From data to decisions in urban transit and logistics

Urban transit and city logistics have undergone major changes in recent years, including increased peak congestion, shrinking mass transit ridership, and the introduction of ride-sharing and micro-mobility platforms. At the same time, widespread data collection offers transit agencies insight into their riders in unprecedented detail. In this setting, data has the potential to inform decision-making and make meaningful impact on problems of great public interest. This thesis concerns data-driven decision-making for public transit systems, and spans topics from demand estimation to the design and operation of fixed-route systems and paratransit. The first chapter is concerned with origin-destination demand estimation for public transit. Our aim is to estimate demand using aggregated station entrance and exit counts, which can be modeled as the problem of recovering a matrix from its row and column sums.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Yan, Julia(Julia Y.)

Advisor dc:contributor.advisor

• Dimitris Bertsimas.

• Operations Research Center.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127296

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127296