Deep Reinforcement Learning-Based Dynamic Pricing for Parking Solutions

Abstract

The growth in the number of automobiles in metropolitan areas has drawn attention to the need for more efficient carpark control in public spaces such as healthcare, retail stores, and office blocks. In this research, dynamic pricing is integrated with real-time parking data to optimise parking utilisation and reduce traffic jams. Dynamic pricing is the practice of changing the price of a product or service in response to market trends. This approach has the potential to manage car traffic in the parking space during peak and off-peak hours. The dynamic pricing method can set the parking fee at a greater price during peak hours and a lower rate during off-peak times. A method called deep reinforcement learning-based dynamic pricing (DRL-DP) is proposed in this paper. Dynamic pricing is separated into episodes and shifted back and forth on an hourly basis. Parking utilisation rates and profits are viewed as incentives for pricing control. The simulation output illustrates that the proposed solution is credible and effective under circumstances where the parking market around the parking area is competitive among each parking provider.