Tier 1 University Transportation Center Match Funds for the Strategic Implications of Changing Public Transportation Travel Trends

69A3552047141Even before the onset of the COVID-19 pandemic, public transit ridership was declining in many metropolitan areas in the United States. To regain riders, transit agencies and their partners must make decisions about which strategies and policies to pursue within the constraints of their operating environments. To help address this, the Transit-Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) Tier 1 University Transportation Center was set up as a research consortium from 2020 to 2023 led by Georgia Tech with research partners at the University of Kentucky, Brigham Young University and University of Tennessee, Knoxville (UTK). The T-SCORE Center had two primary research tracks: (1) Community Analysis (led by the University of Tennessee; included in this report) and (2) Multi-Modal Optimization and Simulation (led by the University of Kentucky; not included). The Community Analysis research track employed a combination of quantitative and qualitative research methods to assess three main drivers of change that have affected transit ridership: price and socioeconomic factors, the competitive landscape, and system disruptions, including COVID-19. The research approach for the Community Analysis track was divided into separate projects, and the UTK team led three projects that aimed to: (1) quantify the impact of different factors affecting transit ridership - including the COVID-19 pandemic - at a nationwide scale; (2) assess the impacts of shared micromobility, particularly electric scooters, on transit ridership; and (3) evaluate new fare payment technologies and emerging pricing strategies, with the vision of taking a step toward Mobility-as-a-Service (MaaS). The findings of these three Community Analysis projects can help inform transit agencies and city officials making decisions about how to increase transit ridership and plan for a sustainable future

MMOS Integration \u2013 Forecasting Ride-Hailing across Multiple Model Frameworks

USDOT Grant 69A3552047141The advent of on-demand transport modes such as ride-hailing and microtransit has challenged forecasters to develop new methods of forecasting the use and impacts of such modes. In particular, there is some professional disagreement about the relative role of activity-based transportation behavior models \u2014 which have detailed understanding of the person making a trip and its purpose \u2014 and multi-agent demand simulations which may have a better understanding of the availability and service characteristics of on-demand services. A particular question surrounds how the relative strengths of these two approaches might be successfully paired in practice. Using daily plans generated by the activity-based model ActivitySim as inputs to the BEAM multi-agent simulation, we construct nine different methodological combinations by allowing the choice to use a pooled ride-hail service in ActivitySim, in BEAM with different utility functions, or in both. Within each combination, we estimate ride-hailing ridership and level of service measures. The results suggest that mode choice model structure drastically affects ride-hailing ridership and level of service. In addition, we see that multi-agent simulation overstates the demand interest relative to an activity-based model, but there may be opportunities in future research to implement feedback loops to balance the ridership and level of service forecasts between the two models