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8 November 2024 A traffic accident early warning model based on XGBOOST
Yanzhi Pang, Xiang Wang, Yang Tang, Gu Guobin, Jianqiu Chen, Bingheng Yang, Longtang Ning, Chun Bao, Shixuan Zhou, Xiali Cao, Xuguang Wen
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Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161Z (2024) https://doi.org/10.1117/12.3049832
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
Abstract
Our research is based on the XGBOOST algorithm, which aims to build a model that can provide accurate early warning for traffic accidents. Our model incorporates machine learning algorithms and deep learning frameworks with the aim of providing effective early warning information by accurately predicting road traffic accidents.XGBOOST, as an efficient implementation of gradient boosting decision trees, dramatically improves the model's generalization ability and training speed through regularization techniques and cluster parallelization. The model parameters are tuned using the GridSearchCV exhaustive search method to ensure the optimal parameter configuration. In the experiments, the early warning model is constructed and optimized by classifying the severity of accidents and fusing multiple types of data using Yulin traffic accident data from 2015 to 2021. Eventually, the model performed well in terms of classification accuracy and stability, especially in recognizing major accident categories, with significantly higher recall and F1 scores. The study in this paper shows that the application of XGBOOST model in traffic accident early warning has high predictive performance and practical value, which provides important technical support for road safety.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yanzhi Pang, Xiang Wang, Yang Tang, Gu Guobin, Jianqiu Chen, Bingheng Yang, Longtang Ning, Chun Bao, Shixuan Zhou, Xiali Cao, and Xuguang Wen "A traffic accident early warning model based on XGBOOST", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161Z (8 November 2024); https://doi.org/10.1117/12.3049832
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KEYWORDS
Data modeling
Roads
Machine learning
Performance modeling
Statistical modeling
Transportation
Visual process modeling
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