EVALUATING INTELLIGENT TRAFFIC MANAGEMENT SYSTEMS FOR SMART CITIES: OPPORTUNITIES, LIMITATIONS, AND IMPLEMENTATION

Abstract

The fast growth of urban traffic demands a structured examination of the Intelligent Traffic Management Systems (ITMS) of smart cities and their future, limitations, and difficulties in the application of the systems. The paper looks into the state-of-the-art solutions, including Deep Reinforcement Learning (DRL) for adaptive traffic signal control, Graph Neural Network (GNNs) for traffic prediction, and Edge-Cloud (E-C) architecture with the IoT to run at the edge. A single integrative prediction and control system is developed and tested on a simulation environment of Simulation of Urban Mobility (SUMO). Quantitative data demonstrate that the proposed ITMS has the potential to offer the reduction of the average wait time of a vehicle by 60-70%, an increase in the traffic throughput by 15-25%, and the shortening of the queues by far compared to the fixed time control systems. These innovations highlight the applicability of AI-planned adaptive plans in simplifying the traffic within cities. The evaluation, though, also identifies such drastic limitations as the lack of integrated frameworks, the inability to make decisions that are conscious of uncertainty, scaling problems, and a lack of real-world validation. The paper identifies the need to have efficient, scalable, and deployable ITMS architectures that can bridge the gap between theoretical development and the practical implementation. Overall, the work is a quantitative and comprehensive foundation for the further evolution of intelligent and sustainable traffic management solutions in smart cities.