Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations

• URL: http://arxiv.org/abs/2410.14727v1
• Date: Wed, 16 Oct 2024 03:14:51 GMT
• Title: Leveraging Intra-Period and Inter-Period Features for Enhanced Passenger Flow Prediction of Subway Stations
• Authors: Xiannan Huang, Chao Yang, Quan Yuan,
• Abstract summary: Short-term passenger flow prediction of subway stations plays a vital role in enabling station personnel to proactively address changes in passenger volume. There is a lack of research on effectively integrating features from different periods, particularly intra-period and inter-period features. We propose a novel model called textbfMuti textbfPeriod textbfSpatial bfbfTemporal textbfMPSTN.
• Score: 4.911867249390154
• License:
• Abstract: Accurate short-term passenger flow prediction of subway stations plays a vital role in enabling subway station personnel to proactively address changes in passenger volume. Despite existing literature in this field, there is a lack of research on effectively integrating features from different periods, particularly intra-period and inter-period features, for subway station passenger flow prediction. In this paper, we propose a novel model called \textbf{M}uti \textbf{P}eriod \textbf{S}patial \textbf{T}emporal \textbf{N}etwork \textbf{MPSTN}) that leverages features from different periods by transforming one-dimensional time series data into two-dimensional matrices based on periods. The folded matrices exhibit structural characteristics similar to images, enabling the utilization of image processing techniques, specifically convolutional neural networks (CNNs), to integrate features from different periods. Therefore, our MPSTN model incorporates a CNN module to extract temporal information from different periods and a graph neural network (GNN) module to integrate spatial information from different stations. We compared our approach with various state-of-the-art methods for spatiotemporal data prediction using a publicly available dataset and achieved minimal prediction errors. The code for our model is publicly available in the following repository: https://github.com/xiannanhuang/MPSTN

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