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Journal Article

Citation

Hong J, Park J, Lee G, Park D. Accid. Anal. Prev. 2019; 131: 327-335.

Affiliation

Department of Transportation Engineering, The University of Seoul, 163 Seoulsiripdae-ro Dongdaemun-gu, Seoul, 02504, South Korea. Electronic address: djpark@uos.ac.kr.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.aap.2019.07.026

PMID

31377496

Abstract

Freight truck-involved crashes result in a high mortality rate and significantly impact logistic costs; therefore, many researchers have analyzed the causes of truck-involved traffic crashes. In the existing literature, it was found that truck-involved crashes are affected by factors such as road geometry, weather, driver and vehicle characteristics, and traffic volume based on a variety of statistical methodologies; however, the endogenous impact resulting from driver traffic violation has not been considered. The goal of the study is to discover the factors influencing freight vehicle crashes and develop more accurate crash probability estimation by explaining the endogenous driver traffic violations. To achieve the purpose of this study, we applied the two-stage residual inclusion (2SRI) approach, a methodology used in the nonlinear regression analysis model for capturing the endogeneity issue. This method improves the accuracy of the model by capturing the unobserved effects of driver traffic violations. From the results, traffic violations were identified to be influenced by the driver's physical condition, as well as driver and vehicle characteristics. Furthermore, variables of driver traffic violations such as improper passing, speeding, and safe distance violation were found to be endogenous in the probability model of freight truck crashes on expressway mainlines.

Copyright © 2019 Elsevier Ltd. All rights reserved.


Language: en

Keywords

Endogeneity; Expressway; Freight truck-involved crash; Traffic violation; Two-stage residual inclusion

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