Arrival time prediction of transport based on recurrent neural network

Jiacheng Lin; Peixin Dong; Yunpeng Hu; Yonghong Chen; Jianping Xing

doi:10.1117/12.2641400

10 November 2022 Arrival time prediction of transport based on recurrent neural network

Jiacheng Lin, Peixin Dong, Yunpeng Hu, Yonghong Chen, Jianping Xing

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123480T (2022) https://doi.org/10.1117/12.2641400
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

In this paper, through in-depth study of highway traffic and non-highway arrival time prediction model and algorithm, it is found that the performance of DA-RNN (recurrent neural network based on two-stage attention) is better than other classical prediction models. We choose to introduce an attention mechanism to adaptively select the most relevant factors from heterogeneous information and establish a prediction network based on da-rnn (recurrent neural network based on two-stage attention). In this paper, the real data of Jinan bus driving is used to carry out experiments, and the bus travel prediction is realized. Under the same data condition, a variety of methods are tested. Through the comparative analysis of experimental results, the method proposed in this paper performs better on the data set provided by Jinan bus company.

Citation Download Citation

Jiacheng Lin, Peixin Dong, Yunpeng Hu, Yonghong Chen, and Jianping Xing "Arrival time prediction of transport based on recurrent neural network", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123480T (10 November 2022); https://doi.org/10.1117/12.2641400

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