Research Team: Giovanni Circella (lead), Siddhartha Gulhare
UC Campus(es): UC Davis
Problem Statement: Transportation is going through an era of huge disruption. A first component of this disruption is associated with the deployment of emerging transportation services based on new smartphone apps and real-time communications. Starting approximately a decade ago, new mobility services, including ridehailing services (also known as Transportation Network Companies, or TNCs), have been significantly transforming the passenger transportation sector. Since their inception, the usage of TNCs has grown rapidly in urban regions, yet their impact on vehicle miles traveled (VMT) and greenhouse gas (GHG) emissions are still largely unknown. Starting in early 2020, the world has experienced an even bigger disruption of a very different origin. In just a few months, the COVID-19 pandemic has quickly disrupted social and economic activities with effects that are still present in society and are expected to last. The pandemic caused a huge reduction in air travel, with a large reduction in both business and leisure trips, as well as many changes in household activities and mobility patterns. Since then, mobility flows have been recovering at different rates and evolving in different spatiotemporal travel patterns compared to their pre-pandemic baseline. Given these dramatic and impactful changes in society it is crucial to understand both the temporary and long-lasting changes that the COVID-19 pandemic and the associated modifications in activities and travel in California, and how various policies might affect this new “normality” in terms of modified travel behaviors and the potential for coupling the economic recovery with improved effects on equity and the environment.
Project Description: The research team will analyze the dataset from the longitudinal panel that the Three Revolutions Future Mobility program has built with annual data collections since 2018 with the latest survey wave being administered in fall 2022. This unique dataset allows researchers to perform novel analyses and does not have reliability issues that are normally present in datasets that rely only on retrospective data. To aid in the generalizability of the conclusions, the researchers will review the weights that were developed for each data collection wave to address the lack of representativeness in the sample and compare the different patterns observed in data collected with online surveys vs. traditional printed questionnaires, to ensure a consistent and methodological sound approach is in place. The researchers will investigate various topics such as changes in travel patterns (mode shift, car shedding, etc.), eventual VMT reduction, and assessing how the COVID-19 pandemic has impacted the travel behaviors of disadvantaged communities across the state.
Status: In Progress