Data-Driven Urban Mobility Prediction Using Spatiotemporal Machine Learning and Geographic Information Systems
Keywords:
urban mobility prediction, spatiotemporal machine learning, geographic information systems, intelligent transportation systems, smart city governance, algorithmic fairness, infrastructure sustainability, data-driven modelingAbstract
Urban mobility prediction is a cornerstone of intelligent transportation systems and smart city governance, enabling efficient resource allocation, congestion mitigation, and sustainable infrastructure planning. This paper presents a comprehensive systems-level examination of data-driven urban mobility prediction through the integration of spatiotemporal machine learning models and geographic information systems. The discussion emphasizes architectural trade-offs between model complexity and computational scalability, the governance challenges associated with heterogeneous data sources, and the policy implications of deploying predictive systems at metropolitan scales. A critical analysis of deep learning frameworks, including convolutional long short-term memory networks, graph neural networks, and attention-based architectures, reveals that performance gains must be weighed against interpretability, fairness, and robustness to distributional shifts. The role of geographic information systems as both a data integration platform and a visualization backbone is assessed, highlighting the necessity of standardized spatial data infrastructures and real-time updating protocols. The paper further explores sustainability considerations, such as energy consumption of large-scale model training versus the benefits of reduced traffic emissions, and the equity dimensions of predictive services that may inadvertently marginalize underserved communities. Through case illustrations from cities like Singapore, London, and New York, the study identifies structural trade-offs in algorithm selection, data governance, and system deployment. Forward-looking perspectives call for hybrid architectures that combine physics-informed constraints with data-driven methods, federated learning for privacy preservation, and regulatory frameworks that ensure algorithmic accountability. This work contributes to the interdisciplinary discourse on socio-technical infrastructures by framing urban mobility prediction not merely as a technical optimization problem but as a governance challenge requiring alignment across engineering, policy, and community stakeholders.
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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.
