Monitoring Public Transit COVID-19 Recovery Efforts and Ridership Trends and Assessing Opportunities for Deploying Dynamic Transit Routes

Research Lead: Alex Kurzhanskiy

University: UC Berkeley

Problem Statement: Mass transit is in crisis. The general decline in ridership over the past few years has been seriously exacerbated by COVID-19 and the subsequent shelter-in-place orders. It is quite possible that ridership levels will be less predictable than in the past once shelter-in-places orders are removed and the economy begins to recover. As a result, agencies will require continuous data to know how to best resume transit service, including possibly employing dynamic transit scheduling and operations in response to rapidly shifting ridership patterns.

Project Description: This research project will collect and analyze transit ridership information in the Bay Area on a weekly basis, as California gradually lifts shelter-in-place orders. Data will be compiled from automatic passenger counters (APC) from a number of Bay Area transit services and analyzed in combination with the Census, land use, bicycle use, traffic and weather data, as well as local news and Twitter comments. These data will be compared with the pre-COVID period and with projections and expectations of how public activities would resume according to the California reopening plan. In summary, this project will: i) provide a better understanding of how accurate were the projections and expectations for recovery as California emerges from the shutdown; ii) evaluate transit agencies’ responses to the quarantine and identify what responses works well in which situations; iii) identify the populations that use and need transit the most during the phases of economy reopening; identify transit agencies that would benefit the most from frequent analysis of ridership information combined with other data; identify the best ways to facilitate data sharing between transit agencies; provide a better understanding of varying ridership patterns and the potential value of employing dynamic transit routes; analyze how reduced transit capacity due to social distancing requirements will affect traffic on freeways and urban arterials; and develop a data model to be used for transit-related information collection and for data sharing between transit agencies that could become the basis for a comprehensive transit information system.

Status: In Progress

Budget: $50,000

Project Partner(s): California Department of Transportation