America’s inadequate demand-responsive transportation (DRT) infrastructure imposes a high cost on individuals, communities, the health care sector, and the economy. Transportation is the glue that connects people to friends, jobs, and doctors’ appointments, and it enables them to shop and otherwise participate in the economic and social life of their communities. Those without adequate transportation options are at a serious disadvantage.
Demand-responsive services are transportation options that do not follow fixed routes or schedules; examples include dial-a-ride, Americans with Disabilities Act (ADA) complementary paratransit, taxis, app-based ride-hailing, ride sharing, car sharing, bike sharing, and other technology-enabled transportation. Many public transit systems in small towns and rural areas operate on a demand-responsive basis as do most human services transportation providers.
Demand-responsive services are critical for people who cannot drive or access regular public transportation, including people with disabilities; older people who are frail, ill, or have stopped driving; people with low incomes; and residents of rural areas. But they are often fragmented. There may be a dozen or more human services and other providers of door-to-door transportation in a given region, but each may operate in a silo, leading to both duplicative services and denials of trip requests. These services should be modernized to allow them to function as part of the emerging mobility ecosystem commonly called mobility-as-a-service (MaaS), in which users can personalize their trips and access via a smartphone or computer seamless, on-demand transportation.
Until recently, the lack of adequate technology has been a major obstacle to this coordination, but that need not be the case. This paper shows how a new data specification called the transactional data specification for demand-responsive transportation, published in 2020 by the National Academies of Sciences, Engineering, and Medicine’s Transportation Research Board, addresses this need.
A data specification allows the computer systems of different providers to communicate directly with one another. It provides a framework for every step of data exchange and storage by defining how the data are packaged and moved. A data specification needs to have clear elements, definitions, and formatting rules to create data in a form that all can use. A specification becomes a standard when it is endorsed by an industry group, often through a formal process involving a working group. The hope is for the demand-responsive transportation industry to embrace the transactional data specification, refine it as the industry implements pilot projects, and ultimately adopt it as an industry standard.
Transportation providers that adopt the common data format provided by the transactional data specification can seamlessly transfer and share data about requested trips within a network of providers, automate the task of assigning service and vehicles, and improve service coordination. Some data exchange is already taking place via other methods—but with limitations and challenges. The transactional data specification makes interoperating easier, reduces complexity, lowers the cost of the process, and improves service to travelers.
The Path Forward
This paper recommends several steps for achieving that goal and offers case studies of existing models. One important model is FlexDanmark, a publicly owned IT company in Denmark with two decades of experience coordinating rides using a transactional data standard. In overcoming the challenge of siloed technology companies, FlexDanmark is the exemplar for what could be achieved in the United States by embracing modern technology and market competition.
Case studies are provided by U.S. based systems in various stages of adopting the new data specification: The Denver Trip Exchange; The Atlanta Regional Commission (ARC); The Greater Minnesota MaaS Ecosystem; The Metropolitan Transportation Commission (MTC) Bay Area Complete Trip Deployment; ITNAmerica (a national non-profit transportation network serving older adults in rural communities).
The final case study is a proof of concept in rural Oregon. Two nonprofits in rural Lake County, Oregon, are the first transportation providers to commit to using the TDS. Funded by AARP, this pilot will demonstrate how the TDS can be integrated with Google Sheets, a cloud-based spreadsheet, to coordinate service without e-mails or phone calls.
The paper concludes with recommendations for key influencers and actors: Congress; the Federal Transit Administration; the Centers for Medicare & Medicaid Services; demand-responsive transportation providers (public and private, human services, and nonprofit); software developers and technology companies; technical assistance centers, membership organizations, and research organizations; and philanthropic and state and local government funders.