Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Ming Sun () and Ronggui Zhou
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Ming Sun: Road Safety Research Center, Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Road, Haidian District, Beijing 100088, China
Ronggui Zhou: Road Safety Research Center, Research Institute of Highway Ministry of Transport, No. 8 Xitucheng Road, Haidian District, Beijing 100088, China

Sustainability, 2023, vol. 15, issue 12, 1-21

Abstract: Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Keywords: hazardous material road transportation; crash; cluster correspondence analysis; risk factors (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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