In 2017, these services had a tumultuous year. Apocalyptic images of discarded dockless bikes in China left American officials that are experimenting with this model for bikesharing scrambling to ensure their cities avoid the same fate. Meanwhile, Uber’s admission that it paid a $100,000 ransom to hackers who stole 57 million user accounts damaged that company’s credibility as a protector of passenger privacy. And a widely shared study from researchers at University of California-Davis refuted several optimistic hypotheses about ride-hailing’s societal benefits: It found that companies like Uber and Lyft are spurring urban congestion, siphoning public transit riders, and failing to entice many people to give up their cars. Not coincidentally, transit agencies like Washington, D.C.’s WMATA are now launching their own investigations to see if declining ridership can be traced to the emergence of ride-hailing.
The tide is turning, especially as the line between public and private forms of urban transportation blurs. American transit agencies are partnering with ride-hailing companies to offer late-night service, move people to bus or rail stations (“first mile/last mile” solutions), and manage paratransit for riders with limited mobility. Ride-hailing companies are in an awkward position if they refuse to share data with governments that subsidize them. “If I’m paying you to move a passenger, the data for that passenger isn’t yours,” I heard a Texas transit official say recently to a ride-hailing executive. “It’s mine.” The executive had no response.
When will policymakers finally be able to access the data they need to manage streets and sidewalks in the public interest, and how will they get it? The most likely solution is via a data exchange that anonymizes rider data and gives public experts (and perhaps academic and private ones too) the ability to answer policy questions.
This idea is starting to catch on. The World Bank’s OpenTraffic project, founded in 2016, initially developed ways to aggregate traffic information derived from commercial fleets. A handful of private companies like Grab and Easy Taxi pledged their support when OpenTraffic launched. This fall, the project become part of SharedStreets, a collaboration between the National Association of City Transportation Officials (NACTO), the World Resources Institute, and the OECD’s International Transport Forum to pilot new ways of collecting and sharing a variety of public and private transport data. Kevin Webb, the founder of SharedStreets, envisions a future where both cities and private companies can utilize SharedStreets to solve questions on topics like street safety, curb use, and congestion.
That’s a laudable goal, but Shared Streets will have to solve several challenges in order to become a go-to resource. For example, it’s hard to provide a complete picture of urban mobility unless the heavyweights like Uber, Lyft, Didi Chuxing, Ofo, and Mobike participate; so far none of them has signed on. There is also the question of how tech behemoths like Google and Apple—collectors of massive datasets about individuals’ movement—can be involved. Perhaps they can be sources of reliable revenue that SharedStreets will need in order to scale (at present the initiative is being incubated with philanthropic support).
Finally, there is the critical question of privacy. Although Uber’s hacking scandal has dinged ride-hailing’s credibility as a protector of passenger data, new mobility services do have a point when they push back against handing over rider information to the government. It’s reasonable to assume that at least some customers will balk at the prospect of public agencies accessing their personal ride histories.
Webb says that SharedStreets will handle those concerns by collecting aggregated data that is rich enough to allow for deep analysis while still hiding information about individual rides. New mobility service companies could further protect their passengers by converting trip data into so-called “synthetic populations” of artificial data modeled after trips that people actually took.
However the new mobility service data arrives—almost certainly aggregated, and potentially artificially modeled—there will need to be a way to ensure it is accurate. After all, companies like Uber and Lyft have a vested interest in the questions policymakers pose about their impact on city streets. Data validation—especially for modeled data—is crucial for such an exchange to be trusted.
