Written By Walter Scheib
Uber recently announced Movement, a website that allows urban planners and other researchers to leverage Uber trip data to make better informed decisions. At first glance, this offering from Uber builds upon our theme of shared data as a key enabler of smart cities (link to our smart cities newsletter); however, it leaves something to be desired upon further inspection.
First, the findings from case studies that Uber offers on the Movement website are hardly groundbreaking: there is more traffic during the holidays, travel times were much longer in the DC area when the Metro was shut down, amongst other relatively simplistic, common-sense examples.
Uber says this data can be used by urban planners, but it’s unclear how useful this data will actually be for urban planning because simple A to B trip duration data does not factor in additional elements that planners would find useful, such as:
- What type of trips are being taken: work, shopping, going to a bar, etc.?
- Are these trips being linked to public transit use?
- Are those requesting Uber rides exclusively using Uber, or is the ride hailing app one of many mobility options they leverage?
The potential value of this data rests in the ability to integrate it with other datasets and run analyses in more powerful software suites, such as ESRI’s ArcMap geospatial analytics software. However, if you can only view Uber’s data on the Movement website, it is largely useless if it cannot be integrated along with other data sources to complete more advanced transportation network analyses.
Uber has the opportunity to monetize this trip data, but other players are moving quickly to fill this void. Soon, street-lighting players like Silver Spring Networks, OSRAM, and Tvilight will employ solutions that monitor traffic flow and road conditions. Furthermore, companies like HERE, the high-definition mapping software company, will soon be enabling armies of self-driving vehicles on the roads in partnership with BMW, Audi, Intel and others. With HERE’s mapping software background, they certainly have the ability to integrate traffic data with other transit data sources and monetize it. Uber has an incumbent advantage here, but they must move quickly before others find clever ways to monetize their own data—rendering Uber’s trove of data much less valuable.
It’s also possible that Uber is using Movement as an open-sourced R&D experiment: releasing a certain level of data to the public in order to see how it will be used, and then investing in the top ideas (or just developing their own solutions in-house). Unleashing Uber’s full datasets on newly-developed software solutions is an opportunity for a new revenue stream—Uber did lose $800M in Q3 2016 after all, and I’m assuming they would be open to tapping this easily accessible revenue stream to offset these losses in future quarters.
In the end, this offering may be nothing more than a small token offering to cities, allowing Uber to say they are a helpful corporate citizen, while continuing to fight cities tooth-and-nail over regulatory issues. While this Uber data release leaves much to be desired, there are several players who are leveraging multiple transportation data sources to better understand how people move around cities and how they can better be served by the centralization of currently disparate transportation options:
- Sidewalk Labs’ Flow Transportation Solution (link)
- Xerox Transportation (Go LA App) (link)
- Moovit transportation app (link)
The Big Picture: Emerging Transportation Data Economy Enables Transportation-as-a-Service
There are certainly challenges involved around this emerging transportation data economy, including data ownership, data privacy, and hacking concerns related to connected vehicles. However, if these challenges can be overcome, transportation players who are part of a multi-party ecosystem that leverages shared data will soon be able to provide much higher levels of value to end-customers than they could ever produce on their own. Think of an integrated transit application that determines, due to current traffic patterns, the fastest way from A to B on a particular day is to link a Zipcar ride to a crosstown Subway trip and then an Uber ride for the last mile of the trip—all seamlessly coordinated and paid for on one app.
If Uber is hoping to truly make an impact with their data as a major player in the urban transportation space, the company should make an effort to head up a transportation data ecosystem that combines data from multiple transportations modes, including ride hailing services, car share, public transit, and bike share services. Uber would then position themselves the hub of all urban transportation, which is a powerful place to be—since those who can integrate and control multiple, disparate data streams will ultimately drive the most value and win in the rapidly-evolving urban transportation space.