Optimizing matching radius for ride-hailing systems with dual-replay-buffer deep reinforcement learning

The matching radius, defined as the maximum pick-up distance within which waiting riders and idle drivers can be matched, is a critical variable in ride-hailing systems. Optimizing the matching radius can significantly enhance system performance, but determining its optimal value is challenging due to the dynamic nature of ride-hailing environments. The matching radius should adapt to spatial and temporal variations, as well as to real-time fluctuations in supply and demand. To address this challenge, this paper proposes a dual-reply-buffer deep reinforcement learning method for dynamic matching radius optimization. By modeling the matching radius optimization problem as a Markov decision process, the method trains a policy network to adaptively adjust the matching radius in response to changing conditions in the ride-hailing system, thereby improving efficiency and service quality. We validate our method using real-world ride-hailing data from Austin, Texas. Experimental results show that the proposed method outperforms baseline approaches, achieving higher matching rates, shorter average pick-up distances, and better driver utilization across different scenarios. • Markov decision process modeling for ride-hailing matching radius optimization problem. • A dual-replay-buffer deep reinforcement learning algorithm for optimizing matching radius. • A novel neural network for generating efficacious matching radius in ride-hailing systems. • A simulation that provides a realistic depiction of the ride-hailing market in Austin, Texas.