Type of experiment data sharing platforms to facilitate collective negotiation
City Global
Name of experiment Crowd-Sensing Transit Platforms

Data-sharing platforms and collective negotiation can end rush hour forever. 

Several initiatives currently underway aim to improve mobility and decrease congestion through data-sharing platforms that allow the management of transportation supply and demand by using incentives delivered through commuter apps to distribute transit more efficiently, inform commuter decisions, and enable collective negotiation of space.

Last century’s short-sighted auto-centric planning combined with rapid population growth in urban centers has yielded critically inefficient urban mobility in most large cities.


Urban Engines was a startup founded in 2014 that operated in Singapore, Washington DC, and Sao Paolo.2 It gathered traffic and transit data such as transit card swipes and applied algorithms to monitor congestion and movement in real time and to help city transit agencies understand what their citizens commutes were like and help create systems that financially incentivize commuters to travel during off-peak hours.2 Urban Engines was bought by Alphabet Inc.’s (Google’s parent company) in September of 2016.6

a project of Alphabet Inc.’s urban innovation subsidiary, Sidewalk Labs, is a software platform designed both for use by the public and to “help cities diagnose and fix congestion problems.”4 It mines data from a variety of sources including Google Maps, Waze, municipal data, and eventually remote traffic sensors.5 Since it can make inferences about what kind of trips people are taking–commutes, leisure, etc–it will allow cities to run experiments to see how congestion would change with added bus routes, for example.4

NextCityis an initiative of Cubic Transportation Systems, the company that the transportation payment systems the world over, from San Francisco’s Clipper card to London’s Oyster card.1NextCity would be both a transit management platform for city governments and an app for commuters.1The app would establish a single-account system covering payment for all modes of transportation.1The government’s platform would collect real-time data through the app which would learn to predict users’ transit choices and behaviors.1 It would allow city governments to micro-manage supply and demand with pricing incentives and also allow commuters to circumvent congestion with alerts and suggested routes.1

Living Mobs was the Mexico City’s team’s (Gabriela Gómez-Mont director of Laboratorio para la Ciudad, José Castillo principal architect at a|911 and Carlos Gershenson, data analyst from IIMAS-UNAM) winning proposal for the Audi Urban Future Award 2014.3 It is a “social contract for reinventing mobility,” relying on citizens to donate their own real time transit data and collectively negotiate transportation as a city.3 To build a beta app, the team gathered over 14,000 data sets from public and private sector partnerships including Uber, Yaxi, Microsoft, and Movistar.3 The app would allow users to access curated commuter data in real time, empowering everyone to adapt their transit behaviors and collectively negotiate the city to alleviate bottlenecks.3

Data we already collect–when analyzed and operationalized–has the capacity to free the world from crowded subway cars and bumper-to-bumper nightmares, to diversify mobility options, to give everyone on the planet a more efficient commute. The question is not whether this will come to fruition but rather: are convenient commutes and well-targeted coupons worth handing private companies and city governments real-time and predictive knowledge of where we are and where we go?
  1. Mendelson, Zoe. “This Amazing App Could Integrate Every Urban Transportation System.” FastCoExist.  March 23, 2016: <http://www.fastcoexist.com/3058075/this-amazing-app-could-integrate-every-urban-transportation-system-and-pay-you-not-to-drive>
  2. Jaffe, Eric. “Using Insights and Incentives to End Rush Hour.” City Lab. March 14, 2014: <http://www.citylab.com/commute/2014/05/using-insights-and-incentives-end-rush-hour/9101/>
  3. Mendelson, Zoe. “How a Mexico City Traffic Experiment Connects to Community Trust.” Next City. June 16, 2015: <https://nextcity.org/daily/entry/mexico-city-government-lab-community-trust-big-data-traffic>
  4. Dougherty, Conor. “Cities to Untangle Traffic Snarles with help from Alphabet Unit.” New York Times. March 17, 2016:  <http://www.nytimes.com/2016/03/18/technology/cities-to-untangle-traffic-snarls-with-help-from-alphabet-unit.html>
  5. Brasuell, James. “Meet Flow: Google’s Transportation Data Revoltion.” Planetizen. March 29, 2016:  <http://www.planetizen.com/node/85242/meet-flow-googles-transportation-data-revolution>
  6. Yeung, Ken. “Google Acquires Urban Engines to Bring its Location-Based Analytics to Google Maps.” Venture Beat. September 15, 2016: <https://venturebeat.com/2016/09/15/google-acquires-urban-engines-to-bring-its-location-based-analytics-to-google-maps/>