Prediction of traffic accident impact range based on CatBoost ensemble algorithm

Songwei Zhang; Haibo Liu; Yundi Yang; Senchang Zhang; Zhongshan Zhang; Chunyu Wang; Mengnan Wang

doi:10.1117/12.2679147

8 May 2023 Prediction of traffic accident impact range based on CatBoost ensemble algorithm

Songwei Zhang, Haibo Liu, Yundi Yang, Senchang Zhang, Zhongshan Zhang, Chunyu Wang, Mengnan Wang

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 1263503 (2023) https://doi.org/10.1117/12.2679147
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

Aiming at the problem that the traditional algorithm is easy to overfitting, which leads to low prediction accuracy of the model. This paper designs a traffic accident impact range prediction model based on CatBoost ensemble algorithm. The model uses linear fitting for range prediction and uses the ordered boosting method to introduce the prior term and weight coefficient. It can automatically adjust dynamically in each calculation, so as to effectively avoid the condition offset and gradient deviation and reduce the overfitting. Under small-scale training, the algorithm can achieve high accuracy prediction and has strong generalization ability.

Citation Download Citation

Songwei Zhang, Haibo Liu, Yundi Yang, Senchang Zhang, Zhongshan Zhang, Chunyu Wang, and Mengnan Wang "Prediction of traffic accident impact range based on CatBoost ensemble algorithm", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 1263503 (8 May 2023); https://doi.org/10.1117/12.2679147

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