Incentive-Based Peak Demand Regulation with Intelligent Parking Management for Enhanced Sustainability

Urban parking facilities often experience severe peak-period congestion, resulting in delays, fuel consumption, and emissions. This paper develops an incentive-based intelligent parking management system to address the challenges of peak demand by encouraging drivers with flexible schedules to shift their parking from peak to off-peak times. The proposed incentive model regulates peak demand, which has been calibrated using historical data on parking demand and occupancy. The model incorporates empirically derived behavioral parameters (from field surveys) to capture drivers’ sensitivity to incentives. The system’s performance is evaluated via discrete-time simulation using real-world parking data from a Japanese supermarket, considering both weekday and weekend demand patterns. The incentive mechanism redistributed approximately 6% of the total parking demand from peak to off-peak periods, markedly reducing peak congestion. This demand shift resulted in substantial sustainability benefits: CO2 emissions decreased by approximately 21% on weekdays (19.5% on weekends), and fuel consumption decreased by about 25% on weekdays (28% on weekends) compared to a baseline scenario without incentives. The prioritizing of electric cars (EVs) and hybrid electric vehicles (HEVs) significantly enhanced emission reductions by promoting cleaner vehicles in the allocation process. This behavioral demand-management strategy offers a practical and scalable solution to enhance urban mobility and sustainability, demonstrating how modest incentives can yield substantial benefits in terms of traffic flow and emissions mitigation.