Transportation

A Practical Guide To Mobility Data Sharing


Cities around the world are requiring that scooter companies share data with them to manage safety, equity, and sustainability

Over the last year and a half, shared electric scooters have expanded to over 300 cities around the world. With this growth, so too have requirements by cities that operators of shared vehicle fleets, namely scooters, provide cities with data and information to help policy makers more effectively manage their streets and public spaces.

Shared mobility services, including scooters, bikes, Uber and Lyft, have many potential benefits, including reducing one’s need for a personal car, delivering service when transit may not be available, and in the case of scooters, providing a more joyful way of getting around. However, these services may also have unintended consequences that are equally important for cities to understand, measure, and mitigate. Research on Uber and Lyft in multiple major U.S. cities has found that they may mostly replace transit, walking, and bike trips. More recently Uber and Lyft released their own report with an estimate of just how many vehicle miles they now deliver in cities.

At the end of the day, there are both positive impacts and negative impacts of private shared mobility services. And it is only with data that transportation planners and policymakers can drive progress towards the positive outcomes: safer, more reliable, more equitable, and more efficient transportation in cities.

An Overview of Scooter Data-Sharing Standards

As part of shared scooter and bike operating permits, many cities are now requiring that companies share data in standard formats. There are two key data specifications which prescribe the application programming interfaces (APIs) that many cities now require in policy: the General Bikeshare Feed Specification (GBFS) and the Mobility Data Specification (MDS).

General Bikeshare Feed Specification (GBFS)

Originally introduced in 2015 by the North American Bikeshare Association (NABSA), the General Bikeshare Feed Specification was designed for docked bikeshare systems, such as New York’s Citi Bike and Chicago’s Divvy system, to more easily provide information to users about how many bikes were currently available. It was largely developed through a partnership between city bikeshare system planners and legacy bikeshare companies (e.g. Motivate, before it was acquired by Lyft).

Key characteristics of GBFS:

  • GBFS APIs report the real-time information about available vehicles. GBFS was not designed to share information about vehicles while they are on trips, being redistributed within the city by the operator, or having their batteries charged (if electric).
  • GBFS is fairly often required by cities as part of micromobility regulations to obtain data about available shared bikes and scooters for increased transparency and oversight by the city.
  • GBFS data feeds typically report the location of a vehicle, vehicle type (bike/scooter), and current battery charge (if electric).

A consulting team is currently engaged in an industry-wide effort with feedback from multiple stakeholders to update and enhance GBFS, as it continues to be more widely used with the arrival of scooters. Key to this effort are new mobility data platforms, such as Populus, which is part of the team updating the standards.

Mobility Data Specification (MDS)

Introduced by LADOT in September 2018, the Mobility Data Specification (MDS) built on GBFS by expanding on what data cities could require from mobility operators (through a “Provider API”). It also introduced the concept that cities could communicate back to operators (through an “Agency API”). In practice, the majority of cities only require and use the MDS Provider API.

Key characteristics of MDS:

  • In addition to the status of available vehicles, MDS also specifies how information should be shared about vehicles that are unavailable due to redistribution, maintenance, or low battery through vehicle event status changes.
  • MDS introduced the concept of sharing data for trips, including starts, ends, and entire “breadcrumb” trip trajectories/ routes.

While MDS was initially designed for dockless bikes and scooters, and currently only specifies how data from these services should be shared, many cities hope to expand these data sharing standards to other services (E.g Uber and Lyft). Last November, the Populus team and Lime partnered together to develop an extension of MDS to shared cars to validate Lime’s use of on-street, curbside parking in Seattle.

The Challenge of Protecting Individual Privacy

The development and adoption of data specifications enables cities to more easily require access to data from private mobility operators, including (in the case of MDS) detailed data about trips that can potentially be used to re-identify the whereabouts of specific individuals. This has raised privacy concerns by a variety of players in both the private and nonprofit sectors.

There are several potential solutions that enable cities to access the data they need to manage the public right of way, while protecting against unnecessary risks associated with free-flowing, unprotected personal trip data.

Secure access to data. Rather than require open access to potentially sensitive data feeds (e.g. APIs such as MDS), cities can require that this type of information be delivered only through secure data feeds that are associated with limited access and specific provisions about their use.

Data license agreements. Cities and operators are now beginning to establish data license and security provisions that define how potentially sensitive data should be stored, processed, and delivered. Several public agencies and private companies are now part of the SAE mobility data consortium, a multistakeholder collaborative developing best practices with guidance on responsible data licensing practices in this rapidly changing space.

Robust, third party solutions. Some public agencies may not have the desire or resources to manage the complex flow of an ever-changing landscape of mobility operators, or the security requirements associated with protecting personal data. Secure third party data platform solutions, such as Populus, allow for cities to efficiently harness mobility data for important policy and planning decisions. Many cities, large and small, are finding that this is a very cost efficient path forward, as these third party solutions are processing data feeds from multiple operators with economies of scale.

Populus

Focusing on information to guide decision making. Many of the key policy and planning decisions that cities hope to make with access to information from mobility operators can often be achieved by access to historical, anonymized, and aggregated data, which minimizes privacy risks and concerns about surveillance. For example, many cities are primarily interested in answering questions such as: Were scooters equitably distributed in low income neighborhoods according to policy over the past month? And where are the majority of scooter trips taking place on our streets? 

As shared fleets of privately-run mobility services continue to grow, including electric scooters, Uber and Lyft, and delivery vehicles, there are many reasons to require that these private fleets deliver data to cities. After all, their business models depend on access to publicly-funded infrastructure that only cities can provide. Moving forward, it is imperative that cities have secure and efficient access to information to plan for growing fleets of shared services. Much progress has been made in the past year. We’ve entered a new era of public and private collaboration where together, we can deliver on promises for a better mobility future.



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