Traffic congestion prediction by spatiotemporal propagation patterns

Xiaolei Di, Yu Xiao, Chao Zhu, Yang Deng, Qinpei Zhao, Weixiong Rao

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

17 Citations (Scopus)
20 Downloads (Pure)


Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-Term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.

Original languageEnglish
Title of host publicationProceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
Number of pages6
ISBN (Electronic)978-1-7281-3363-8
Publication statusPublished - 1 Jun 2019
MoE publication typeA4 Article in a conference publication
EventMobile Data Management Conference - Hong Kong, Hong Kong
Duration: 10 Jun 201913 Jun 2019
Conference number: 20

Publication series

NameMobile Data Management Conference
ISSN (Print)1551-6245
ISSN (Electronic)2375-0324


ConferenceMobile Data Management Conference
Abbreviated titleMDM
Country/TerritoryHong Kong
CityHong Kong


  • Short term Prediction
  • Spatiotemporal Deep Learning Model
  • Traffic congestion


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