Citation

Wu P, Chen T, Diew Wong Y, Meng X, Wang X, Liu W. Transp. Res. A Policy Pract. 2023; 173: e103717.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.tra.2023.103717

PMID

unavailable

Abstract

Traffic safety is a critical factor that has always been considered in policy making for urban transportation planning and management. Accurately predicting crash risk hot spots allows urban transportation agencies to better implement countermeasures towards enhancing traffic safety. Considerable efforts have been devoted to investigate crash risk hot spots in many previous studies. We hereby identify three research gaps that remain to be resolved: first, the effects of spatio-temporal features surrounding hot spots are often ignored; second, false discovery rates tend to be higher when applying local spatial indices to identify hot spots; and third, the spatio-temporal correlations and heterogeneity of crash-related features in a subject spot and its neighboring spots have not been well captured in most crash risk prediction models. To fill these gaps, we propose an urban crash risk identification model by integrating space-time cubes and machine learning techniques. The spatio-temporal correlations and heterogeneity of crash-related features are represented by using statistical descriptions of neighboring cubes. Three space-time cube risk datasets collected from Manhattan in New York City are used to validate the proposed model in the case study. The eXtreme Gradient Boosting (XGBoost) classifier is employed to predict the risk patterns (hot spots, normal spots, and cold spots) of each cube due to its satisfactory prediction performance. The validation results suggest that our proposed model attains lower false discovery rates and higher crash risk prediction accuracy as compared to conventional methods. As the results of the feature selection are empowered by machine learning, we found that most key features are inherent to the features of spatial neighboring cubes, which manifests the importance of considering the features of neighboring spots when identifying crash risk hot spots. Moreover, SHapley Additive exPlanations (SHAP) are employed to interpret the effects of key features on hot spots, upon which the contributions of the features related to urban facilities, public transit, and land use are discussed. Based on the feature interpretation, several policy recommendations could be made to enhance urban road traffic safety in the future.

Language: en

Keywords

Crash risk hot spots; Crash risk prediction; Machine learning; Space–time cubes