An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers’ visual perceptions
Journal article, 2023

Illegal running into the opposite lane (IROL) on curve sections of two-lane rural roads is a frequently hazardous behavior and highly prone to fatal crashes. Although driving behaviors are always determined by the information from drivers’ visual perceptions, current studies do not consider visual perceptions in predicting the occurrence of IROL. In addition, most machine learning methods belong to black-box algorithms and lack the interpretation of prediction results. Therefore, this study aims to propose an interpretable prediction model of IROL on curve sections of two-lane rural roads from drivers’ visual perceptions. A new visual road environment model, consisting of five different visual layers, was established to better quantify drivers’ visual perceptions by using deep neural networks. In this study, naturalistic driving data was collected on curve sections of typical two-lane rural roads in Tibet, China. There were 25 input variables extracted from the visual road environment, vehicle kinematics, and driver characteristics. Then, XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive exPlanation) methods were combined to build a prediction model. The results showed that our prediction model performed well, with an accuracy of 86.2% and an AUC value of 0.921. The average lead time of this prediction model was 4.4 s, sufficient for drivers to respond. Due to the advantages of SHAP, this study interpreted the impacting factors on this illegal behavior from three aspects, including relative importance, specific impacts, and variable dependency. After offering more quantitative information on the visual road environment, the findings of this study could improve the current prediction model and optimize road environment design, thereby reducing IROL on curve sections of two-lane rural roads.

Interpretable machine learning

Curve sections of two-lane rural roads

Deep neural networks

Naturalistic driving data

Visual road environment quantification

Illegal running into the opposite lane

Author

Li He

Tongji University

Bo Yu

Tongji University

Yuren Chen

Tongji University

Shan Bao

University of Michigan

Kun Gao

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

You Kong

Shanghai Maritime University

Accident Analysis and Prevention

0001-4575 (ISSN)

Vol. 186 107066

Subject Categories

Transport Systems and Logistics

Infrastructure Engineering

Vehicle Engineering

DOI

10.1016/j.aap.2023.107066

PubMed

37058902

More information

Latest update

5/3/2023 1