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Abstract
This research proposes a deep reinforcement learning-based dynamic pricing (DRL-DP) model to optimize parking utilization and reduce traffic congestion in urban areas. The model uses real-time parking data and adjusts prices based on demand, aiming to maximize revenue and improve parking space efficiency. Simulations show the model's effectiveness in competitive parking markets.
Publisher
Applied Sciences
Published On
Jan 05, 2023
Authors
Van-Hai Bui, Sina Zarrabian, Paul Kump, Zhe Li, Poh, Tee Connie, Thian Song Ong, Michael Kah, Ong Goh
Tags
dynamic pricing
deep reinforcement learning
parking optimization
traffic congestion
urban areas
real-time data
revenue maximization

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