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GMGCN: Gated Memory Graph Convolutional Network for Passenger Demand Prediction

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Web Information Systems Engineering – WISE 2021 (WISE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13080))

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Abstract

With the rapid proliferation of smart ways to travel, the accurate prediction of passenger demand in urban areas has become an important task. Existing methods mainly focus on the modeling of Euclidean correlation between spatial neighborhoods. However, in regional-level prediction tasks, the spatial Euclidean correlation can no longer satisfy the requirement for accurate prediction due to the inability to effectively capture spatial information. To solve such difficulty, we design a novel gated-memory graph convolutional network (GMGCN) for passenger demand prediction. Specifically, we focus on modeling the non-Euclidean spatial correlation between regions. The non-Euclidean correlation can not only model the spatial distances of each region, but also construct long-range information flow caused by transportation. Moreover, by adding gated temporal convolution and long-term memory modules, GMGCN can better model the relationship between different time periods and avoid information loss in the recurrent neural network, which boosts the GMGCN’s ability to capture cyclical changes in passenger demand over time. Besides, the impact of the external features like weather and holidays that change over time are integrated into the model by an embedding module. Extensive experiments on real datasets quantitatively and qualitatively demonstrate the superiority of our approach compared with the state-of-the-art.

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Acknowledgments

This work was supported by the National Key R&D Program of China under Grant No. 2018AAA0101204, the National Natural Science Foundation of China (NSFC) under Grant No. 61772491, and Anhui Initiative in Quantum Information Technologies under Grant No. AHY150300.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Tianyuan Bi, Kai Han & Cheng Shen

  2. Suzhou Institute for Advanced Research, No. 188, Ren’Ai Road, Suzhou Industrial Park, Suzhou, 215123, Jiangsu, People’s Republic of China

    Tianyuan Bi, Kai Han & Cheng Shen

Authors
  1. Tianyuan Bi
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  2. Kai Han
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  3. Cheng Shen
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Corresponding author

Correspondence to Kai Han .

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Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Wenjie Zhang

  2. Peking University, Beijing, China

    Lei Zou

  3. Zayed University, Dubai, United Arab Emirates

    Zakaria Maamar

  4. Swinburne University of Technology, Melbourne, VIC, Australia

    Lu Chen

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Bi, T., Han, K., Shen, C. (2021). GMGCN: Gated Memory Graph Convolutional Network for Passenger Demand Prediction. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13080. Springer, Cham. https://doi.org/10.1007/978-3-030-90888-1_27

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  • DOI: https://doi.org/10.1007/978-3-030-90888-1_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90887-4

  • Online ISBN: 978-3-030-90888-1

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