In an office in downtown San Francisco this week, six stories above
the blaring horns of buses and cars running up and down Market
Street, Shivakumar and Prabhakar showed me a screen of a train system
that could be any big city in the world — Sao Paulo, San Francisco,
Bangalore. Prabhakar clicked the play button and we watched a
geometrical visualization of the flow of train commuters moving into
stations, getting on trains and getting off at their stops. Some trains
were too full, some not full enough. It was mesmerizing to watch, in a
weird way.
But for city planners and transportation operators it could be an entirely new way of doing things. Urban Engines has built a system that can take data mostly from commuter transit cards — the bus card, the subway card (if you’re in San Francisco, your Clipper card) — and use algorithms to infer information about how commuters and the transportation flow are behaving.
Without embedding any sensors in the subway or video cameras watching the platforms, Urban Engines can tell things like how long commuters were waiting, how many trains went by that were so full commuters couldn't get on and what the volume of each train car was throughout the day. It only needs the data from when the commuter enters and exits the station, and by knowing the aggregate of all the commuter data at the same time, it can infer how the system is operating. It can do the same thing with a city bus system.
Essentially, Urban Engines is taking the smallest and cleanest amount of data possible to map out the entire public transportation network. The approach means that it can build such a monitoring system much more inexpensively than comparable transportation systems that use sensors, video cameras or even people manually observing and counting. A surprising amount of city accountability around transportation comes from city workers standing next to potential problem areas and observing — not exactly efficient or accurate.
The second piece of Urban Engine’s idea comes from behavioral economics: offering incentives or punishments to shape behavior. After identifying problem spots in transportation systems, it can help city planners use incentives to make the systems run better. For example, if too many people are using buses early in the morning, incentives (like being entered into a lottery) could be provided to encourage commuters to take the bus an hour later. Or if some train stations are being over-used and others are being under-used, incentives could be provided to get more commuters to the under-used stations.
Many of the ideas around incentives are based on the work that Prabhakar was doing at Stanford. He’s the director of the Stanford Center for Societal Networks, which works on making “societal networks smarter, more scalable and more efficient.” After leaving Google, Shivakumar (who’s Urban Engines’ CEO) came to Stanford as a visiting researcher to reboot, and the pair have been working together ever since.
Even though this might be the first time you’re hearing about them, Urban Engines actually already has some early traction and a hot list of investors. It’s working with the World Bank to implement its system for the buses in Sao Paulo. It’s helping Singapore shift its train commuters from peak hours to off-peak hours. And it’s got an early deployment with the train system in Washington, D.C. It’s also done pilots projects in Bangalore, with Infosys, on the Stanford campus.
Last year, Urban Engines raised a Series A round from some of the most prominent investors in the Valley, including Andreessen Horowitz, Google Ventures, Eric Schmidt, Greylock, SV Angel and angel investor Ram Shriram. The executives didn’t disclose the size of the round, but they’re employing about 20 people.
The underlying macro trend behind Urban Engines is that the population is growing rapidly, and by 2050 there will be 9 billion people on the planet. Much of the growth is happening in cities, and worldwide, more people now live in cities than outside of cities. City transportation systems will only get increasingly crunched over the coming decades. City planners and transportation builders will need new tools to help manage the influx.
But for city planners and transportation operators it could be an entirely new way of doing things. Urban Engines has built a system that can take data mostly from commuter transit cards — the bus card, the subway card (if you’re in San Francisco, your Clipper card) — and use algorithms to infer information about how commuters and the transportation flow are behaving.
Without embedding any sensors in the subway or video cameras watching the platforms, Urban Engines can tell things like how long commuters were waiting, how many trains went by that were so full commuters couldn't get on and what the volume of each train car was throughout the day. It only needs the data from when the commuter enters and exits the station, and by knowing the aggregate of all the commuter data at the same time, it can infer how the system is operating. It can do the same thing with a city bus system.
Essentially, Urban Engines is taking the smallest and cleanest amount of data possible to map out the entire public transportation network. The approach means that it can build such a monitoring system much more inexpensively than comparable transportation systems that use sensors, video cameras or even people manually observing and counting. A surprising amount of city accountability around transportation comes from city workers standing next to potential problem areas and observing — not exactly efficient or accurate.
The second piece of Urban Engine’s idea comes from behavioral economics: offering incentives or punishments to shape behavior. After identifying problem spots in transportation systems, it can help city planners use incentives to make the systems run better. For example, if too many people are using buses early in the morning, incentives (like being entered into a lottery) could be provided to encourage commuters to take the bus an hour later. Or if some train stations are being over-used and others are being under-used, incentives could be provided to get more commuters to the under-used stations.
Many of the ideas around incentives are based on the work that Prabhakar was doing at Stanford. He’s the director of the Stanford Center for Societal Networks, which works on making “societal networks smarter, more scalable and more efficient.” After leaving Google, Shivakumar (who’s Urban Engines’ CEO) came to Stanford as a visiting researcher to reboot, and the pair have been working together ever since.
Even though this might be the first time you’re hearing about them, Urban Engines actually already has some early traction and a hot list of investors. It’s working with the World Bank to implement its system for the buses in Sao Paulo. It’s helping Singapore shift its train commuters from peak hours to off-peak hours. And it’s got an early deployment with the train system in Washington, D.C. It’s also done pilots projects in Bangalore, with Infosys, on the Stanford campus.
Last year, Urban Engines raised a Series A round from some of the most prominent investors in the Valley, including Andreessen Horowitz, Google Ventures, Eric Schmidt, Greylock, SV Angel and angel investor Ram Shriram. The executives didn’t disclose the size of the round, but they’re employing about 20 people.
The underlying macro trend behind Urban Engines is that the population is growing rapidly, and by 2050 there will be 9 billion people on the planet. Much of the growth is happening in cities, and worldwide, more people now live in cities than outside of cities. City transportation systems will only get increasingly crunched over the coming decades. City planners and transportation builders will need new tools to help manage the influx.
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