Self-Supervised Learning for Dynamic Traffic Flow Prediction in Urban Networks
Keywords:
self-supervised learning, traffic flow prediction, urban networks, intelligent transportation systems, graph neural networks, spatiotemporal analytics, urban computing, mobility prediction, smart infrastructure, transportation intelligenceAbstract
Urban transportation systems increasingly depend on predictive intelligence to support congestion mitigation, adaptive signal coordination, infrastructure planning, emergency response, and multimodal mobility management. Traditional supervised traffic prediction frameworks have demonstrated strong performance under stable and data-rich conditions, yet they remain constrained by the extensive labeling requirements, regional transfer limitations, and sensitivity to changing urban dynamics. The emergence of self-supervised learning has introduced a new paradigm for transportation intelligence by enabling predictive models to extract latent structural and temporal representations directly from large-scale unlabeled mobility data. This paper presents a comprehensive systems-oriented examination of self-supervised learning for dynamic traffic flow prediction in urban networks. The study analyzes how representation learning architectures transform heterogeneous transportation observations into generalized predictive knowledge across road segments, sensor infrastructures, and spatiotemporal mobility patterns. Particular attention is devoted to the interaction between graph-based urban topology modeling, temporal sequence learning, multimodal sensing integration, and adaptive forecasting mechanisms operating under evolving environmental conditions. The paper further investigates deployment trade-offs associated with computational scalability, governance, infrastructure resilience, fairness, privacy preservation, and operational sustainability within smart city ecosystems. In addition to reviewing contemporary methodological developments, the study evaluates institutional and policy implications associated with large-scale deployment of predictive mobility intelligence. Cross-domain comparisons with energy systems, telecommunications, and industrial cyber-physical infrastructures are incorporated to contextualize the broader significance of self-supervised urban analytics. The findings suggest that self-supervised learning substantially improves adaptability, transferability, and robustness in traffic prediction environments while simultaneously introducing new challenges related to interpretability, governance accountability, and infrastructure dependence. The paper concludes by outlining future research directions involving foundation transportation models, federated urban intelligence architectures, and integrated city-scale decision ecosystems.
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