On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment with rebalancing
Name
1006509328-MIT.pdf
Description
Full printable version
Size
6.02 MB
Format
Adobe PDF
Checksum (MD5)
bb06031d8539a650f721624f8d59ff9f
Author(s)
Wallar, Alexander James
Advisor(s)
Daniela L. Rus.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
On-demand ride-sharing systems with autonomous vehicles have the potential to enhance the efficiency and reliability of urban mobility. However, existing ride-sharing algorithms are unable to accommodate high capacity vehicles and do not incorporate future predicted demand. This thesis presents a real-time method for high-capacity ride-sharing that scales to a large number of passengers and trips, dynamically generates optimal routes with respect to online demand and vehicle locations, and incorporates predictions of anticipated requests to improve the performance of a network of taxis. We experimentally assess the trade off between fleet size, capacity, waiting time, travel delay, and amount of predictions for low to medium capacity vehicles. We validated the algorithm with over three million taxi rides from the New York City taxi dataset and demonstrate that our approach can service nearly 99% of Manhattan taxi demand using a fleet of only 3000 vehicles (less than 25% of the active taxis in Manhattan).
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 61-64).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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