Data Sharing Guidance for Public Transit Agencies—Now and in the Future (2020)

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CHAPTER 5 Models for Accessing External Data Sources In addition to getting value from sharing their own data, transit agencies see benefits from accessing external data sets. Mobility data from other sources can help transit agencies under- stand how people in their service areas get around, which can inform transit planning strategies. The transit agency interviewees were interested in external data sets including the following: • Location data from cellphone connections, smartphone location-based services, and other GPS-containing devices. • Data from transportation apps, including transit planning and fare payment apps, private mobility providers’ apps, and MaaS apps. • Other data collected by and pertaining to private mobility providers. Motivations for accessing this data include the following: • Enabling transit agencies to evaluate overall demand patterns in their service area and deter- mine how to better meet peoples’ needs. • Enabling more detailed road speed data to inform bus operations and route alignment deci- sions, and to improve bus arrival predictions. • Enabling analysis of how people behave when incidents or other disruptions prevent them from using the transit system. • Enabling transit agencies to identify access and egress modes and distances. In addition to private data sources, transit agencies use external, publicly available data sources, including census data, weather data, and Geographic Information System (GIS) data from cities, states, and regional agencies. There are several methods that transit agencies are using to access private data sources. The following are examples that were identified either through the literature review or in interviews. 5.1  Purchasing Data or Analysis “Buying anonymous and aggregated One model to acquire private data is by purchasing the data or mobile phone event data is a practical commissioning data analysis. TfL has documented its pilot project, alternative to [roadside interview producing transport model matrices derived from data from Telefonica surveys]. It will provide data on trip UK, a cell service provider (Transport for London n.d.). The matrices patterns in a cost efficient, safe way were produced using depersonalized, aggregated data that will be without inconveniencing customers or used to better understand demand patterns for public transport users compromising their privacy.” and drivers. They note that this data provides a better, more convenient (Transport for London n.d.) alternative to roadside interview surveys. One transit agency interviewee indicated that their agency was in the process of acquiring cellphone 45   

46   Data Sharing Guidance for Public Transit Agencies—Now and in the Future data to better understand demand patterns in their transit system, and another indicated their agency had already purchased cellphone data. Many city interviewees indicated their cities had also purchased data from technology companies, such as location-based services (LBS) companies, that generate traffic condition insights or travel demand flow information at the zonal aggregated or road segment levels. Some cities were able to share purchased data among departments in the city, while others were limited to using the data for specific projects, because of restrictions in their data usage agreements. Although some public agencies purchase data that can be used indefinitely, another purchasing model is the service subscription-based software platform. In that case, the data may belong to the company that provides the service or may even belong to another company or organization. Cities also purchase secondary data products from private companies or research institutions. Secondary data products include items such as “dashboards” [i.e., centralized webpages where residents can view multiple types of information about their city simultaneously (sometimes in interactive fashion) and predictive algorithms (i.e., programs that aggregate, model, and project data to assist in future decisionmaking)] (Brauneis and Goodman 2017). 5.2 Accessing Data Through Mobility Service Partnerships Many transit agencies have partnerships with TNCs; a 2018 study cited 29 examples in the United States (Schweiterman et al. 2018). FTA’s MOD Sandbox Program provides funding and oversight for many of these partnerships and includes a data sharing requirement. Data sharing happens in a variety of ways. This section describes examples from three different transit agencies. In one case, the transit agency has taken an evolving approach to its partner- ships, gaining access to more data over time. In another case, the transit agency negotiated a detailed agreement with their partner for specific data items and levels of aggregation. In the third case, the transit agency and its private partner provide data to a third party who conducts analysis combining the two data sets. Evolving Access to Private Data In this example, the transit agency interviewee indicated their agency has a partnership with a TNC to provide (1) first- and last-mile trips to transit stations and (2) door-to-door rides for members of its transportation disadvantaged program. This transit agency has been at the forefront of transit agency partnership with TNCs. Initially, the agency received only aggre- gated data from the TNC on the subsidized first- and last-mile rides, but with each contract amendment, they have negotiated to receive more data. Still, they do not receive trip-level data. They use data from the TNC to track users of the program as well as program response time. They would like to use this data to better plan their transit services. They see the evolution in data transfer for both programs as a sign of their experience. Custom Agreement Another transit agency interviewee indicated their agency has taken steps to negotiate a detailed data agreement upfront. For their FTA MOD Sandbox-funded project, the transit agency used an informal procurement process to seek a mobility provider that was willing to share data. Once they selected a partner, the transit agency continued with a careful and 

