Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data

Ming Sun, Ronggui Zhou, Chengwu Jiao and Xiaoduan Sun
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Ming Sun: Road Safety Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China
Ronggui Zhou: Road Safety Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China
Chengwu Jiao: Road Safety Research Center, Research Institute of Highway Ministry of Transport, Beijing 100088, China
Xiaoduan Sun: Department of Civil Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA

IJERPH, 2022, vol. 19, issue 7, 1-22

Abstract: Although crashes involving hazardous materials (HAZMAT) are rare events compared with other types of traffic crashes, they often cause tremendous loss of life and property, as well as severe hazards to the environment and public safety. Using five-year (2013–2017) crash data (N = 1610) from the Highway Safety Information System database, a two-step machine learning-based approach was proposed to investigate and quantify the statistical relationship between three HAZMAT crash severity outcomes (fatal and severe injury, injury, and no injury) and contributing factors, including the driver, road, vehicle, crash, and environmental characteristics. Random forest ranked the importance of risk factors, and then Bayesian networks were developed to provide probabilistic inference. The results show that fatal and severe HAZMAT crashes are closely associated with younger drivers (age less than 25), driver fatigue, violation, distraction, special roadway locations (such as intersections, ramps, and bridges), higher speed limits (over 66 mph), midnight and early morning (12:00–5:59 a.m.), head-on crashes, more than four vehicles, fire/explosion/spill, poor lighting conditions, and adverse weather conditions. The overall prediction accuracy of 85.8% suggests the effectiveness of the proposed method. This study extends machine learning applications in a HAZMAT crash analysis, which would help develop targeted countermeasures and strategies to improve HAZMAT road transportation safety.

Keywords: hazardous material road transportation; crash severity; random forest; Bayesian network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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