Artificial Intelligence-Driven Traffic Congestion Prediction Using Urban Mobility Data

Matteo Schneider

Artificial Intelligence-Driven Traffic Congestion Prediction Using Urban Mobility Data

Author(s)	Matteo Schneider
Country	Switzerland
Abstract	Rapid urbanization and increasing travel demand have intensified traffic congestion in many metropolitan transportation networks. Accurate short-term congestion prediction can support traffic management, route planning, intelligent transportation systems, and the allocation of road-network resources. Urban mobility data obtained from road sensors, location-enabled vehicles, aggregated movement trajectories, and transportation information systems provide useful representations of changing traffic conditions. However, congestion develops through complex spatial and temporal interactions that may be difficult to represent using conventional forecasting procedures alone. This hypothetical methodological study examines an artificial intelligence-driven framework for predicting traffic congestion using multidimensional urban mobility data. The proposed framework integrates historical traffic speed, traffic-flow density, aggregated vehicle movement patterns, origin-destination intensity, temporal characteristics, and neighboring-road conditions. A conceptual dataset containing 10,000 fifteen-minute mobility observations is divided into hypothetical training, validation, and testing subsets. Three prediction configurations are considered: a historical-pattern baseline, a conventional neural-network model, and an integrated artificial intelligence mobility-fusion model. The illustrative analysis compares the models through accuracy, precision, recall, and F1 score using a hypothetical congestion classification scenario. A second comparison examines prediction quality across fifteen-, thirty-, forty-five-, and sixty-minute forecasting horizons. Under the assigned values, the integrated mobility model demonstrates the strongest hypothetical classification performance, while prediction quality gradually decreases as the forecasting horizon becomes longer. These numerical outcomes are illustrative rather than experimentally observed results. The study concludes that artificial intelligence combined with heterogeneous urban mobility information provides a useful methodological direction for short-term congestion prediction. Nevertheless, reliable implementation requires representative mobility data, temporal validation, careful handling of spatial dependencies, consideration of changing travel behavior, and comparison with appropriate baseline methods. Empirical experimentation is therefore required before practical improvements in congestion forecasting can be established.
Keywords	Artificial Intelligence, Traffic Congestion Prediction, Urban Mobility Data, Traffic Flow Forecasting, Intelligent Transportation Systems, Mobility Analytics, Machine Learning, Traffic Speed Prediction, Urban Computing, Spatiotemporal Prediction.
Field	Engineering
Published In	Volume 1, Issue 3, July-August 2025
Published On	2025-07-14

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Artificial Intelligence-Driven Traffic Congestion Prediction Using Urban Mobility Data

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