Optimizing Vehicle Distributions and Fleet Sizes for Shared Mobility-on-Demand
Name
wallar-icra-fleet-2019.pdf
Description
Accepted version
Size
521.05 KB
Format
Adobe PDF
Checksum (MD5)
c457beb13def3e84cfaf3d68558c98c4
Author(s)
Wallar, Alexander James
Alonso-Mora, Javier
Rus, Daniela
Date Issued
August 2019
Journal
2019 International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Wallar, Alex et al. “Optimizing Vehicle Distributions and Fleet Sizes for Shared Mobility-on-Demand.” 2019 International Conference on Robotics and Automation, May 2019, Montreal, Canada, Institute of Electrical and Electronics Engineers, August 2019. © 2019 The Author(s)
Version
Author's final manuscript
Abstract
Mobility-on-demand (MoD) systems are revolutionizing urban transit with the introduction of ride-sharing. Such systems have the potential to reduce vehicle congestion and improve accessibility of a city's transportation infrastructure. Recently developed algorithms can compute routes for vehicles in real-time for a city-scale volume of requests while allowing vehicles to carry multiple passengers at the same time. However, these algorithms focus on optimizing the performance for a given fleet of vehicles and do not tell us how many vehicles are needed to service all the requests. In this paper, we present an offline method to optimize the vehicle distributions and fleet sizes on historical demand data for MoD systems that allow passengers to share vehicles. We present an algorithm to determine how many vehicles are needed, where they should be initialized, and how they should be routed to service all the travel demand for a given period of time. Evaluation using 23,529,740 historical taxi requests from one month in Manhattan shows that on average 2864 four passenger vehicles are needed to service all of the taxi demand in a day with an average added travel delay of 2.8 mins.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA.2019.8793685
