Behavioral dynamics of public transit ridership in Chicago and impacts of COVID-19

Public transportation ridership analysis in the United States has traditionally centered around the tracking and reporting of the count of trips taken on the system. Such analysis is valuable but incomplete. This work presents a ridership analysis framework that keeps the rider, rather than the trip, as the fundamental unit of analysis, aiming to demonstrate to transit agencies how to leverage data sources already available to them in order to capture the various behavior patterns existing on their transit network and the relative prevalence of each at any given moment and over time. In examining year over year changes as well as the impacts of the COVID-19 pandemic on ridership, this analysis highlights the complex landscape of behaviors underlying trip counts. It keeps riders' mobility patterns and needs as the focal point and, in doing so, creates a more direct line between results of analysis and policies geared toward making the system better for its riders.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Fissinger, Mary Rose.

Advisor dc:contributor.advisor

• Jinhua Zhao and John Attanucci.

• Civil and Environmental Engineering.

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/1721.1/129000

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129000