A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction

Guanyao Li*, Xiaofeng Wang, Gunarto Sindoro Njoo, Shuhan Zhong, S. H.Gary Chan, Chih Chieh Hung, Wen Chih Peng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Docked bike systems have been widely deployed in many cities around the world. To the service provider, predicting the demand and supply of bikes at any station is crucial to offering the best service quality. The docked bike prediction problem is highly challenging because of the complicated joint spatial-temporal (ST) dependency as bikes are picked up and dropped off, the so-called 'flows', between stations. Prior works often considered the spatial and temporal dependencies separately using sequential network models, and based on locality assumptions. Without sufficiently capturing the joint spatial and temporal features, these approaches are not optimal for attaining the best prediction accuracy. We propose STGNN-DJD, a novel data-driven Spatial-Temporal Graph Neural Network to solve the bike demand and supply prediction problem by unifiedly embedding the Dynamic and Joint ST Dependency in two novel ST graphs. Given station locations and historical rental data on bike flow over the past time slots 0 to t-1, we seek to predict online the bike demand and supply at any station at time t. To extract joint spatial-temporal dependency, STGNN-DJD employs a graph generator to construct, at the beginning of time t, two graphs that embed the flow relationships between stations at various time slots (flow-convoluted graph) and dynamic demand-supply pattern correlation between stations (pattern correlation graph), respectively. Given the two spatial-temporal graphs, STGNN-DJD subsequently employs a graph neural network with novel flow-based and attention-based aggregators to generate embedding of each station for docked bike prediction. We have conducted extensive experiments on two large bike-sharing datasets. Our re-sults confirm the effectiveness of STGNN-DJD as compared with other state-of-the-art approaches, with significant improvement on RMSE and MAE (by 20%-50%). We also provide a case study on dynamic dependencies between stations and demonstrate that the locality assumption does not always hold for a docked bike system.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 38th International Conference on Data Engineering, ICDE 2022
PublisherIEEE Computer Society
Pages713-726
Number of pages14
ISBN (Electronic)9781665408837
DOIs
StatePublished - 2022
Event38th IEEE International Conference on Data Engineering, ICDE 2022 - Virtual, Online, Malaysia
Duration: 9 May 202212 May 2022

Publication series

NameProceedings - International Conference on Data Engineering
Volume2022-May
ISSN (Print)1084-4627

Conference

Conference38th IEEE International Conference on Data Engineering, ICDE 2022
Country/TerritoryMalaysia
CityVirtual, Online
Period9/05/2212/05/22

Keywords

  • bike demand and supply prediction
  • spatial-temporal data prediction
  • spatial-temporal graph neural network

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