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

Abstract

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.

This work makes use of two primary methodological tools: the k-means clustering algorithm to identify behavioral patterns, and linear and spatial regression to model metrics of urban mobility across the city. The former is chosen because of its established history in the literature as a technique for classifying smart cards, and because its simplicity and efficiency in clustering high numbers of cards made it an attractive option for a framework that could be adopted and customized by various transit agencies. Spatial regression is employed in conjunction with classic linear regression to capture spatial dependencies inherent in but often ignored in the modeling of urban mobility data.

Chapter 3 of this work identifies the behavioral dynamics underlying top-level ridership decreases between 2017 and 2018 on the Chicago Transit Authority (CTA) and finds that riders decreasing the frequency with which they ride, rather than leaving the system, is the primary driver behind the loss of trips on the system, despite growth in the number of frequent riders using the system for commuting travel. The following chapter applies a similar framework to understand the precipitous ridership drop due to COVID-19 and discovers distinct responses on the part of two frequent rider groups, with peak rail riders abandoning the system at rates of 93% while half of off-peak bus riders continued to ride during the pandemic. Chapter 5 uses linear and spatial regression to model the percent change in trips due to COVID by census tract and finds that even when controlling for demographics, pre-pandemic behavior is predictive of the percent loss in trips.

Specifically, high rates of bus usage and transfers, along with pass usage, are associated with smaller drops in trips, while riding during the peak is predictive of larger decreases in trips. Chapter 6 presents preliminary thoughts on employing a spatial regression framework on high-dimensional data to learn urban mobility patterns. This work highlights the insights to be gained from an analysis framework that reveals the complex behavioral dynamics present on a transit network at any given time. It further connects these behaviors to other rider characteristics such as home location and response to the COVID-19 pandemic, painting a rich picture of an agency's riders with their existing data and allowing for informed, targeted policy creation. A key finding was that frequent, off-peak bus riders who frequently have to transfer are one of the largest groups of riders and the group most associated with continued ridership during the pandemic.

Future policies should recognize that this group uses the system when and where overall ridership is low, and direction of resources away from these parts of the system will disproportionately hurt riders who are most reliant on public transit and therefore have the most to gain from increased investment. The CTA should work in conjunction with other stakeholders to ensure that as public transit ridership recovers from the pandemic, attention is paid not only to those riders who need to be brought back onto the system, but also those who never left it.