Deep Graph Convolutional Networks for Incident-Driven Traffic Speed Prediction

Qinge Xie, Tiancheng Guo, Yang Chen, Yu Xiao, Xin Wang, Ben Zhao

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

1 Citation (Scopus)

Abstract

Accurate traffic speed prediction is an important and challenging topic for transportation planning. Previous studies on traffic speed prediction predominately used spatio-temporal and context features for prediction. However, they have not made good use of the impact of traffic incidents. In this work, we aim to make use of the information of incidents to achieve a better prediction of traffic speed. Our incident-driven prediction framework consists of three processes. First, we propose a critical incident discovery method to discover traffic incidents with high impact on traffic speed. Second, we design a binary classifier, which uses deep learning methods to extract the latent incident impact features. Combining above methods, we propose a Deep Incident-Aware Graph Convolutional Network (DIGC-Net) to effectively incorporate traffic incident, spatio-temporal, periodic and context features for traffic speed prediction. We conduct experiments using two real-world traffic datasets of San Francisco and New York City. The results demonstrate the superior performance of our model compared with the competing benchmarks.
Original languageEnglish
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherACM
Pages1665-1674
Number of pages10
ISBN (Electronic)9781450368599
DOIs
Publication statusPublished - 19 Oct 2020
MoE publication typeA4 Article in a conference publication
EventACM International Conference on Information and Knowledge Management - Virtual, Online, Ireland
Duration: 19 Oct 202023 Oct 2020
Conference number: 29

Conference

ConferenceACM International Conference on Information and Knowledge Management
Abbreviated titleCIKM
Country/TerritoryIreland
CityVirtual, Online
Period19/10/202023/10/2020

Keywords

  • traffic speed prediction
  • deep learning

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