PUBLIC TRANSPORTATION AT A CROSSROADS Transportation Network Companies, COVID-19, and Transit Ridership

*PhD Defense*
Time
09/01/2022 13:30–15:30
Location
Zoom meeting - https://us06web.zoom.us/j/82687083777?pwd=ZzNNWmtFeGZYQmNoTlFWSUVMMGhsZz09
Farzana Khatun
Farzana Khatun
TS PhD candidate
Abstract

Public transportation in the U.S., including in California, is under siege. Over the last two decades, ridership has been steadily declining, possibly because traditional transit users gained more accessibility to private cars and because of the emergence of transportation network companies (TNCs, i.e., Uber and Lyft). The COVID-19 pandemic worsened a bad situation. Public transportation officials are now confronted with the challenge of restoring the health of public transit so it can contribute to a more equitable and sustainable transportation system. Therefore, in this dissertation, I first investigate how Transportation Network Companies (TNCs, e.g., Uber and Lyft) impacted transit ridership pre-pandemic, before analyzing how COVID-19 affected transit and other modes. I rely on both discrete choice and quasi-experimental models to analyze data from the 2009 and 2017 National Household Travel Surveys and from a California survey administered in May 2021 by Ipsos.

In Chapter 2, my results for the U.S. show that individuals/households who use either public transit or TNCs share socio-economic characteristics, reside in similar areas, and differ from individuals/households who use neither public transit nor TNCs. In addition, individuals/households who use both public transit and TNCs tend to be Millennials or belong to Generation Z, with a higher income, more education, no children, and fewer vehicles than drivers. To the best of my knowledge, this is the first nationwide study to contrast public transit and TNC users that relies on cross-nested logit structures. My second contribution here is a comparison between individual and household-level models to account for intra-household dependencies of mode choice.

In Chapter 3, I quantify the impact of TNCs on household transit use by comparing travel for households from the 2017 NHTS (who had access to both transit and TNCs) matched with households from the 2009 NHTS (who only had access to transit) using propensity score matching. Overall, I find an 18.2% daily reduction in transit use for the entire U.S., with a 21.8% drop for weekdays and a 15.2% decrease for weekends. My main contribution here is to tease out the causal link between the emergence of TNCs and the decline of transit at the household level using propensity score matching, as previous studies relied either on descriptive statistics, correlation analyses, or considered aggregate ridership changes.

In Chapter 4, I analyze how Californians changed transportation modes due to COVID-19 and explore their intentions to use different modes after the pandemic. I find that driving but especially transit and TNCs, could see substantial drops in popularity after the pandemic. Many Hispanics, African Americans, Asians, lower-income people, and people who would like to telecommute more intend to use transit less. Key obstacles to a resurgence of transit after COVID-19 are insufficient reach and frequency, shortcomings that are especially important to younger adults, people with more education, and affluent households ("choice riders"). In addition to addressing these concerns, effective transit policies must be integrated into a comprehensive framework designed to achieve California's social and environmental goals.

Overall, my findings highlight the danger for public transit to enter into outsourcing agreements with TNCs, neglect captive riders (people with no alternatives to transit), and to expose choice riders to TNCs. Key priorities for transit agencies should therefore be to increase the frequency of their service (as appropriate), and extend their reach to solve the "first and last mile problem", possibly by creating partnerships with micromobility providers.

Keywords: Public Transit; Transportation Network Companies; Active Modes; COVID-19; Cross-Nested Logit; Generalized Ordered Logit; Propensity Score matching