In two recently released papers, a pair of scholars affiliated with Harvard Kennedy School’s Ash Center for Democratic Governance and Innovation take a close look at how urban leaders are grappling with the quick pace of technological and regulatory change in America’s cities today. In Reforming Mobility Management: Rethinking the Regulatory Framework, Stephen Goldsmith, the Daniel Paul Professor of the Practice of Government at Harvard Kennedy School, lays out a new model for how cities can regulate ridesharing services, simultaneously tackling traffic congestion. Craig Campbell, a fellow with the Ash Center’s Innovations in Government Program and the Assistant Director for Policy & Operations in the New York City Mayor’s Office of Data Analytics, tackles in Replicating Urban Analytics Use Cases, the question of how cities can adopt and adapt successful civic data analytics programs and tools from peer municipalities.
NEW URBAN MOBILITY MANAGEMENT
With the proliferation of ridesharing services in seemingly every city across the country, concerns are increasingly arising about how the ever-growing number of Uber and Lyft trips are impacting traffic congestion. Last year, in fact, mayors of large cities citied traffic as a top three concern mentioned by citizens. Yet these ridesharing services have found a ready customer base in urban dwellers frustrated by antiquated public transportation systems and monopolistic taxi cartels.
To alleviate the tension surrounding urban mobility, cities, writes Goldsmith in Reforming Mobility Management: Rethinking the Regulatory Framework, “must embrace a new role of planner, coordinator, and facilitator of a distributed system of integrated [transportation] providers.” In his new paper, Goldsmith calls on cities to acknowledge that ridesharing services can greatly increase access to and availability of urban mobility options and suggests integrating ridesharing into a more unified transportation system. He advocates for a “light touch” regulatory regime for ridesharing that employs curbside pricing to create a market-based usage model to wring maximum efficiency from congested urban roadways and curbsides.
Goldsmith cautions cities not to replicate the failures of top-down regulatory mechanisms like taxi medallions, but “focus on creating systems that primarily use transparent reputational and locational information and fees to guide company and traveler behavior.” Specifically, he urges cities to avoid vehicle caps and burdensome license requirements. Instead, he argues that charging for curbside usage, a more flexible regulatory model, will encourage more efficient use of road space.
“By charging for time stopped at the curb, the system incentivizes quick turnover and efficient pickups to avoid the negative effects of vehicles lingering at curbside,” Goldsmith writes.
REPLICATING CITY SUCCESS
Like nearly every other facet of life, technology has upended how cities halls around the country do business. Integrating the use of data into all manner of municipal operations has city leaders both excited and frustrated, explains New York’s Campbell. As cities grapple with new models for data-driven decision making, they are increasingly turning to each other for guidance and examples of what works and what doesn’t.
“How should a data-processing solution in Louisville, or a problem-solving methodology in New York City, get iterated in other cities? Why does a statistical model for rodent mitigation developed in Chicago fail to work in Pittsburgh?” asks Campbell in his paper, written when he was a fellow with the Civic Analytics Network, a consortium of leading urban chief data officers, managed by the Ash Center.
Campbell unpacks both the theoretical and practical considerations for replicating data-analytics use cases from city to city. Specifically, the paper lays out a series of recommendations for cities working to replicate analytics use cases:
- Look internally at use cases in other municipal departments, as “facilitating peer-to-peer replication within the organization can build familiarity with procedures for exchanging knowledge about analytics deliverables without the risk of copying projects from other cities.”
- Avoid “cosmetic analytics” warns Campbell, “A project motivated by a desire to project an image of a city or agency’s technological savviness should send up a red flag.”
- Campbell recommends connecting analytics professions with communities of practice in transportation, economic development, policing, and other areas in order to “create opportunities for infusing analytical approaches into existing pathways for innovation diffusion in domain-specific areas.”
- Campbell also cautions cities about simply replicating specific tools or products, but encourages them to “replicate processes for collaborative problem-solving.”
- Finally, he encourages cities to discuss failure “and the conditions of an analytics project that led to it,” which can facilitate learning that Campbell argues is often overlooked in discussions of “what works.”
While there is much to be learned from peer cities and practioners, there are core values and infrastructure each city has to practice and build, Campbell notes. “Successfully connecting use case replication to a city’s long-term analytics “journey” requires a deep investment in data and a sustained appetite for innovation—the foundational layers of problem-solving that cannot be copied,” he concludes.
Find more scholarship from the Ash Center online here. Resources and information for leaders in civic data analytics are available on the Ash Center’s Data-Smart City Solutions website and more resources for government leaders on the Government Innovators Network.