Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model. (June 2021)

• Record Type: Journal Article
• Authors: Hosseinzadeh, Aryan; Moeinaddini, Amin; Ghasemzadeh, Ali
• Is Part Of: Journal of safety research. Volume 77(2021)
• Page Start: 151
• Page End: 160
• Publication Date: 2021-06
• Subjects: Crash injury severity -- Large truck crashes -- Support Vector Machine -- Random parameter logit model -- At-fault party -- Unobserved heterogeneity; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2021.02.012
• Languages: English
• ISSNs: 0022-4375

Abstract

Highlights: The role of at-fault party in large truck-involved crashes has been investigated. Support Vector Machine and Random Parameter Logit Model were utilized for analysis. Fatigue and deviation to left in the at-fault truck-driver crashes led to fatality. 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.