Abstract
The fusion of electricity, automation, and sharing is forming a new Autonomous Mobility-on-Demand (AMoD) system in current urban transportation, in which the Shared Autonomous Electric Vehicles (SAEVs) are a fleet to execute delivery, parking, recharging, and repositioning tasks automatically. To model the decision-making process of AMoD system and optimize multiaction dynamic dispatching of SAEVs over a long horizon, the dispatching problem of SAEVs is modeled according to Markov Decision Process (MDP) at first. Then two optimization models from short-sighted view and farsighted view based on combinatorial optimization theory are built, respectively. The former focuses on the instant and single-step reward, while the latter aims at the accumulative and multistep return. After that, the Kuhn–Munkres algorithm is set as the baseline method to solve the first model to achieve optimal multiaction allocation instructions for SAEVs, and the combination of deep Q-learning algorithm and Kuhn–Munkres algorithm is designed to solve the second model to realize the global optimization. Finally, a toy example, a macrosimulation of 1 month, and a microsimulation of 6 hours based on actual historical operation data are conducted. Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of SAEVs.
Highlights
Introduction ree revolutions ofElectrification, Automation, and Sharing are booming in current urban transportation [1].e fusion of L4/L5 level autonomous driving, electric vehicles, and shared mobility mode is forming a new Autonomous Mobility-on-Demand (AMoD) system [2, 3]
Results show that (1) the Kuhn–Munkres algorithm ensures the computational effectiveness in the large-scale real-time application of the AMoD system; (2) the second optimization model considering long-term return can decrease average user waiting time and achieve a 2.78% increase in total revenue compared with the first model; (3) and integrating combinatorial optimization theory with reinforcement learning theory is a perfect package for solving the multiaction dynamic dispatching problem of Shared Autonomous Electric Vehicles (SAEVs)
Few scholars focused on the dispatching problem of SAEVs since it is a new emerging topic in recent years. e most studied area is the static or dynamic relocation problem of the electric car-sharing system based on manned vehicles [12,13,14,15,16,17]
Summary
Introduction ree revolutions ofElectrification, Automation, and Sharing are booming in current urban transportation [1].e fusion of L4/L5 level autonomous driving, electric vehicles, and shared mobility mode is forming a new Autonomous Mobility-on-Demand (AMoD) system [2, 3]. In the AMoD system, SAEVs can automatically pick up and deliver passengers from origin to destination, drive to the nearby charging station/pile for electricity supplement, reposition to hotspots with low vehicle supply and high trip demand, and park on the road waiting for the new assignment [4,5,6]. E most studied area is the static or dynamic relocation problem of the electric car-sharing system based on manned vehicles [12,13,14,15,16,17] These studies almost focus on the pickup and delivery task for users and barely consider the recharging task of electric vehicles and repositioning task of redundant vehicles [18,19,20]. Methods involved in the above studies mainly include nonlinear programming models and solving algorithms with high time complexity, which ignores the computational efficiency in large-scale
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