Network-Wide Link Travel Time and Station Waiting Time Estimation Using Automatic Fare Collection Data: A Computational Graph Approach

Jinlei Zhang, Feng Chen, Lixing Yang, Wei Ma, Guangyin Jin, Ziyou Gao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for public agencies to better understand the performance of the URT system. This paper focuses on an essential and hard problem to estimate the network-wide link travel time and station waiting time using the automatic fare collection (AFC) data in the URT system, which is beneficial to better understanding the system-wide real-time operation state. The emerging data-driven techniques, such as the computational graph (CG) method in the machine learning field, provide a new solution for solving this problem. In this study, we first formulate a data-driven estimation optimization framework to estimate the link travel time and station waiting time. Then, we cast the estimation optimization model into a CG-based framework to solve the optimization problem and obtain the estimation results. The methodology is verified on a synthetic URT network and applied to a real-world URT network using the synthetic and real-world AFC data, respectively. Results show the robustness and effectiveness of the CG-based framework. To the best of our knowledge, this is the first time that the CG is applied to the URT. This study can provide critical insights to better understand the operational state of URT.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • computational graph model
  • link travel time estimation
  • station waiting time estimation
  • Urban rail transit

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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