Learning spatial-temporal dynamics and interactivity for short-term passenger flow prediction in urban rail transit

Wu, Jinxin; Li, Xianwang; He, Deqiang; Qin, Li; Xiang, Weibin

doi:10.1007/s10489-023-04508-5

Cited by 7 publications

(2 citation statements)

References 53 publications

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“…In the initial stage of deep learning, many models based on recurrent neural networks (RNNs) have emerged, which are widely employed in passenger flow prediction tasks for their superior ability to handle temporal features [12][13][14]. As one of the variants of RNN, the gated recurrent unit (GRU) distinguishes itself through its advanced gating mechanism [15,16]. However, despite the progress made in contemporary passenger flow prediction models, a fundamental limitation of these models is their inability to capture spatial features.…”

Section: Reference Descriptionmentioning

confidence: 99%

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Li,

Liu,

2023

Applied Sciences

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Accurate prediction of metro passenger flow helps operating departments optimize scheduling plans, alleviate passenger flow pressure, and improve service quality. However, existing passenger flow prediction models tend to only consider the historical passenger flow of a single station while ignoring the spatial relationships between different stations and correlations between passenger flows, resulting in low prediction accuracy. Therefore, a multi-scale residual depthwise separable convolution network (MRDSCNN) is proposed for metro passenger flow prediction, which consists of three pivotal components, including residual depthwise separable convolution (RDSC), multi-scale depthwise separable convolution (MDSC), and attention bidirectional gated recurrent unit (AttBiGRU). The RDSC module is designed to capture local spatial and temporal correlations leveraging the diverse temporal patterns of passenger flows, and then the MDSC module is specialized in obtaining the inter-station correlations between the target station and other heterogeneous stations throughout the metro network. Subsequently, these correlations are fed into AttBiGRU to extract global interaction features and obtain passenger flow prediction results. Finally, the Hangzhou metro passenger inflow and outflow data are employed to assess the model performance, and the results show that the proposed model outperforms other models.

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Section: Reference Descriptionmentioning

confidence: 99%

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Li,

Liu,

2023

Applied Sciences

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“…However, this method only considers the shallow features of external factors while ignoring their deeper features. Wu et al (2023) proposed the Multi Feature Fusion Graph Convolutional Network (MFGCN), where GCN is used to extract spatial dependencies, while LSTM with attention mechanism is used to extract temporal features. However, this method failed to fully consider external factors such as date, weather, and air index.…”

Section: Related Workmentioning

confidence: 99%

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

Liu

2023

International Journal of Swarm Intelligence Research

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To address the problems of existing passenger flow prediction methods such as low accuracy, inadequate learning of spatial features of station topology, and inability to apply to large networks, a SAE-GCN-BiLSTM-based passenger flow forecasting method for urban rail transit is proposed. First, the external features are extracted layer by layer using stacked autoencoder (SAE). Then, graph convolutional network (GCN) is used to capture the spatial features of station topology, and bi-directional long and short-term memory network (BiLSTM) is used to extract the bi-directional temporal features, realizing the extraction of the spatio-temporal features. Finally, external features and spatio-temporal features are fused for accurate prediction of urban rail transit passenger flow. The experimental results show that the proposed method is higher than several other advanced models in the evaluation indexes under different granularities, indicating that the model effectively develops the accuracy and robustness of urban rail transit passenger flow prediction.

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A hybrid neural network for urban rail transit short-term flow prediction

Zhu,

Sun,

et al. 2024

J Supercomput

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Learning spatial-temporal dynamics and interactivity for short-term passenger flow prediction in urban rail transit

Cited by 7 publications

References 53 publications

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction

A Passenger Flow Prediction Method Using SAE-GCN-BiLSTM for Urban Rail Transit

A hybrid neural network for urban rail transit short-term flow prediction

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