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Factors Affecting Single and Multivehicle Motorcycle Crashes: Insights from Day and Night Analysis Using XGBoost-SHAP Algorithm.

Authors :
Wisutwattanasak, Panuwat
Se, Chamroeun
Champahom, Thanapong
Kasemsri, Rattanaporn
Jomnonkwao, Sajjakaj
Ratanavaraha, Vatanavongs
Source :
Big Data & Cognitive Computing; Oct2024, Vol. 8 Issue 10, p128, 21p
Publication Year :
2024

Abstract

This study aimed to identify and compare the risk factors associated with motorcycle crash severity during both daytime and nighttime, for single and multivehicle incidents in Thailand using 2021–2024 data. The research employed the XGBoost (Extreme Gradient Boosting) method for statistical analysis and extensively examined the temporal instability of risk factors. The results highlight the importance of features impacting the injury severity of roadway collisions across various conditions. For single motorcycle crashes, the key risk factors included speeding, early morning incidents, off-road events, and long holidays. In multivehicle crashes, rear-end collisions, interactions with large vehicles, and collisions involving other motorcycles or passenger cars were linked to increased injury severity. The findings indicate that the important factors associated with motorcyclist injury severity in roadway crashes vary depending on the type of crash and time of day. These insights are valuable for policymakers and relevant authorities in developing targeted interventions to enhance road safety and mitigate the incidence of severe and fatal motorcycle crashes. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
25042289
Volume :
8
Issue :
10
Database :
Complementary Index
Journal :
Big Data & Cognitive Computing
Publication Type :
Academic Journal
Accession number :
180527225
Full Text :
https://doi.org/10.3390/bdcc8100128