Spatio-Temporal Digraph Convolutional Network Based Taxi Pick-Up Location Recommendation

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Yan Zhang
  • Guojiang Shen
  • Xiao Han
  • Wei Wang
  • Xiangjie Kong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Journal / PublicationIEEE Transactions on Industrial Informatics
Publication statusOnline published - 10 Jun 2022

Abstract

The recommendation of taxi pick-up locations plays an important role for drivers in carrying passengers efficiently. In addition, the emergence of the Internet of Vehicles (IoVs) provides technical support for it. However, existing recommendation methods do not model dynamic Global Positioning System (GPS) information well and in real-time. In this paper, we propose a Spatio-Temporal Digraph Convolutional Network (STDCN) model. First of all, the pick-up and drop-off locations are modeled into a directed spatio-temporal graph as input to the model. The correlation between each node is calculated as a unified edge weight based on Grey Relational Analysis (GRA). Then, the STDCN is used for dynamic spatio-temporal feature extraction. Finally, the edge-cloud collaboration framework is adopted to recommend local taxi pick-up locations in real-time. The experimental results show that the proposed method is better than competing methods in terms of effectiveness and efficiency, and it shows good industrial conversion application prospects.

Research Area(s)

  • Computational modeling, Feature extraction, Informatics, Internet of vehicles, pick-up location recommendation, Public transportation, real-time, Real-time systems, Roads, spatio-temporal digraph convolutional network, Trajectory