Using Deep Reinforcement Learning to Coordinate Multi-Modal Journey Planning with Limited Transportation Capacity
Citation:
Lara Codec? and Vinny Cahill, Using Deep Reinforcement Learning to Coordinate Multi-Modal Journey Planning with Limited Transportation Capacity, SUMO User Conference 2021, Online, September 13-15 2021, 2, 2021, 13-32Abstract:
Multi-modal journey planning for large numbers of simultaneous travellers is a challenging prob-
lem, particularly in the presence of limited transportation capacity. Fundamental trade-offs exist
between balancing the goals and preferences of each traveller and the optimization of the use of
available capacity. Addressing these trade-offs requires careful coordination of travellers’ individual
plans. This paper assesses the viability of Deep Reinforcement Learning (DRL) applied to simulated
mobility as a means of learning coordinated plans. Specifically, the paper addresses the problem of
travel to large-scale events, such as concerts and sports events, where all attendees have as their goal
to arrive on time. Multi-agent DRL is used to learn coordinated plans aimed at maximizing just-
in-time arrival while taking into account the limited capacity of the infrastructure. Generated plans
take account of different transportation modes’ availability and requirements (e.g., parking) as well as
constraints such as attendees’ ownership of vehicles. The results are compared with those of a naive
decision-making algorithm based on estimated travel time. The results show that the learned plans
make intuitive use of the available modes and improve average travel time and lateness, supporting
the use of DRL in association with a microscopic mobility simulator for journey planning.
Sponsor
Grant Number
Marie Curie
713567
Science Foundation Ireland (SFI)
16/SP/3804
Author's Homepage:
http://people.tcd.ie/vjcahillDescription:
PRESENTEDOnline
Author: Cahill, Vinny
Other Titles:
SUMO User Conference 2021Type of material:
Conference PaperCollections
Series/Report no:
2Availability:
Full text availableSubject (TCD):
Smart & Sustainable Planet , Artificial IntelligenceMetadata
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