Cross-classified multilevel models for severity of commercial motor vehicle crashes considering heterogeneity among companies and regions. (September 2017)

• Record Type: Journal Article
• Authors: Park, Ho-Chul; Kim, Dong-Kyu; Kho, Seung-Young; Park, Peter Y.
• Is Part Of: Accident analysis and prevention. Volume 106(2017)
• Page Start: 305
• Page End: 314
• Publication Date: 2017-09
• Subjects: Commercial motor vehicle safety -- Cross-classified multilevel model -- Type I statistical error -- Fatal and injury severity -- Logit model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2017.06.009
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: The factors affecting severity of commercial motor vehicle crashes are analyzed. A cross-classified multilevel ordered logit model (CCMM) is proposed. The CCMM avoids the type I statistical errors. The CCMM can analyze two non-nested groups simultaneously. Abstract: This study analyzes 86, 622 commercial motor vehicle (CMV) crashes (large truck, bus and taxi crashes) in South Korea from 2010 to 2014. The analysis recognizes the hierarchical structure of the factors affecting CMV crashes by examining eight factors related to individual crashes and six additional upper level factors organized in two non-nested groups (company level and regional level factors). The study considers four different crash severities (fatal, major, minor, and no injury). The company level factors reflect selected characteristics of 1, 875 CMV companies, and the regional level factors reflect selected characteristics of 230 municipalities. The study develops a single-level ordinary ordered logit model, two conventional multilevel ordered logit models, and a cross-classified multilevel ordered logit model (CCMM). As the study develops each of these four models for large trucks, buses and taxis, 12 different statistical models are analyzed. The CCMM outperforms the other models in two important ways: 1) the CCMM avoids the type I statistical errors that tend to occur when analyzing hierarchical data with single-level models; and 2) the CCMM can analyze two non-nested groups simultaneously. Statistically significant factors include taxi company's type of vehicle ownership and municipality's level of transportation infrastructure budget. An improved understanding of CMV related crashes should contribute to the development of safety countermeasures to reduce the number and severity of CMV related crashes.