Stochastic Weight Completion for Road Networks using Graph Convolutional Networks

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15 Citationer (Scopus)

Abstrakt

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.

OriginalsprogEngelsk
TitelProceedings of 35th IEEE International Conference on Data Engineering, ICDE 2019
Antal sider12
ForlagIEEE
Publikationsdato2019
Sider1274-1285
Artikelnummer8731475
ISBN (Trykt)978-1-5386-7475-8
ISBN (Elektronisk)978-1-5386-7474-1
DOI
StatusUdgivet - 2019
BegivenhedThe 35th IEEE International Conference on Data Engineering (ICDE) - Macau, Macau, Kina
Varighed: 8 apr. 201912 apr. 2019

Konference

KonferenceThe 35th IEEE International Conference on Data Engineering (ICDE)
LokationMacau
LandKina
ByMacau
Periode08/04/201912/04/2019
NavnProceedings of the International Conference on Data Engineering
ISSN1063-6382

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  • Publikation

    • 15 Citationer
    • 1 Ph.d.-afhandling

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

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

    Publikation: Bog/antologi/afhandling/rapportPh.d.-afhandlingForskning

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    Citationsformater

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