Short-term passenger flow prediction for multi-traffic modes: A residual network and Transformer based multi-task learning method

• URL: http://arxiv.org/abs/2203.00422v1
• Date: Sun, 27 Feb 2022 01:09:19 GMT
• Title: Short-term passenger flow prediction for multi-traffic modes: A residual network and Transformer based multi-task learning method
• Authors: Yongjie Yang, Jinlei Zhang, Lixing Yang, Ziyou Gao
• Abstract summary: Res-Transformer is a learning model for short-term passenger flow prediction of multi-traffic modes. Model is evaluated on two large-scale real-world datasets from Beijing, China. This paper can give critical insights into the short-tern passenger flow prediction for multi-traffic modes.
• Score: 21.13073816634534
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the prevailing of mobility as a service (MaaS), it becomes increasingly important to manage multi-traffic modes simultaneously and cooperatively. As an important component of MaaS, short-term passenger flow prediction for multi-traffic modes has thus been brought into focus. It is a challenging problem because the spatiotemporal features of multi-traffic modes are critically complex. To solve the problem, this paper proposes a multi-task learning-based model, called Res-Transformer, for short-term passenger flow prediction of multi-traffic modes (subway, taxi, and bus). Each traffic mode is treated as a single task in the model. The Res-Transformer consists of three parts: (1) several modified transformer layers comprising 2D convolutional neural networks (CNN) and multi-head attention mechanism, which helps to extract the spatial and temporal features of multi-traffic modes, (2) a residual network architecture used to extract the inner pattern of different traffic modes and enhance the passenger flow features of multi-traffic modes. The Res-Transformer model is evaluated on two large-scale real-world datasets from Beijing, China. One is the region of a traffic hub and the other is the region of a residential area. Experiments are conducted to compare the performance of the proposed model with several state-of-the-art models to prove the effectiveness and robustness of the proposed method. This paper can give critical insights into the short-tern passenger flow prediction for multi-traffic modes.

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