Citation

Shaon MRR, Qin X, Chen Z, Zhang J. Transp. Res. Rec. 2018; 2672(38): 22-34.

Affiliation

Department of Civil and Environmental Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 2Research Center for Internet of Mobility, School of Transportation, Southeast University, Nanjing, Jiangsu, PR China Corresponding Author: Address correspondence to Xiao Qin: qinx@uwm.edu

Copyright

(Copyright © 2018, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/0361198118790617

PMID

unavailable

Abstract

A significant portion of crashes occurred on highway segments, with more than 90% of crashes associated with driving errors. To avoid a crash, a driver needs to detect a hazard, decide the safest driving maneuvers, and execute them properly. Driver errors at any of these sequential phases may lead to a crash; therefore, it is necessary to identify the contributing factors and assess their influence on driver behavior. To assist this investigation, a multinomial probit model was employed to study driver errors reported in crashes in rural and urban areas. The modeling results identified many highway geometric features, traffic conditions, roadway events, and driver characteristics as statistically correlated to different types of driver error. Following the extensive list, the impacts of error-contributing factors were discussed within each error category. This exercise helps to gain a better understanding of similar or varying effects of explanatory variables across different error categories. The broad and insightful information will help researchers and safety professionals to better understand when, where, and how the driver error may lead to a crash and to develop cost-effective preventive countermeasures.

Language: en