Mobility Data Specification

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Mobility Data Specification (MDS) is a standard for exchanging data between mobility operators and cities or other regulators. Consisting of several APIs, it allows agencies to analyze data from mobility operators in a standardized format as well as implement regulation digitally. Although currently focused on dockless scooters, bikeshare, and carshare; MDS has the capacity to expand to additional transportation modes and services[1].

History of MDS

MDS was developed by the Los Angeles Department of Transportation (LADOT) in 2018, with the first version released in May of that year[2]. As of November 2019, governance of MDS has been transferred to the Open Mobility Foundation [3]. OMF members include cities such as Los Angeles, Seattle, San Francisco, and Santa Monica, as well as shared mobility providers and nonprofits. The foundation is governed by a board consisting of city and county transportation officials[3].

Components of MDS

MDS consists of three APIs: Provider, Agency, and Policy.

Event States: MDS Agency, Provider APIs


The Provider API is designed to be implemented by mobility operators. Regulatory agencies can query the provider API to request historical information on trips and vehicle status[1].


The Agency API is designed to be implemented by regulatory agencies. Providers query the Agency API when certain events occur in their systems, such as trip starts[1]. This allows agencies to monitor mobility services in real-time.


The Policy API is designed to be implemented by regulatory agencies. It contains whatever local rules an agency may set that affect the operation of mobility services[1]. For example, it may set a lower speed limit for scooters downtown, or ban them from a certain block while a farmer’s market is in operation. Unlike the agency API, it need not be queried in real-time. The MDS specification envisions that individual regulatory bodies will specify minimum refresh intervals sufficient to inform operators of rule changes.

Privacy Concerns

Because MDS provides data on individual vehicles and trips, it has been the subject of privacy concerns. Although MDS data does not include personal information about the user, it is possible that vehicle route data, origin/destination data, and vehicle id data could be de-anonymized and used to track individuals [2][4]. While the detailed data that MDS provides is clearly a huge asset to regulators, it must be balanced with strict privacy and data protection measures.

Transit-Related Uses for MDS


MDS data can be used to help inform transit planning by providing data on how many dockless mobility trips start or end at transit stops. An agency might incorporate this data into their first and last mile plans to ensure adequate parking areas are provided for the number of devices at the stop. Routes to and from the stop could also be analyzed to guide agencies in placing bike lanes and other access improvements.

MDS data could also be used to provide transit riders with real-time information about dockless mobility options at transit stops. Agencies could leverage existing automated announcement systems and passenger information displays to announce the number of dockless mobility devices available and their operators, enhancing convenience for riders who use those services.

Because of its two-way nature, MDS could also be used by transit agencies to provide information to dockless mobility riders. Agencies may wish to provide information about transit services to riders approaching a transit stop. This information could remind people who don’t normally use transit about the transit services available, and/or provide familiar transit riders with real-time departure information to help them make a more convenient transfer. Agencies could also remind riders about rules for parking the devices at transit stops, and evaluate compliance.


Although currently used for dockless mobility devices such as scooters and dockless bikeshare, MDS is designed to expand to other mobility-as-a-service providers including ridesourcing/Transportation Network Companies (TNCs) such as Uber and Lyft. If regulators are successful in incorporating these services into MDS, transit agencies will be able to leverage MDS to evaluate and enforce compliance with rules prohibiting TNCs from picking up passengers at bus stops or driving in bus-only lanes.


While MDS does not currently support prospective trips, if this capability is added it may be useful for managing paratransit service in collaboration with other operators. Operators could use MDS to share timing and route details for upcoming paratransit trips, allowing them to more effectively match customers to planned trips. As costs for paratransit continue to increase, this could help agencies run more efficient service– especially in areas with multiple paratransit operators that aren’t currently integrated. A similar system could be used to arrange non-emergency medical transportation.

It’s also possible that cities could eventually request transit agencies to implement MDS on their vehicles, sharing detailed information about transit service to city agencies responsible for managing the street network. While this would present a challenge around implementing the proper hardware for buses to interact with MDS, it could help support inter-agency cooperation.


  1. 1.0 1.1 1.2 1.3 Open Mobility Foundation. "Mobility Data Specification" accessed via Github November 14 2019.
  2. 2.0 2.1 David Zipper. "Cities Can See Where You’re Taking That Scooter" "Slate." April 2 2019.
  3. 3.0 3.1 Open Mobility Foundation. "FAQ | Open Mobility Foundation" accessed November 14 2019.
  4. Montjoye et. al. "Unique in the Crowd: The privacy bounds of human mobility" "Nature." 2013.

Additional Reading

Los Angeles Department of Transportation. Urban Mobility in a Digital Age. (2016)

Document guiding LADOT's vision for digital urban mobility applications and regulation. Predates introduction of MDS.

Open Mobility Foundation. MDS on Github (2019)

Top-level Github page for MDS. Includes information in "" at top-level as well as specific to each API.

Regina Clewlow Finding the right balance between mobility data-sharing in cities and personal privacy (2019)

Article outlining some potential privacy concerns with MDS.