Full-scale spatio-temporal traffic flow estimation for city-wide networks: a transfer learning based approach

Yuan Zhang, Qixiu Cheng, Yang Liu, Zhiyuan Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)

Abstract

The full-scale spatio-temporal traffic flow estimation/prediction has always been a hot spot in transportation engineering. The low coverage rate of detectors in transport networks brings difficulties to the city-wide traffic flow estimation/prediction. Moreover, it is difficult for traditional analytical traffic flow models to deal with the traffic flow estimation/prediction problem over urban transport networks in a complex environment. Current data-driven methods mainly focus on road segments with detectors. An instance-based transfer learning method is proposed to estimate network-wide traffic flows including road segments without detectors. Case studies based on simulation data and empirical data collected from the open-source PeMS database are conducted to verify its effectiveness. For the traffic flow estimation of segments without detectors, the mean absolute percentage error (MAPE) is approximately 11% for both datasets, which is superior to the existing methods in the literature and reduces MAPE by two percentage points.

Original languageEnglish
Pages (from-to)869-895
Number of pages27
JournalTransportmetrica B
Volume11
Issue number1
DOIs
Publication statusPublished - Jan 2023

Keywords

  • clustering ensemble algorithm
  • Gaussian process
  • link relevance
  • transfer learning method
  • Transport network flow estimation

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Transportation

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