Understanding key contributing factors on the severity of traffic violations by elderly drivers: a hybrid approach of latent class analysis and XGBoost based SHAP-Reference-Cited by-同舟云学术

Understanding key contributing factors on the severity of traffic violations by elderly drivers: a hybrid approach of latent class analysis and XGBoost based SHAP

Published:2024-01-29 Issue:2 Volume:31 Page:273-293
ISSN:1745-7300
Container-title:International Journal of Injury Control and Safety Promotion
language:en
Short-container-title:International Journal of Injury Control and Safety Promotion

Author:

Sun Zhiyuan¹,Wang Zhicheng¹,Qi Xin¹,Wang Duo¹,Gu Xin¹,Wang Jianyu²,Lu Huapu³,Chen Yanyan¹

Affiliation:

1. Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China

2. Beijing Key Laboratory of General Aviation Technology, Beijing University of Civil Engineering and Architecture, Beijing, China

3. Institute of Transportation Engineering, Tsinghua University, Beijing, China

Funder

Beijing Natural Science Foundation

MOE (Ministry of Education in China) Liberal arts and Social Sciences Foundation

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17457300.2023.2300479

Reference50 articles.

1. Road traffic accidents and the elderly

2. A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers

3. Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability

4. Identification of hazardous road locations of traffic accidents by means of kernel density estimation and cluster significance evaluation

5. A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Considering multi-scale built environment in modeling severity of traffic violations by elderly drivers: An interpretable machine learning framework;Accident Analysis & Prevention;2024-11

2. Injury severity analysis of e-bike riders in China based on the in-vehicle recording video crash data: a random parameter ordered logit model;International Journal of Injury Control and Safety Promotion;2024-07-29

3. Injury severity prediction and exploration of behavior-cause relationships in automotive crashes using natural language processing and extreme gradient boosting;Engineering Applications of Artificial Intelligence;2024-07