Citation

Cai Q, Abdel-Aty M, Zheng O, Wu Y. Transp. Res. C Emerg. Technol. 2022; 135: e103541.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.trc.2021.103541

PMID

unavailable

Abstract

This study aims to explore the effects of drivers' visual environment on speeding crashes by using different machine learning techniques. To obtain the data of drivers' visual environment in the real world, a framework was proposed to obtain the Google street view (GSV) images. Deep neural network and computer vision technologies were applied to obtain the clustering and depth information from the GSV images. To reflect drivers' visual environment in the real world, the coordinate transformation was conducted, and several visual measures were proposed and calculated. Three different tree-based ensemble models (i.e., random forest, adaptive boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost)) were applied to estimate the number of speeding crashes and the comparison results showed that XGBoost could provide the best data fit. The explainable machine learning method were applied to explore the effects of drivers' visual environment and other features on speeding crashes. The results validated the visual environment data obtained by the proposed method for the speeding crash analysis. It was suggested that the proportion of trees in the drivers' view and the proportion of road length with trees could reduce speeding crashes. In addition, the complexity level of drivers' visual environment was found to increase the crash occurrence. This study provided new insights to obtain the detailed information from GSV images for traffic safety analysis. The findings based on the explainable machine learning could also provide road planners and engineers clear suggestions to select appropriate countermeasures to enhance traffic safety.

Language: en

Keywords

Computer vision; Coordinate transformation; Deep learning; Drivers’ visual environment; Explainable machine learning; Google street view; Speeding crashes