A Smart Mobility Framework for Optimizing Electric Vehicle Integration in Urban Transportation Networks

Abstract

The rapid adoption of electric vehicles (EVs) presents both opportunities and challenges for urban transportation networks, particularly in terms of energy management, traffic efficiency, and infrastructure planning. This paper proposes a smart mobility framework for optimizing electric vehicle integration within urban transportation systems by leveraging intelligent transportation systems (ITS), Internet of Things (IoT) technologies, and data-driven optimization techniques. The framework integrates real-time traffic data, EV charging demand, grid conditions, and mobility patterns to enable coordinated decision-making across transportation and energy networks. The proposed approach employs predictive analytics and optimization algorithms to improve charging station placement, charging scheduling, and route planning for EVs, thereby reducing congestion, charging delays, and peak load stress on the power grid. Vehicle-to-Infrastructure (V2I) and Vehicle-to-Grid (V2G) communication mechanisms are incorporated to enhance system responsiveness and support bidirectional energy exchange. Additionally, the framework enables adaptive traffic management strategies that prioritize EVs while maintaining overall network efficiency. Simulation-based evaluation in a representative urban scenario demonstrates that the proposed framework significantly improves traffic flow, reduces energy consumption, and enhances charging infrastructure utilization compared to conventional EV integration methods. The results highlight improvements in travel time reliability, grid stability, and overall system sustainability. By providing a scalable and interoperable solution, the proposed smart mobility framework supports the seamless integration of EVs into urban transportation networks and contributes to the development of sustainable, energy-efficient, and intelligent.