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A Deep Gravity model for mobility flows generation

Author

Listed:
  • Filippo Simini

    (University of Bristol, Department of Engineering Mathematics
    The Alan Turing Institute
    Argonne National Laboratory Lemont)

  • Gianni Barlacchi

    (Amazon Alexa)

  • Massimilano Luca

    (Fondazione Bruno Kessler
    Free University of Bolzano)

  • Luca Pappalardo

    (Institute of Information Science and Technologies (ISTI), National Research Council (CNR))

Abstract

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.

Suggested Citation

  • Filippo Simini & Gianni Barlacchi & Massimilano Luca & Luca Pappalardo, 2021. "A Deep Gravity model for mobility flows generation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26752-4
    DOI: 10.1038/s41467-021-26752-4
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    References listed on IDEAS

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    1. Kaixin Zhu & Zhifeng Cheng & Jianghao Wang, 2024. "Measuring Chinese mobility behaviour during COVID-19 using geotagged social media data," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    2. Chen, Yong & Geng, Maosi & Zeng, Jiaqi & Yang, Di & Zhang, Lei & Chen, Xiqun (Michael), 2023. "A novel ensemble model with conditional intervening opportunities for ride-hailing travel mobility estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).
    3. Yang, Yitao & Jia, Bin & Yan, Xiao-Yong & Chen, Yan & Song, Dongdong & Zhi, Danyue & Wang, Yiyun & Gao, Ziyou, 2023. "Estimating intercity heavy truck mobility flows using the deep gravity framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. Tongzheng Pu & Chongxing Huang & Jingjing Yang & Ming Huang, 2023. "Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction," Sustainability, MDPI, vol. 15(13), pages 1-23, July.
    5. Sandra Melo & Rui Gomes & Reza Abbasi & Amílcar Arantes, 2024. "Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems," Sustainability, MDPI, vol. 16(11), pages 1-18, May.
    6. Francesco Scotti & Andrea Flori & Piercesare Secchi & Marika Arena & Giovanni Azzone, 2024. "Heterogeneous drivers of overnight and same-day visits," Papers 2402.05679, arXiv.org.
    7. Xu, Zhihao & Lv, Zhiqiang & Chu, Benjia & Li, Jianbo, 2024. "A Fast Spatial-temporal Information Compression algorithm for online real-time forecasting of traffic flow with complex nonlinear patterns," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    8. Cao, Yu & Hua, Zesu & Chen, Ting & Li, Xiaoying & Li, Heng & Tao, Dingtian, 2023. "Understanding population movement and the evolution of urban spatial patterns: An empirical study on social network fusion data," Land Use Policy, Elsevier, vol. 125(C).

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