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The Business Case for Mobility Data Sharing

The Business Case for Mobility Data Sharing
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By Hannah Safford and Dan Sperling

The explosive growth of mobility services—scooter-sharing, bike-sharing, ridehailing, peer-to-peer carsharing—is clear to anyone who walks around a city. Less obvious but equally game-changing is the accompanying explosion in mobility data being collected. App-based mobility services enable private companies to amass unprecedented amounts of information on how, when and where we travel, and how much we pay—which they use to expand their services and improve profits.

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The massive potential of this data is going unrealized. McKinsey reports that even though data from personal vehicles will be worth $450 to $750 billion worldwide by 2030, monetizing car data remains a major challenge. Even this hefty valuation may be an underestimate. McKinsey focused on possibilities for infotainment and advertising, without considering how mobility data can support smart investments in urban infrastructure.

The problem is that most mobility data remains locked away at private companies. Finance and insurance giant AIG emphasizes that our modern economy “relies on…the willingness of businesses and individuals to share data.” But we haven’t figured out how to share mobility data in a way that works. Government-run transit agencies are being sued for possible exploitation of customer data. Ridehailing companies like Lyft and Uber are locked in constant battle with regulators over what data-sharing requirements are reasonable.

Security and privacy are proving to be two of the thorniest issues when it comes to sharing of mobility data. Consider the “Mobility Data Specification (MDS),” a protocol developed by the Los Angeles Department of Transportation to promote information exchange around scooter-sharing and bike-sharing services. The MDS is a good idea in theory, but in practice threatens to seriously compromise individual privacy by making it possible to link travel records to specific individuals—revealing where people live and when people may leave their homes vacant.

The only way that mobility data sharing can work is if governments, businesses, and consumer advocates work together to establish sensible and mutually agreeable data-sharing practices.

A new paper from UC Davis presents a four-part framework for how to do just that. Each step will require buy-in from all stakeholders to be successful. But there are actions that businesses can take to lead the way.

Step 1. Set standards. Without data standards, innovative technologies like automated vehicles (AVs) and smart infrastructure will never make it very far. AVs and mobility devices from different manufacturers need to “talk” to each other to navigate around each other. Governments also need data standards to plan, manage, and invest in roads, curbs, and parking. Voluntarily adopted standards have been more successful in the transportation sector than standards imposed from the top down. When it comes to emerging mobility options, private companies can accelerate standardization by coordinating with each other to develop and adopt common mobility data formats that build on existing specifications (e.g., the MDS).

Mobility Data Sharing Network

A framework for effective mobility data sharing.

Step 2. Collect data. Private companies have been collecting mobility data for years, but today’s data from new mobility companies dwarfs what was previously available. How much data is really needed by local governments? Patchwork demands—or even worse, blanket demands—for data trigger alarms for companies. Just as companies have to work with each other to establish common data standards, public officials need to work with companies and each other to establish common data-collection requirements that minimize compliance burdens. Lyft, for instance, recently worked with the City of San Francisco to establish some mutually agreeable requirements for sharing ridehailing data.

Step 3. Store and share data securely. Many cities lack the capacity to defend sensitive mobility data against cybersecurity attacks. Efforts are underway to establish protected, centralized data repositories for storing and exchanging mobility data. The U.S. Department of Transportation’s Secure Data Commons (SDC) only grants access to mobility data for approved purposes (such as for research or city planning). Users may be required to analyze data directly on the SDC platform, rather than downloading data on potentially vulnerable systems for offline analysis.

Of course, the SDC and similar systems are only as valuable as the data they contain. It’s all too common for mobility companies to claim that they’d be happy to share their data for public good…as long as they could be sure that the data were adequately protected. Companies can “put their money where their mouth is” by voluntarily contributing data to protected repositories, thereby ensuring that such repositories continue to exist and expand.

Step 4. Analyze and apply data. Data sharing isn’t really about the data—it’s about the insights contained within. Private companies can lend their expertise to help unlock these insights. Inspiring examples of public-private collaboration around mobility data abound. Atlanta’s Together For Safer Roads initiative integrated public and private data into an interactive dashboard that helped city officials dramatically improve traffic safety. Companies like Ford, Uber, and Lyft have partnered with SharedStreets on tools to support improved urban transportation.

As mobility options expand, so will the mobility-data ecosystem. As the National Association of City Transportation Officials observes, “A wide variety of new services, standards, and formats are currently available to gather, manage, and analyze mobility data.” The private sector can do much to streamline these.

Businesses have generated astonishing innovation in mobility technology over the past decade. Similarly, astonishing innovation around mobility data must follow.

SharedStreets Map

SharedStreets is working with transportation network companies like Uber and Lyft to help city officials understand ridehailing patterns.

Hannah Safford is a Ph.D. candidate in Environmental Engineering at UC Davis, and a researcher with the UC Davis Policy Institute for Energy, Environment, and the Economy.

Dan Sperling, Ph.D. is the Distinguished Blue Planet Prize Professor of Engineering and Environmental Science at the University of California, Davis, director of ITS-Davis, and lead author of Three Revolutions: Steering Automated, Shared, and Electric Vehicles to a Better Future. Follow on Twitter: @DanSperling_ITS@ITS_UCDavis@3Rev_ITSDavis.

 

This article was written by Daniel Sperling from Forbes and was legally licensed through the NewsCred publisher network. Please direct all licensing questions to legal@newscred.com.

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