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

Kaiqi Chen, Pingting Zhou, Jingyi Liu (), Min Deng, Qi Guo, Chen Yao, Jinyong Chen and Xinyu Pei
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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

Sustainability, 2025, vol. 17, issue 4, 1-31

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.

Keywords: trajectory forecasting; potential field; human mobility; movement pattern; environmental modeling; heterogeneous effect (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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