Dynamic Pricing for Parking System Using Reinforcement Learning

Abstract

The number of vehicles in urban cities has increased and raised attention towards the need for effective parking lot management in public areas such as hospital, shopping mall and office building. In this study, dynamic pricing is deployed with real time parking information to maximize the parking usage rate and alleviate traffic congestion. Dynamic pricing is a practice of varying the price of product of service reflected by the market conditions. This technique can be used to deal with vehicle flow around the parking area including peak and non-peak hour. During peak hours, the dynamic pricing mechanism will regulate the price of parking fee to a relatively high rate, and vice versa for non-peak hours. Reinforcement Learning (RL) is used in this paper to develop a dynamic pricing model for parking management. Dynamic pricing over time is divided into episodes and shuffled back and forth through an hourly increment. The parking usage rate and traffic congestion rate are regarded as the rewards for price regulation.