Smart parking and traffic congestion control using optimization

Abstract

This research looks into the best ways to allocate parking spaces for vehicles with emission classifications in order to maximize riding and walking distances, petrol efficiency, and CO2 emissions. The strategies examined are Conventional, Uncontrolled, Balanced, and Eco-friendly. This research presents an environmentally friendly method that uses the Artificial Bee Colony (ABC) Optimization and Whale Optimization Algorithm (WOA) to incorporate car emission classifications into the parking allocation system. The findings show that the Eco-nice strategy significantly reduces CO2 emissions and fuel intake, outperforming alternative methodologies. It does this by reducing walking distances and altering the lengths at which electric powered vehicles (EV) and hybrid electric powered Vehicles (HEVs) are used. It performs admirably at parking occupancy prices as high as 60%; but, as costs rise, resource scarcity makes allocation increasingly challenging. The analysis also looks at how specific EV/HEV penetration rates affect how people use common spaces; it finds that, typical with sustainability goals, walking distances slightly increase while riding distances slightly decrease as these fees rise. The results demonstrate that the environmentally friendly approach simultaneously improves customer satisfaction and operational effectiveness and advances environmental sustainability in town parking areas. The results of the check indicate that a solid framework for realistic parking manipulation is presented by integrating modern optimization tactics like ABC and WOA with an eco-friendly approach, which reduces emissions and placement site visitor congestion in towns. This study provides helpful data on how to support future research to extend and improve these tactics to healthier multiple car classes and larger parking areas, so supporting significant efforts towards sustainable city improvement.