The rapid growth in the availability and utility of vast amounts of digital data is arguably one of the most significant technological developments in recent years. In public transit, many agencies utilize modern technologies to collect large amounts of data, whereas smaller agencies with fewer resources and less expertise still use more traditional, manual data collection methods. Regardless of their technological capabilities, transit agencies recognize that some amount of transit data is useful and required. To the best of our knowledge, no standard data description of detailed fixed-route ridership exists today in the United States, forcing transit agencies to develop their own system of collecting, storing, and analyzing ridership and related data. In response to this need, this research aimed at developing one of the first public transit ridership data standards for fixed-route services and to support and promote its adoption and use. The resulting standard, an extension to the General Transit Feed Specification (GTFS) data standard, is referred to as GTFS-ride. GTFS-ride is easy to understand, able to accommodate the complexities of larger transit agencies, and capable of establishing a strong connection to the state of a transit network as it existed when the ridership data was collected. The first complete draft of GTFS-ride was released on September 6, 2017. This paper explains the structure of the five files that compose GTFS-ride, introduces additional support elements developed to facilitate its promotion and adoption, and documents the lessons learned from pilot implementations of GTFS-ride at three Oregon public transit agencies.

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Transit agencies have experienced dramatic changes in service and ridership because of the COVID-19 pandemic. As communities transition to a new normal, strategic measures are needed to support continuing disease suppression efforts. This research provides actionable results to transit agencies in the form of improved transit routes. A multi-objective heuristic optimization framework employing the non-dominated sorting genetic algorithm II algorithm generates multiple route solutions that allow transit agencies to balance the utility of service to riders against the susceptibility of routes to enabling the spread of disease in a community. This research uses origin–destination data from a sample population to assess the utility of routes to potential riders, allows vehicle capacity constraints to be varied to support social distancing efforts, and evaluates the resulting transit encounter network produced from the simulated use of transit as a proxy for the susceptibility of a transit system to facilitating the transmission of disease among its riders. A case study of transit at Oregon State University is presented with multiple transit network solutions evaluated and the resulting encounter networks investigated. The improved transit network solution with the closest number of riders (1.2% more than baseline) provides a 10.7% reduction of encounter network edges.

Understanding how riders use a transit agency’s services is central to providing effective service. Although the ideal experience for riders may not include transfers, these may be necessary to connect them from their origin to their destination. Previous methods have identified key hidden transfer locations within transit networks. However, there has been little effort to develop tools that enable small- to mid-sized agencies that typically lack access to sophisticated data sources to conduct this analysis. This research introduces a methodology for identifying transfer opportunities using a combination of statistical analyses of nonindividually identified automated passenger counter data to compute a transfer metric representing the association one service has with another through shared passengers. A unique aspect of this work is the utilization of ridership data compliant with the data standard General Transit Feed Specification (GTFS)-ride, which captures historic states of the transit network with associated ridership levels. GTFS schedule data from an Oregon transit agency were employed to identify transfer opportunities by assessing the probability of a transfer based on transfer time, an upper limit on transfer distance, and a new metric that measures the geographic coverage gains made by a particular transfer. A final transfer metric was calculated and compared against recently collected survey data. The key contribution of this work is the identification of transfer opportunities that lie outside traditional transfer hub locations. The resultant transfer metric will enable transit service planners to conduct regular analyses of their network, identifying key transfer locations and opportunities for further development.