Prediction of traffic accident duration based on N-BEATS

Yue He; Senchang Zhang; Peiyao Zhong; Zhenliang Li

doi:10.1117/12.2679093

8 May 2023 Prediction of traffic accident duration based on N-BEATS

Yue He, Senchang Zhang, Peiyao Zhong, Zhenliang Li

Proceedings Volume 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023); 126350L (2023) https://doi.org/10.1117/12.2679093
Event: International Conference on Algorithms, Microchips, and Network Applications 2023, 2023, Zhengzhou, China

Abstract

The prediction of traffic accident duration is the basis of highway emergency management. Timely and accurate prediction of traffic accident duration can provide a reliable basis for road guidance and rescue organization. This paper discusses the traffic accident duration prediction method of N-BEATS model in detail. Through the change of sliding window size and the continuous adjustment of the number of iterations, the appropriate parameters are found to produce a good prediction effect. The dataset used in this paper is US Accidents, a nation-wide dataset of traffic accidents covering 49 states in the US. The experimental results show that compared with the classical time series prediction models such as Bi-LSTM, SVM, RNN-GRU and AttnAR, prediction of traffic accident duration model based on N-BEATS proposed in this paper is optimal in the three evaluation indicators of RMSE, MAE and SD, which shows that the model has the highest prediction accuracy and good performance.

Citation Download Citation

Yue He, Senchang Zhang, Peiyao Zhong, and Zhenliang Li "Prediction of traffic accident duration based on N-BEATS", Proc. SPIE 12635, Second International Conference on Algorithms, Microchips, and Network Applications (AMNA 2023), 126350L (8 May 2023); https://doi.org/10.1117/12.2679093

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