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14 June 2023 Spatio-temporal attention network for urban rail transit passenger flow prediction
Rui Li, Junli Wang, Junhui Ruan, Minghui Cheng, Junqing Shi
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Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127082K (2023) https://doi.org/10.1117/12.2683887
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China
Abstract
Accurate prediction of passenger flow can provide an important basis for the operation and management of urban rail transit. Previous models are based on the learning of static graph structures and cannot achieve the distinction of network structures by attention mechanism. In order to realize the learning of dynamic spatio-temporal characteristics of passenger flow and improve the accuracy of passenger flow prediction, a neural network model based on attention mechanism is proposed in this paper. It consists of a spatial attention module and a temporal attention module. The model uses three different coding strategies to enhance the learning capability of the attention mechanism for spatial location and structural features. In the temporal attention module, bi-directional GRU and attention are combined to extract dynamic changes in the temporal dimension of passenger flow data. Experiments on the Hangzhou Metro dataset demonstrate that this model outperforms the classical model.
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Citation Download Citation
Rui Li, Junli Wang, Junhui Ruan, Minghui Cheng, and Junqing Shi "Spatio-temporal attention network for urban rail transit passenger flow prediction", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127082K (14 June 2023); https://doi.org/10.1117/12.2683887
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KEYWORDS
Machine learning
Data modeling
Convolution
Feature extraction
Neural networks
Transportation
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