Artificial Intelligence Approaches for Predictive Traffic Management in Smart Cities

Abstract

Urban traffic congestion is a persistent smart-city problem because demand changes rapidly across space and time while conventional signal plans are frequently based on fixed cycles or delayed measurements. This methodology paper proposes an artificial-intelligence framework that combines multi-source traffic sensing, spatio-temporal forecasting, congestion-risk estimation, and reinforcement-learning-based control. The predictive component integrates recurrent temporal representation with graph-based spatial learning so that traffic speed, flow, and queue conditions can be forecast at connected road segments before congestion becomes critical. The control component converts the predicted state into adaptive signal and route-management actions under constraints for safety, minimum green time, and network stability. The methodology specifies data preparation, model training, validation, simulation-based deployment, comparison baselines, and evaluation metrics. An illustrative experiment shows how the proposed hybrid approach can be assessed against fixed-time control, prediction-only management, and learning-based alternatives. The framework is grounded in traffic-forecasting and intelligent-control research published up to 2020 and is intended as a reproducible design for researchers studying predictive traffic management in smart-city networks.