Use Cases: How & Why Cities Use MDS

December 9, 2021


MDS is used by more than 120 cities around the world to plan transportation infrastructure, support and regulate shared mobility services, and advance the goal of a safe, equitable, sustainable transportation system. Cities need access to precise, accurate, and up-to-date information to achieve these goals and manage their public right of way. They also need flexible tools that allow them to respond to emergent needs and address the unique priorities of their communities. These key properties – precision, accuracy, and flexibility – are what make MDS a critical tool for cities.

The public can see how officials are using MDS and other tools to manage shared mobility programs and make improvements to transportation systems through comprehensive reports published by cities and public agencies. In reviewing the many shared mobility reports released by public agencies throughout the years, some trends emerge.


Cities use MDS to establish equity programs that promote vehicle availability, especially to residents in historically underserved areas. Comprehensive and precise data allows cities to measure compliance with these policies, evaluating vehicle availability in specific locations that serve low-income populations, and comparing service quality between high and low-income neighborhoods. Some specific examples of cities using MDS data to promote equality include:

  • Baltimore: The City of Baltimore conducted an equity zone “deep dive analysis” that used precise MDS data to measure mobility operators’ compliance with vehicle deployment requirements at specific locations, analyze the key destinations being accessed via vehicles in equity zones, and evaluate the program’s effectiveness in serving target populations. As the City’s annual report stated, “Looking at the data around deployment, or where vehicles are placed each morning . . . is one of the best proxies for increasing equity of access.”
  • Chicago: In a 2019 pilot evaluation, the City of Chicago used MDS data to measure e-scooter availability throughout the day in different census block groups. Using precise MDS geolocation and time information, the City determined that, in the afternoon, black residents had access to micromobility vehicles 13% of the time while white residents had access 57% of the time. That finding led the City to change the equity program to improve equitable access.


To protect public safety, cities limit shared micromobility vehicles’ speed and where they may operate or park, especially in pedestrian-heavy areas. MDS allows cities to measure compliance with these rules and take action when operators do not comply. Restricted areas are often quite small (e.g., a park, trail, or block), and thus require precise location data to measure compliance. These case studies show how MDS can be used to enforce traffic safety:

  • Chicago: The City of Chicago established geofences to keep e-scooters out of certain restricted areas. By using MDS location data, the City was able to measure mobility operator compliance with geofence rules along the narrow Lakefront Trail, which resulted in the average non-compliance rate dropping to 0.2%.
  • Los Angeles: After complaints along the Venice Boardwalk, Los Angeles imposed rules intended to reduce excess scooters and bikes, require parking in designated areas, and limit riding in pedestrian-heavy areas. The City used MDS data to verify that micromobility companies were complying with the rules and used in-field enforcement and penalties against non-compliant companies. This innovation preserved mobility options for the public while reducing clutter and neighborhood complaints: Scooters and bike deployment decreased from an average of 270 vehicles per day to 15 per day, and MyLA311 service requests fell by nearly 30% even while overall ridership climbed.
  • Portland: During large-scale protests in summer 2020, the City of Portland, Oregon decided to remove micromobility devices from downtown streets because of concerns that those devices could be used to cause property damage or otherwise compromise public safety. MDS data served as real-time “air traffic control” to identify the location of parked micromobility devices and ensure that they were promptly found and removed.


Precise and comprehensive route data allows cities to improve safety through better infrastructure planning, and measure the effect of their investments. Cities rely on MDS data to determine where they need to construct safe riding infrastructure, where to place parking for shared mobility vehicles, and to measure how infrastructure and policy changes alter where and how people ride. Effective infrastructure planning is an iterative process that depends upon flexible data, which the following case studies demonstrate:

  • Portland: The City of Portland used precise MDS data to decide where to invest in bike lanes along key corridors. Having precise information allowed the City to measure shifts in usage away from a park path when a new safe lane was built on an adjacent roadway, and to measure ridership before and after other upgrades to demonstrate how safe infrastructure increased ridership.
  • Baltimore: By analyzing precise routes along adjacent corridors, the City of Baltimore was able to measure how rider behavior changes with the installation of safe infrastructure for riding. The City’s 2020 evaluation report demonstrates the utility of precise data: “For example, in the case of Covington Street, the data show a shift in patterns when a bicycle facility was installed to give riders a safe option parallel to Key Highway . . . This indicates that riders prefer to use safe and comfortable infrastructure when it is available and will even slightly divert their intended route to do so.”
  • Sacramento: The City of Sacramento analyzed precise MDS trip data alongside maps of 311 complaints and existing bike parking to identify where it needed to provide additional parking for bikes and scooters, to accommodate rides while preventing sidewalk obstruction.


Cities employ MDS to understand how micromobility supports public transit access, and to encourage that synergy to further their climate and equity goals. Precise location data is necessary to understand if a trip originated or ended at a transit station, and to make appropriate provisions for micromobility vehicles at those stations. For example:

  • Baltimore: The City of Baltimore analyzed routes and trip origin/destination to identify key transit stations where e-scooters were being used as first/last mile connectors to transit.
  • Alexandria: By evaluating the percentage of trips that begin or end near transit, alongside feedback about vehicle availability, the City of Alexandria, Virginia identified a dearth of vehicles at particular transit stations as a challenge for its e-scooter program. The City is evaluating incentive programs that would better connect e-scooters with transit.
  • Los Angeles: MDS data gave the City of Los Angeles a window into when and where micromobility was used to connect to transit. Precise data allowed the City to understand where people most commonly pick up or leave micromobility vehicles near transit stations. These insights are being used to plan multimodal trip incentives and other interventions to encourage transit connections with micromobility.


To learn more about these case studies, see the below reports:

The OMF also maintains a Use Case Database, which serves as a starting point for understanding how MDS can be used and what data is required to meet those use cases.

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