SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning
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2018Access:
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Gueriau, M., & Dusparic, I., SAMoD: Shared Autonomous Mobility-on-Demand using Decentralized Reinforcement Learning, The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018), 2018Abstract:
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.
Author's Homepage:
http://people.tcd.ie/duspariDescription:
PUBLISHED
Author: Dusparic, Ivana; Guériau, Maxime
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The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC2018).Type of material:
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Urban mobility, Autonomous vehicles, Mobility-on-demandSubject (TCD):
Smart & Sustainable PlanetMetadata
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