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Trajectory Forecasting for Human Mobility Considering Movement Patterns and the Heterogeneous Effects of Geographical Environments via Potential Fields

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  • Kaiqi Chen

    (Department of Geo-Informatics, Central South University, Changsha 410017, China)

  • Pingting Zhou

    (Department of Geo-Informatics, Central South University, Changsha 410017, China)

  • Jingyi Liu

    (The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China)

  • Min Deng

    (Department of Geo-Informatics, Central South University, Changsha 410017, China)

  • Qi Guo

    (The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China)

  • Chen Yao

    (The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China)

  • Jinyong Chen

    (The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China)

  • Xinyu Pei

    (The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China)

Abstract

Trajectory forecasting for human mobility plays a critical role in the effective management and sustainable development of urban transportation, which aligns with the advocacy of Sustainable Development Goals (SDGs). Although several approaches have been developed in other trajectory forecasting applications, such as autonomous driving and intelligent robotics, there remain limitations in forecasting trajectories of human mobility. This is because they do not adequately consider the prior knowledge of human movement patterns and the heterogeneous effects of geographical environments. Therefore, in this study, we propose an environment-driven trajectory forecasting method that can adapt to distinct movement patterns. First, the indicator systems, which systematically summarize the heterogeneous effects of different environmental factors on human mobility, are, respectively, constructed for the convergence, divergence, and leadership patterns. Then, based on the corresponding indicator system, the potential field is generated, representing the calibrated probability of the human mobility direction under the environmental effects. A gradient descent algorithm is finally employed on the potential field to forecast the next-step mobility location. Extensive experiment results demonstrated the satisfactory performance of our proposed method under different movement patterns. Compared to other baselines, our proposed method also shows advantages in both long-term and real-time forecasting.

Suggested Citation

  • Kaiqi Chen & Pingting Zhou & Jingyi Liu & Min Deng & Qi Guo & Chen Yao & Jinyong Chen & Xinyu Pei, 2025. "Trajectory Forecasting for Human Mobility Considering Movement Patterns and the Heterogeneous Effects of Geographical Environments via Potential Fields," Sustainability, MDPI, vol. 17(4), pages 1-31, February.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1483-:d:1588770
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    References listed on IDEAS

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    1. Maria Giannoulaki & Zoi Christoforou, 2024. "Pedestrian Walking Speed Analysis: A Systematic Review," Sustainability, MDPI, vol. 16(11), pages 1-19, June.
    2. Liming Shao & Meining Ling & Ying Yan & Guangnian Xiao & Shiqi Luo & Qiang Luo, 2024. "Research on Vehicle-Driving-Trajectory Prediction Methods by Considering Driving Intention and Driving Style," Sustainability, MDPI, vol. 16(19), pages 1-15, September.
    3. Abdallah Abuaisha & Sameer Abu-Eisheh, 2023. "Optimization of Urban Public Transportation Considering the Modal Fleet Size: A Case Study from Palestine," Sustainability, MDPI, vol. 15(8), pages 1-14, April.
    4. Rocio de la Torre & Canan G. Corlu & Javier Faulin & Bhakti S. Onggo & Angel A. Juan, 2021. "Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications," Sustainability, MDPI, vol. 13(3), pages 1-21, February.
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