The need for more effective car park management in public areas like healthcare facilities, shopping malls and office buildings has been brought to light as a result of the rise in the number of automobiles in metropolitan areas. In order to optimize parking utilization and reduce traffic jams, this study combines dynamic pricing with real-time parking data. The practice of adjusting a product's or service's price in response to market trends is known as dynamic pricing. During both off-peak and peak hours, this strategy has the potential to manage vehicle traffic in the parking space. The dynamic pricing method has the ability to set the price of the parking fee to be higher during peak hours and lower during off-peak hours. This paper proposes a technique known as deep reinforcement learning-based dynamic pricing (DRL-DP). On an hourly basis, dynamic pricing is divided into episodes and shifted back and forth. Pricing control is seen as an incentive based on profits and parking utilization rates. In the context of a competitive parking market around the parking area, the simulation output demonstrates that the proposed solution is plausible and efficient.
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