Self-Supervised Spatiotemporal Graph Representation for Human Mobility and Trajectory Forecasting in Urban Environments
Keywords:
spatiotemporal graph, self-supervised learning, human mobility, trajectory forecasting, urban infrastructureAbstract
Human mobility prediction plays a critical role in urban planning, transportation management, and public safety. Traditional trajectory forecasting models rely heavily on large amounts of labeled data and are often limited by their inability to generalize across heterogeneous urban environments. This paper proposes a self-supervised spatiotemporal graph representation framework for human mobility and trajectory forecasting. The framework constructs dynamic spatiotemporal graphs from raw trajectory data without requiring explicit human supervision, leveraging contrastive and generative pretext tasks to learn transferable representations. We examine the architectural design choices, including the trade-offs between graph convolution, temporal attention, and modeling of long-range dependencies. The system-level discussion emphasizes deployment scalability, data governance, robustness to distribution shifts, and fairness implications across demographic groups. Cross-domain comparisons with traffic flow prediction, epidemic spread modeling, and social network analysis illustrate the broader applicability of self-supervised graph learning. We further address policy considerations regarding privacy, bias, and infrastructural resilience. The proposed approach demonstrates that self-supervision can reduce labeled data requirements while maintaining predictive accuracy, thereby enabling more sustainable and equitable urban mobility systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
