Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management
This addresses fleet management challenges for SAV companies, but appears incremental as it builds on existing RL research without introducing a new paradigm.
The study tackled the problem of negative externalities like excessive vehicle miles traveled in Shared Autonomous Vehicle fleets by exploring reinforcement learning approaches to minimize rider waiting time, parking cost, and empty travel, but did not report specific numerical results.
Shared Automated Vehicles (SAVs) Fleets companies are starting pilot projects nationwide. In 2020 in Fairfax Virginia it was announced the first Shared Autonomous Vehicle Fleet pilot project in Virginia. SAVs promise to improve quality of life. However, SAVs will also induce some negative externalities by generating excessive vehicle miles traveled (VMT), which leads to more congestions, energy consumption, and emissions. The excessive VMT are primarily generated via empty relocation process. Reinforcement Learning based algorithms are being researched as a possible solution to solve some of these problems: most notably minimizing waiting time for riders. But no research using Reinforcement Learning has been made about reducing parking space cost nor reducing empty cruising time. This study explores different \textbf{Reinforcement Learning approaches and then decide the best approach to help minimize the rider waiting time, parking cost, and empty travel