A Mobile Edge Computing Framework for Traffic Optimization at Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern at urban intersections leads to excessive energy use due to frequent vehicle braking, idling, and acceleration. This pattern, amplified by the growing use of automobiles, adversely affects city sustainability, causing delays, pollution, and increased carbon emissions. Addressing this, we introduce a mobile edge computing framework utilizing Internet of Things (IoT) and edge computing. This framework incorporates real-time vehicle-to-infrastructure communication and intelligent speed control algorithms into a mobile app, targeting speed optimization at signalized intersections to alleviate the negative impacts of stop-and-go traffic at urban intersections. The framework comprises three components: a cyberinfrastructure-based messaging system for real-time traffic and signal data from IoT-connected signal controllers and traffic sensors; a speed optimization algorithm generating speed advisories using phone-based sensor data (like GPS and driving directions), signal phase and timing information, and roadside vehicle detector data; and an ad-hoc mobile computing environment turning smartphones into edge devices for hosting the algorithm. This enables intelligent speed advisory along signalized corridors. The paper presents the detailed design and implementation of the proposed framework, highlighting its practicality, utility, and energy-saving potential. Our studies, including traffic simulations, real-vehicle lab experiments, evaluation surveys, and field tests, demonstrate its robustness and effectiveness. Specifically, simulations indicate that using the mobile app universally could lead to a 24% energy reduction in urban transportation systems.