Dual attentive graph neural network for metro passenger flow prediction

Yuhuan Lu, Hongliang Ding, Shiqian Ji, N. N. Sze, Zhaocheng He

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Metro system has been increasingly recognized as a backbone of urban transportation system in many cities around the world. To improve the demand management and operation efficiency, it is crucial to have accurate prediction of real-time metro passenger flow. However, the forecast performance is often subject to the complex spatial and temporal distributions of the metro passenger flow data. To this end, we developed a novel dual attentive graph neural network that can effectively predict the distribution of metro traffic flow considering the spatial and temporal influences. Specifically, two directed complete metro graphs (i.e., inbound and outbound graphs) and the weighted matrix of them are proposed to characterize the inbound (entering the system) and outbound (leaving the system) passenger flow, respectively. The weighted matrix of inbound graph is estimated based on the historical origin-destination demand and that of the outbound graph is estimated based on the similarity metrics between every two stations. Moreover, to capture the dependencies between inbound and outbound flows, multi-layer graph spatial attention networks that incorporate the spatial context are applied to exploit the dynamic inter-station correlations. Then, the acquired dependency features integrated with external factors, such as weather conditions, are filtered by temporal attention and fed into a sequence decoder to produce short-term and long-term passenger flow predictions. Finally, a series experiments are conducted based on a comprehensive empirical dataset. Findings indicated that the proposed model does not only well predict the metro passenger flow, but also effectively detect the emergencies and incidents of metro system.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Attention mechanism
  • Graph neural network
  • Metro system
  • Passenger flow prediction

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this