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Graph Convolutional Networks for Road Networks

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Thomas Dyhre NielsenAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 460 - 463

https://doi.org/10.1145/3347146.3359094

Published: 05 November 2019 Publication History

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Abstract

The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks.

We introduce the Relational Fusion Network (RFN), a novel type of GCN designed specifically for road networks. In particular, we propose methods that substantially outperform state-of-the-art GCNs on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs fail to effectively leverage road network structure on these tasks.

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Cited By

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Cao YLi LChen XXu XHuang ZYu YSerra ESpezzano F(2024)Hypergraph Hash Learning for Efficient Trajectory Similarity ComputationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679555(175-186)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679555
Zhang YWang XWang PWang BZhou ZWang Y(2024)Modeling Spatio-Temporal Mobility Across Data Silos via Personalized Federated LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.345365723:12(15289-15306)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3453657
Zhang YWang PWang BWang XZhao ZZhou ZBai LWang Y(2024)Adaptive and Interactive Multi-Level Spatio-Temporal Network for Traffic ForecastingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339297525:10(14070-14086)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3392975
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Index Terms

Graph Convolutional Networks for Road Networks
1. Applied computing
  1. Operations research
    1. Transportation
2. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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Published In

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

View all

Cao YLi LChen XXu XHuang ZYu YSerra ESpezzano F(2024)Hypergraph Hash Learning for Efficient Trajectory Similarity ComputationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679555(175-186)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679555
Zhang YWang XWang PWang BZhou ZWang Y(2024)Modeling Spatio-Temporal Mobility Across Data Silos via Personalized Federated LearningIEEE Transactions on Mobile Computing10.1109/TMC.2024.345365723:12(15289-15306)Online publication date: Dec-2024
https://doi.org/10.1109/TMC.2024.3453657
Zhang YWang PWang BWang XZhao ZZhou ZBai LWang Y(2024)Adaptive and Interactive Multi-Level Spatio-Temporal Network for Traffic ForecastingIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.339297525:10(14070-14086)Online publication date: Oct-2024
https://doi.org/10.1109/TITS.2024.3392975
Huang HHu ZWang YLu ZWen X(2024)Intersec2vec-TSC: Intersection Representation Learning for Large-Scale Traffic Signal ControlIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.334015325:7(7044-7056)Online publication date: Jul-2024
https://doi.org/10.1109/TITS.2023.3340153
Bilotta SBonsignori VNesi P(2024)High Precision Traffic Flow Reconstruction via Hybrid MethodIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332954425:5(4066-4076)Online publication date: May-2024
https://doi.org/10.1109/TITS.2023.3329544
Ji QWen XJin JZhu YLv Y(2024)Urban Traffic Control Meets Decision Recommendation System: A Survey and PerspectiveIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2024.12465911:10(2043-2058)Online publication date: Oct-2024
https://doi.org/10.1109/JAS.2024.124659
Wang WXu HTan G(2024)InterCoop: Spatio-Temporal Interaction Aware Cooperative Perception for Networked Vehicles2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610188(14443-14449)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610188
Qin YGao CTu ZWu HWei SWang YZhang LLi YRenz MNascimento M(2023)Modeling Multi-Grained User Preference in Location VisitationProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625628(1-10)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625628
Zheng YSu HDing JJin DLi YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)Road Planning for Slums via Deep Reinforcement LearningProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599901(5695-5706)Online publication date: 6-Aug-2023
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Meyer EBrenner MZhang BSchickert MMusani BAlthoff M(2023)Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric2023 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV55152.2023.10186741(1-8)Online publication date: 4-Jun-2023
https://doi.org/10.1109/IV55152.2023.10186741
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