT safety. The amount of data does not result in a significant difference in risk ranking. Employment time and the task stability has very small impact on results. Abstract: The primary objective of this study is to understand the relationship between driving risk of commercial dangerous-goods truck (CDT) and exposure factors and find a way to evaluate the risk of specific transportation environment, such as specific transportation route. Due to increasing transportation demand and potential threat to public, commercial dangerous goods transportation (CDGT) has drawn attention from decision makers and researchers within governmental and non-governmental safety organization. However, there are few studies focusing on driving risk assessment of commercial dangerous-goods truck by environmental factors. In this paper we employ survival analysis methods to analyze the impact of risk exposure factors on non-accident mileage of commercial dangerous-good truck and assess risk level of specific driving environment. Using raw location data from six transportation companies in China, we derive a set of 17 risk exposure factors that we use for model parameters estimation. The survival model and hazard model were estimated using the Weibull distribution as the baseline distribution. The results show that four factors - weather, traffic flow, travel time and average velocity have a significant impact on the non-accident mileage of driver in this company, and the assessment results of survival function and hazard function are robust to the different levels of testing data. The employment time has some effect on the results but does not result in a significant difference in most cases, and the task stability has little impact on the results. The findings of this study should be useful for decision makers and transportation companies to better risk assessment of CDT.