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Foreseeing private car transfer between urban regions with multiple graph-based generative adversarial networks

World Wide Web volume 25, pages 2515–2534 (2022)Cite this article

Abstract

Private car transfer indicates that people drive private cars and travel between urban regions to perform daily activities. Foreseeing private car transfer between urban regions can facilitate a broad scope of applications ranging from route planning, hot region discovery to urban computing. However, three challenges remain. i) Private car transfer between regions is affected by multiple spatio-temporal correlations. ii) Transfer records are highly sparse and imbalanced. iii) Modeling the stay duration of private cars. In this paper, we model private cars’ travel in urban regions as the spatio-temporal graph and formulate private car transfer foreseeing as the time-evolving adjacency matrix prediction of the graph. To specify, we propose MG-GAN (Multiple Graph-based Generative Adversarial Network) to predict private car transfer. For one thing, we design multi-graph dense convolutions with gated recurrent networks as the generative network to capture multiple spatio-temporal correlations. For another, the attentive multi-graph convolutional network is designed as the discriminative network to learn the stay duration correlations of private cars in each region. The iterative adversarial processes between generating and discriminating networks enhance the MG-GAN’s ability to tackle the sparse data problem. Besides, a topic clustering algorithm based on multi-source data fusion is proposed to balance the fused data. Extensive experiments on the real-world private car and taxi trip datasets demonstrate that MG-GAN performs better than the state-of-the-art baselines.

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Notes

  1. https://github.com/HunanUniversityZhuXiao/PrivateCarTrajectoryData

  2. https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page

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Acknowledgments

This work was supported in part by the Humanities and Social Sciences Foundation of MOE under grant 21YJCZH183, in part by the Key Research and Development Project of Hunan Province of China under Grant 2021GK2020, and in part by the Funding Projects of Zhejiang Lab under grants 2021LC0AB05 and 2020LC0PI01.

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Chenxi Liu, Zhu Xiao, Dong Wang & Jiawei Cai

  2. Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA

    Minhao Cheng

  3. Intelligent Computing Platform Research Center, Zhejiang Lab, Hangzhou, China

    Hongyang Chen

Authors
  1. Chenxi Liu
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  2. Zhu Xiao
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  3. Dong Wang
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  4. Minhao Cheng
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  5. Hongyang Chen
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  6. Jiawei Cai
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Correspondence to Zhu Xiao, Dong Wang or Hongyang Chen.

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This article belongs to the Topical Collection: Special Issue on Computational Aspects of Network Science

Guest Editors: Apostolos N. Papadopoulos and Richard Chbeir

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Liu, C., Xiao, Z., Wang, D. et al. Foreseeing private car transfer between urban regions with multiple graph-based generative adversarial networks. World Wide Web 25, 2515–2534 (2022). https://doi.org/10.1007/s11280-021-00995-z

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  • DOI: https://doi.org/10.1007/s11280-021-00995-z

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