SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning
Item Type:Conference Paper
Citation:Gueriau, M., & Dusparic, I., SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning, The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018), 2018
ITSC2018.pdf (Accepted for publication (author's copy) - Peer Reviewed) 493.7Kb
Shared mobility-on-demand systems can improve the efficiency of urban mobility through reduced vehicle ownership and parking demand. However, some issues in their implementations remain open, most notably the issue of rebalancing non-occupied vehicles to meet geographically uneven demand, as is, for example, the case during the rush hour. This is somewhat alleviated by the prospect of autonomous mobility-on-demand systems, where autonomous vehicles can relocate themselves; however, the proposed relocation strategies are still centralized and assume all vehicles are a part of the same fleet. Furthermore, ride-sharing is not considered, which also has an impact on rebalancing, as already occupied vehicles can also potentially be available to serve new requests simultaneously. In this paper we propose a reinforcement learning-based decentralized approach to vehicle relocation as well as ride request assignment in shared mobility-on-demand systems. Each vehicle autonomously learns its behaviour, which includes both rebalancing and selecting which requests to serve, based on its local current and observed historical demand. We evaluate the approach using data on taxi use in New York City, first serving a single request by a vehicle at a time, and then introduce ride-sharing to evaluate its impact on the learnt rebalancing and assignment behaviour.
Other Titles:The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018).
Type of material:Conference Paper
Availability:Full text available
Subject (TCD):Smart & Sustainable Planet