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MGC-GAN: Multi-Graph Convolutional Generative Adversarial Networks for Accurate Citywide Traffic Flow Prediction
conference contribution
posted on 2023-02-21, 01:47 authored by L Li, J Bi, K Yang, F Luo, Luxing YangLuxing YangAccurate citywide traffic flow prediction is of great importance to intelligent transportation system. Existing methods typically assume the complete citywide traffic data can be obtained in real-time, which is impossible in applications. Furthermore, many recent works only consider one single kind of spatial correlation in traffic network when building graph representations. This work proposes an adversarial learning framework named Multi-Graph Convolutional Generative Adversarial Networks (MGC-GAN) to address the aforementioned challenges. To generate citywide traffic flow predictions using limited traffic data, we construct three kinds of graphs using easily accessed geographical and semantic information to model the complex spatial correlations in citywide transportation networks. Following that, a parallel GCN layer is designed to separately process multiple graphs. In addition, we design the Parallel Graph Convolution and Temporal Convolution Module (PGTCM) to effectively capture the heterogeneous spatial-temporal dependencies. Extensive experiments are carried out on two citywide traffic datasets, demonstrating that MGC-GAN outperforms several state-of-the-art baseline methods.
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Volume
2022-OctoberPagination
2557-2562Location
Prague, Czech RepublicPublisher DOI
Start date
2022-10-09End date
2022-10-12ISSN
1062-922XISBN-13
9781665452588Language
EnglishPublication classification
E1 Full written paper - refereedTitle of proceedings
SMC 2022 : Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 2022Event
IEEE International Conference on Systems, Man, and Cybernetics. (2022 : Prague, Czech Republic)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
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