Citation

Xing L, Yu L, Zheng O, Abdel-Aty M. Accid. Anal. Prev. 2023; 185: e107011.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.aap.2023.107011

PMID

36898230

Abstract

In the diverging area of toll plazas, the absence of lane markings, the gradual widening of lanes, and the crossing of vehicles with different tolling methods increase the likelihood of collisions. This study proposed a concept of motion constraint degree to investigate traffic conflict risks in the toll plaza diverging area. On the basis of the motion constraint degree, a two-step method was developed, in which all potentially influencing factors were separated into two parts. The first part was used to analyze the association between the motion constraint degree and some factors, while the remaining factors were utilized for risk regression/prediction together with the motion constraint degree. The random parameters logit model was applied for regression analysis and four prevalent machine learning models were employed for risk prediction.

RESULTS indicate that (1) the proposed approach considering motion constraint degree outperforms the conventional direct method, no matter for conflict risk regression or prediction; (2) the motion constraint degree is not monotonically correlated with the risk level of vehicles; (3) due to the layout of the toll plaza, ETC vehicles are less likely to be at risk in the diverging area; and (4) lane-changing behaviors in the restricted space increase the conflict risk.

Language: en

Keywords

Machine learning; Driving behavior; Traffic conflict; Motion constraint degree; Random parameters logit; Toll plaza diverging area