Short-term inbound passenger flow prediction of urban rail transit network based on RS-Conv1dGRU

Duohong Wang; Ruiling Gao; Wanqing Su

doi:10.1117/12.2637159

8 May 2022 Short-term inbound passenger flow prediction of urban rail transit network based on RS-Conv1dGRU

Duohong Wang, Ruiling Gao, Wanqing Su

Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）; 122493B (2022) https://doi.org/10.1117/12.2637159
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 2022, Xiamen, China

Abstract

With the increasing scale of urban rail network operation, the network level passenger flow prediction is more valuable than single station research. In this paper, we propose an RS-Conv1dGRU-based short-term passenger flow forecasting model for urban railways. The forecasting process consists of two stages: the first stage: considering the spatial dependency between neighboring stations, distant stations with similar functions and accessible stations, we propose the SR-GA adaptive station arrangement method to rearrange the line stations in order to effectively explore the spatial correlation between stations; the second stage :An end-to-end prediction framework based on GRU and Conv1d is constructed to use the stations data from the first stage as input to achieve inbound passenger flow prediction at the network level. The validity of the model is verified by using AFC data of a city metro, and the results show that RSConv1dGRU (using station location rearrangement) shows better prediction results than Conv1d-GRU (considering only geographic proximity), and the prediction effect is improved more significantly during peak hours. Compared with the existing methods, the prediction accuracy of RS-Conv1dGRU is better, where MAE and RMSE are 10.45 and 18.10, respectively.

Citation Download Citation

Duohong Wang, Ruiling Gao, and Wanqing Su "Short-term inbound passenger flow prediction of urban rail transit network based on RS-Conv1dGRU", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 122493B (8 May 2022); https://doi.org/10.1117/12.2637159

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KEYWORDS

Mining

Data modeling

Performance modeling

Convolution

Feature extraction

Roads

Neural networks

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