Spatial Community-Informed Evolving Graphs for Demand Prediction

Qianru Wang, Bin Guo, Yi Ouyang, Kai Shu, Zhiwen Yu, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The rapidly increasing number of sharing bikes has facilitated people’s daily commuting significantly. However, the number of available bikes in different stations may be imbalanced due to the free check-in and check-out of users. Therefore, predicting the bike demand in each station is an important task in a city to satisfy requests in different stations. Recent works mainly focus on demand prediction in settled stations, which ignore the realistic scenarios that bike stations may be deployed or removed. To predict station-level demands with evolving new stations, we face two main challenges: (1) How to effectively capture new interactions in time-evolving station networks; (2) How to learn spatial patterns for new stations due to the limited historical data. To tackle these challenges, we propose a novel Spatial Community-informed Evolving Graphs (SCEG) framework to predict station-level demands, which considers two different grained interactions. Specifically, we learn time-evolving representation from fine-grained interactions in evolving station networks using EvolveGCN. And we design a Bi-grained Graph Convolutional Network(B-GCN) to learn community-informed representation from coarse-grained interactions between communities of stations. Experimental results on real-world datasets demonstrate the effectiveness of SCEG on demand prediction for both new and settled stations. Our code is available at https://github.com/RoeyW/Bikes-SCEG

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Proceedings
EditorsYuxiao Dong, Dunja Mladenic, Craig Saunders
PublisherSpringer Science and Business Media Deutschland GmbH
Pages440-456
Number of pages17
ISBN (Print)9783030676698
DOIs
StatePublished - 2021
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020 - Virtual, Online
Duration: Sep 14 2020Sep 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12461 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020
CityVirtual, Online
Period9/14/209/18/20

Keywords

  • Demand prediction
  • Graph neural network
  • Spatial-temporal analysis
  • Urban computing

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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