Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model |
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| Institution: | 1. Department of Civil and Environmental Engineering, University of Louisville, Louisville, KY 40292, United States;2. Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, Iran;3. Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, United States;1. Department of Civil Engineering, University of New Mexico, MSC01 1070, 1 University of New Mexico, Albuquerque, NM 87131, United States;2. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, China;3. Department of Civil and Environmental Engineering, University of Utah, 110 Central Campus Drive, 2137 MCE, Salt Lake City, UT 84112, United States;4. School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73 Huanghe Rd., NanGang Dist., Harbin 150090, China;1. Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China;2. Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China;3. Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China;4. Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai, China;1. Laboratório Nacional de Engenharia Civil, Departamento de Transportes, Núcleo de Planeamento, Tráfego e Segurança Av. do Brasil 101, Lisboa 1700-066, Portugal;2. Department of Civil and Environmental Engineering, Rowan University, Glassboro, NJ 08028, United States;1. School of Civil and Construction Engineering Oregon State University, 309 Owen Hall, Corvallis, OR 97331-3212, USA;2. Department of Civil and Environmental Engineering, West Virginia University, Room 621 ESB, P. O. Box 6103, Morgantown, WV 26506-6103, USA;1. Alabama Transportation Institute, The University of Alabama Tuscaloosa, AL, United States;2. Department of Civil, Construction and Environmental Engineering, The University of Alabama Tuscaloosa, AL, United States |
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| Abstract: | Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes. |
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| Keywords: | Crash injury severity Large truck crashes Support Vector Machine Random parameter logit model At-fault party Unobserved heterogeneity |
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