City-Wide Traffic Prediction Using Aggregated Mobile Network Activity Data

Abstract

Accurate city-wide traffic prediction is essential for intelligent mobility management in modern urban environments. This work introduces a novel approach that leverages aggregated cellular network activity as a large-scale, infrastructure-independent data source for predicting travel time and speed. We first develop the Network Insight Model, which demonstrates that cellular activity patterns alone can effectively capture city-wide traffic dynamics. We then propose the Integrated Mobility Model, a multimodal fusion architecture that combines cellular network activity with Bluetooth sensor data to further enhance prediction accuracy. Both models incorporate attention mechanisms, enabling interpretable insights into which intersections and road segments most strongly influence traffic conditions. Using real-world datasets from York Region, Canada, we evaluate our models against standard regression baselines and state-of-the-art deep learning approaches. Across multiple metrics, including MAE, RMSE, R2, MAPE, and the Travel Time Index (TTI), our models consistently achieve superior predictive performance while providing meaningful, attention-based explanations of traffic patterns.