Big data is increasingly key to understanding how people move, and helping them do so most efficiently. And there is ever more data available. Everyone's got a smartphone, which can provide, and collect, vast amounts of information. Transit systems across the country are making their data available to everyone, letting the public see when the No. 4 bus will arrive.

What's needed now is a way to crunch all that data and make it useful to anyone trying to determine the best way of getting from Point A to Point B. That's why a group of former Google engineers and Stanford researchers founded Urban Engines and released an app it calls Urban Engines Maps.

Urban Engines pulls data from everywhere it can: It licenses information from cities, pulls in anything public, buys data on private vehicles from an unnamed third party, and uses open street maps. The startup builds all that information into a single engine to deliver, in real-time, updates on the full range of transportation modes in any given place.

Using the app, individuals can plug in their destination and present location, and see different combinations of travel modes to move as quickly as possible. The startup's data software lets public transportation agencies track their systems by vehicle occupancy and location, giving them a clearer view of patterns, where problems are, and what's working well. And it helps private companies plan logistics with interactive maps that blend private and public data.

Today, the company launched its latest feature: A/B testing. Instead of just getting a picture of how things look at the moment—how crowded each bus is, what streets are gridlocked—Urban Engines now offers companies and transit agencies the ability to test and compare different choices for what routes they take, what type of vehicles they use, how many vehicles they use, and what the best time to travel is, all in the virtual world.

The big development here is that Urban Engines is making the process easy for companies that don't have unlimited time and resources to cobble together all the available data and test out different scenarios. Urban Engines' clients will operate the service in a regular Internet browser. "You don't need a data scientist," says Karen Roter Davis, the startup's head of strategy and business operations.

"The ability to do these different iterations and scenarios on the fly really makes a difference," says Roter Davis. In a demo for WIRED, she tested different setups for an imagined shipping company based in the western half of San Francisco. Roter Davis cycled through scenarios, changing the number and type of delivery vehicles, their starting locations, and the time of day to see how quickly they could make a set of deliveries. That faux company, the idea goes, could do just that to consider news ways of doing business, without the risk of investing in a real-world change only to find out it's no good.

Urban Engine's A/B testing software is aimed at transit agencies as well. Based on data it's collected and analyzed over the past few years, it can play with variables like weather, closed lines, and ridership. The Bay Area's BART system, for example, could use the program to get an idea of what its system will look like when OneRepublic plays an outdoor concert in downtown San Francisco the week of the Super Bowl. What would happen if it ran extra trains? What's the best time to increase or decrease service? How will road closures affect its service?

The startup hasn't said how much it will charge for access to the system, but if it works as well as billed, it could be a big help for organizations interested in making things better, but without the resources to run pilot programs in the real world. "We are democratizing the way in which we look at movement," Roter Davis says.