Stochastic Weight Completion for Road Networks using Graph Convolutional Networks

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

18 Citations (Scopus)

Abstract

Innovations in transportation, such as mobility-on-demand services and autonomous driving, call for high-resolution routing that relies on an accurate representation of travel time throughout the underlying road network. Specifically, the travel time of a road-network edge is modeled as a time-varying distribution that captures the variability of traffic over time and the fact that different drivers may traverse the same edge at the same time at different speeds. Such stochastic weights may be extracted from data sources such as GPS and loop detector data. However, even very large data sources are incapable of covering all edges of a road network at all times. Yet, high-resolution routing needs stochastic weights for all edges. We solve the problem of filling in the missing weights. To achieve that, we provide techniques capable of estimating stochastic edge weights for all edges from traffic data that covers only a fraction of all edges. We propose a generic learning framework called Graph Convolutional Weight Completion (GCWC) that exploits the topology of a road network graph and the correlations of weights among adjacent edges to estimate stochastic weights for all edges. Next, we incorporate contextual information into GCWC to further improve accuracy. Empirical studies using loop detector data from a highway toll gate network and GPS data from a large city offer insight into the design properties of GCWC and its effectiveness.

Original languageEnglish
Title of host publicationProceedings of 35th IEEE International Conference on Data Engineering, ICDE 2019
Number of pages12
PublisherIEEE
Publication date2019
Pages1274-1285
Article number8731475
ISBN (Print)978-1-5386-7475-8
ISBN (Electronic)978-1-5386-7474-1
DOIs
Publication statusPublished - 2019
EventThe 35th IEEE International Conference on Data Engineering (ICDE) - Macau, Macau, China
Duration: 8 Apr 201912 Apr 2019

Conference

ConferenceThe 35th IEEE International Conference on Data Engineering (ICDE)
LocationMacau
CountryChina
CityMacau
Period08/04/201912/04/2019
SeriesProceedings of the International Conference on Data Engineering
ISSN1063-6382

Keywords

  • Graph completion
  • Graph convolutional neural network
  • Travel cost estimation
  • Travel time prediction
  • Uncertain graph

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  • Research Output

    • 18 Citations
    • 1 Ph.D. thesis

    Managing and Analyzing Big Traffic Data-An Uncertain Time Series Approach

    Hu, J., 7 May 2019, Aalborg Universitetsforlag. 149 p. (Ph.d.-serien for Det Tekniske Fakultet for IT og Design, Aalborg Universitet).

    Research output: Book/ReportPh.D. thesis

    Open Access
    File

    Cite this

    Hu, J., Guo, C., Yang, B., & Jensen, C. S. (2019). Stochastic Weight Completion for Road Networks using Graph Convolutional Networks. In Proceedings of 35th IEEE International Conference on Data Engineering, ICDE 2019 (pp. 1274-1285). [8731475] IEEE. Proceedings of the International Conference on Data Engineering https://doi.org/10.1109/ICDE.2019.00116