Using LightGBM with SHAP for predicting and analyzing traffic accidents severity-Reference-Cited by-同舟云学术

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Using LightGBM with SHAP for predicting and analyzing traffic accidents severity

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:2023 7th International Conference on Transportation Information and Safety (ICTIS)
language:
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Author:

Li Jinqiang¹,Guo Yuying¹,Li Li¹,Liu Xiaofeng²,Wang Runmin³

Affiliation:

1. Chang’an University,School of Electronics and Control Engineering,Xi’an,China

2. Tianjin University of Technology and Education,School of Automotive and Transportation,Tianjin,China

3. Chang’an University Xi’an,School of Information Engineering,China,China

Funder

Natural Science Basic Research Program of Shaanxi Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10243582/10243583/10243855.pdf?arnumber=10243855

Reference19 articles.

1. Predictive System of Traffic Congestion based on Machine Learning

2. LightGBM: A Highly Efficient Gradient Boosting Decision Tree;ke;Advances in neural information processing systems,2017

3. Short-Term Traffic Flow Prediction Based on Ensemble Machine Learning Strategies

4. Combination predicting model of traffic congestion index in weekdays based on LightGBM-GRU;cheng;Scientific Reports,2022

5. A comparison of statistical learning methods for deriving determining factors of accident occurrence from an imbalanced high resolution dataset

Cited by 1 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. Multiple Intrusion Detection Using Shapley Additive Explanations and a Heterogeneous Ensemble Model in an Unmanned Aerial Vehicle’s Controller Area Network;Applied Sciences;2024-06-25

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