Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning.

Autor: Guo Y; College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, PR China; Department of Civil Engineering, Tsinghua University, Beijing 100084, PR China., Li M; Department of Civil Engineering, Tsinghua University, Beijing 100084, PR China. Electronic address: mengli@tsinghua.edu.cn., Li K; School of Vehicle and Mobility, Tsinghua University, Beijing 100084, PR China., Li H; Department of Civil Engineering, Tsinghua University, Beijing 100084, PR China., Li Y; College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, PR China.
Jazyk: angličtina
Zdroj: Accident; analysis and prevention [Accid Anal Prev] 2024 Dec; Vol. 208, pp. 107806. Date of Electronic Publication: 2024 Oct 07.
DOI: 10.1016/j.aap.2024.107806
Abstrakt: Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effectively manage non-recurrent traffic congestion and enhance road safety. This study conducts a comprehensive causal analysis of traffic incident duration using a data collected over a long time and including different types of roads across the city of Tianjin, China. Employing the innovative framework of causal forests with biased machine learning (CF-DML) techniques, this study advances beyond traditional methods by focusing on interpreting the causal relationships between various factors and incident duration, emphasizing the role of heterogeneity among these factors. The CF-DML framework enables the assessment of the average treatment effects (ATEs) of various factors on incident duration. Notably, the significant influence of road type and suburban setting on treatment effects is underscored, which is generally consistent with the results obtained through classical methods. Second, to look more closely at the important factors such as road and collision types, a conditional average treatment effects (CATE) analysis is conducted, explaining heterogeneity through a causal heterogeneity tree. Third, based on insights from causal analysis, policies related to lane configurations are explored, emphasizing the necessity of considering causal effects in traffic management decisions. The CF-DML framework enhances our understanding of traffic incident dynamics, contributing to improved road safety and traffic flow in diverse urban environments.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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