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作者:

Yang, Yongjie (Yang, Yongjie.) | Zhang, Jinlei (Zhang, Jinlei.) | Yang, Lixing (Yang, Lixing.) | Yang, Yang (Yang, Yang.) | Li, Xiaohong (Li, Xiaohong.) | Gao, Ziyou (Gao, Ziyou.)

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摘要:

Managing multiple traffic modes cooperatively is becoming increasingly important owing to the diversity of passenger demands. Short-term passenger flow predictions for multi-traffic modes can be applied to the management of the multi-traffic modes system. However, this is challenging because the spatiotemporal features of multi-traffic modes are complex. Moreover, the passenger flows of the multi-traffic modes differentiated and fluctuated significantly. To address these is-sues, this study proposes a multitask learning-based model, called Res-Transformer, for short-term inflow prediction of multi-traffic modes. The Res-Transformer consists of two parts: (1) modified Transformer layers comprising the Conv-Transformer layer and the multi-head attention mechanism, which helps extract the spatiotemporal features of multi-traffic modes, and (2) the structure of the residual network, which is utilized to obtain correlations among multi-traffic modes and prevent gradient vanishing and explosion. The proposed model was evaluated using two large-scale real-world datasets from Beijing, China. One was a traffic hub, and the other was a residential area. The results not only demonstrate the effectiveness and robustness of the Res-Transformer but also prove the benefits of considering multi-traffic modes jointly. This study provides critical insights into short-term inflow prediction of the multi-traffic modes system.

关键词:

Transformer Deep learning Short-term passenger flow prediction Multi-traffic modes Multi-task learning

作者机构:

  • [ 1 ] [Yang, Yongjie]Beijing Jiaotong Univ, Sch Syst Sci, Beijing 100044, Peoples R China
  • [ 2 ] [Zhang, Jinlei]Beijing Jiaotong Univ, Sch Syst Sci, Beijing 100044, Peoples R China
  • [ 3 ] [Yang, Lixing]Beijing Jiaotong Univ, Sch Syst Sci, Beijing 100044, Peoples R China
  • [ 4 ] [Gao, Ziyou]Beijing Jiaotong Univ, Sch Syst Sci, Beijing 100044, Peoples R China
  • [ 5 ] [Li, Xiaohong]Beijing Jiaotong Univ, Sch Civil Engn, Beijing 100044, Peoples R China
  • [ 6 ] [Yang, Yang]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100044, Peoples R China

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来源 :

INFORMATION SCIENCES

ISSN: 0020-0255

年份: 2023

卷: 642

8 . 1 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:19

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