Models for Accessing External Data Sources    47   time-consuming process to iron out the details of their data sharing agreement. The agree- ment includes the following: • Variables. A list of variables that the TNC will share and the level of granularity for each one. • Access. Designation of which researchers would have access to the data, and where it would be housed. • Public data. Designation of what data would be made public. The transit agency has commit- ted to sharing aggregated data with municipal operators and city partners. In addition, there will be public reports on the pilot. Disaggregate data will not be made public. • Data ownership. The transit agency will not own the disaggregated data but rather will have access to it for 5 years. They will be able to access aggregate data indefinitely. Sharing Data Through a Third Party A partnership between Uber and transit agencies in Cincinnati includes a “first-of-its-kind study” in partnership with transit agencies SORTA (Southwest Ohio Regional Transit Authority) and TANK (Transit Authority of Northern Kentucky), in which a transit consultant will combine Uber and transit agency data to draw insights that can inform strategic transit planning in the region (Salzberg 2018; Schweiterman et al. 2018). In this case, Uber hired a consulting firm to analyze how people move in the city, including their use of rideshare and transit. The consultant worked directly with representatives from Uber, the city, and the transit agencies and has published a report on curb use and will publish a report on transit. The completed curb study has recommendations to the city for designing pick-up and drop-off areas and reallocating on-street parking (Teale 2019). Using a third party generally means that the data provided by the private mobility provider is not subject to state public records laws. As such, mobility providers may be more willing to provide disaggregate data that may be proprietary or hold potential privacy concerns. Other examples of third-party models include the SharedStreets initiative, supported by NACTO (described in Section 4.1), and University of Washington’s Transportation Data Collaborative, which was under development as of July 2019. The Collaborative provides policies, protocols, and platforms to enable data sharing and analysis of sensitive data (gener- ated from public or private mobility services) with partnering agencies to create data-driven policy and support research uses. It creates an innovative model to address data ownership, access, and privacy and ethical issues in the interest of partner organizations. Not only will the data collected be exempt from public records requests, but the Collaborative aims to leverage the technical skills and storage and computing power of the university (Shared-Use Mobility Center 2019). 5.3  Accessing Data Through Regulation Public transit agencies can work with cities and states to develop and push for regulation that can facilitate public agencies’ access to external data streams. Many cities have begun regulating and managing private mobility companies that operate on their public right of way. In response to the proliferation of micromobility services, various U.S cities have restructured their man- agement and regulation of transportation services. Although these policies vary in scope and detail, the core data sharing features of most fledgling micromobility policies are similar. Cities require micromobility companies to share data regarding trip and fleet availability, with many including specific expectations for the frequency at which data is shared (Migurski 

48   Data Sharing Guidance for Public Transit Agencies—Now and in the Future 2018). A micromobility company interviewee indicated that, for them to enter this market, they regularly provide aggregated mobility data to cities and transit agencies Transit agencies can also work with state legislatures to update public records laws that pose impediments to data sharing. The California Public Records Act has provisions that data that constitutes trade secrets will not be disclosed. Los Angeles Metro ensured that data it collected through an agreement with Via would be exempt from disclosure under those provisions. TriMet in Portland, Oregon, supported an update of Oregon law that exempts travel pattern data from public records requests (Shared-Use Mobility Center 2019).