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Short-term forecasting of origin-destination matrix in transit system via a deep learning approach

  • Yuxin He
  • , Yang Zhao*
  • , Kwok-Leung Tsui
    • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

37   Link opens in a new tab Citations (Scopus)

Abstract

Short-term travel demand forecasting is the critical first step to support transportation system management. Complex relevance among Origin-Destination (OD) pairs, temporal dependencies, and external factors bring challenges to it. An innovative deep learning approach, Multi-Fused Residual Network (MF-ResNet) is proposed to forecast travel demand. The complex relevance among OD pairs is converted into graphical-based spatial dependencies by treating OD matrix as the input of the model. The residual network units enable MF-ResNet to model not only near but also distant spatial correlations. Three conv-based residual network units model the temporal closeness, mid-term periodicity, as well as long-term periodicity features, and Fully-Connected (F-C) layers capture external factors. The fusion techniques coordinate all of the features. The proposed method is applied to the short-term forecasts of metro OD matrix in Shenzhen, China. The experimental results show that MF-ResNet can capture multiple complex dependencies robustly and outperforms traditional methods in terms of forecasting accuracy.
Original languageEnglish
Article number2033348
JournalTransportmetrica A: Transport Science
Volume19
Issue number2
Online published19 Feb 2022
DOIs
Publication statusPublished - Feb 2023

Funding

This work was supported by the Hong Kong Research Grants Council Theme-based Research Scheme [grant number T32-101/15-R], National Natural Science Foundation of China [grant number 71901188], Guangdong Basic and Applied Basic Research Foundation [grant number 2021A1515110731], and Basic research grants of Shenzhen Natural Science Foundation [grant  number JCYJ20210324121203008].

Research Keywords

  • Short-term OD matrix forecasting
  • MF-ResNet
  • spatiotemporal
  • conv-based residual network units
  • NEAREST NEIGHBOR MODEL
  • NEURAL-NETWORK
  • TRIP MATRICES
  • RAIL TRANSIT
  • TRAFFIC FLOW
  • PREDICTION
  • RIDERSHIP
  • VOLUMES

RGC Funding Information

  • RGC-funded

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  • TBRS: Safety, Reliability, and Disruption Management of High Speed Rail and Metro Systems

    XIE, M. (Principal Investigator / Project Coordinator), BENSOUSSAN, A. (Co-Principal Investigator), LO, S. M. (Co-Principal Investigator), SHOU, B. (Co-Principal Investigator), SINGPURWALLA, N. D. (Co-Principal Investigator), TSE, W. T. P. (Co-Principal Investigator), TSUI, K. L. (Co-Principal Investigator), YU, Y. (Co-Principal Investigator), YUEN, K. K. R. (Co-Principal Investigator), CHAN, A. B. (Co-Investigator), CHAN, N.-H. (Co-Investigator), CHIN, K. S. (Co-Investigator), CHOW, H. A. (Co-Investigator), Chow, W. K. (Co-Investigator), EDESESS, M. (Co-Investigator), GOLDSMAN, D. M. (Co-Investigator), Huang, J. (Co-Investigator), LEE, W. M. (Co-Investigator), LI, L. (Co-Investigator), LI, C. L. (Co-Investigator), LING, M. H. A. (Co-Investigator), LIU, S. (Co-Investigator), MURAKAMI, J. (Co-Investigator), NG, S. Y. S. (Co-Investigator), NI, M. C. (Co-Investigator), TAN, M.H.-Y. (Co-Investigator), Wang, W. (Co-Investigator), Wang, J. (Co-Investigator), WONG, C. K. (Co-Investigator), WONG, S. Y. Z. (Co-Investigator), WONG, S. C. (Co-Investigator), Xu, Z. (Co-Investigator), ZHANG, Z. (Co-Investigator), Zhang, D. (Co-Investigator), ZHAO, J. L. (Co-Investigator) & Zhou, Q. (Co-Investigator)

    1/01/16 → 31/12/21

    Project: Research

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