Network-level crash risk analysis using large-scale geometry features.

Autor: Qiu S; School of Civil Engineering, Central South University, Changsha 410075, China; MOE Key Laboratory of Engineering Structures of Heavy-haul Railway, Changsha 410075, China; Intelligent Monitoring Research Center of Rail Transit Infrastructure, Changsha 410075, China. Electronic address: sheldon.qiu@csu.edu.cn., Ge H; School of Civil Engineering, Central South University, Changsha 410075, China. Electronic address: hanzhangge@csu.edu.cn., Li Z; School of Civil Engineering, Central South University, Changsha 410075, China. Electronic address: lizheng0924@csu.edu.cn., Gao Z; School of Transportation Engineering, Chang'An University, Xian 710064, China. Electronic address: gaozhixiang1998@chd.edu.cn., Ai C; Department of Civil and Enviromental Engineering, University of Massachusetts Amherst, USA. Electronic address: chengbo.ai@umass.edu.
Jazyk: angličtina
Zdroj: Accident; analysis and prevention [Accid Anal Prev] 2024 Nov; Vol. 207, pp. 107746. Date of Electronic Publication: 2024 Aug 16.
DOI: 10.1016/j.aap.2024.107746
Abstrakt: Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways' horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model's attributes with the Harris Hawks Optimization (HHO) algorithm-referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of "region-road-segment" for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models.
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. Published by Elsevier Ltd.)
Databáze: MEDLINE
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