Big Data, Little Traffic: Is Tech Our Pass to the Fast Lane?


In June, Columbus, Ohio, won the U.S. Department of Transportation's Smart Cities Challenge, beating out six other finalists for $50 million in grants to become the federal government's top proving ground for intelligent transportation systems. According to the Columbus Dispatch, "that means that, soon, driverless vehicles will roam parts of Columbus, access to electric vehicle charging stations will increase and more cars will be able to communicate with traffic signals and other transportation infrastructure."

U.S. Census data continues to show people moving into urban areas at rates, in many places, that leave transportation officials struggling to keep up. Cities are deploying a number of non-tech solutions: refurbishing bike lanes, charging drivers for access to congested areas and even encouraging businesses to have commuters work from home. But cities are also leaning on tomorrow's data-crunching technologies to lighten the burden.

Sensors in the ground or on traffic lights read how many cars are at an intersection or on a roadway and the speed of travel. Systems then crunch that information to build traffic patterns—but that only captures part of the story. With smartphones and navigation systems, cities will soon be able to paint an even sharper picture.

"Having origin and destination data is giving us much more pinpoint accuracy about people's travel habits, where they're coming from and where they're going," says Rob Puentes, CEO of Eno Transportation, a nonprofit think tank.

Public Transit, Private Data

The holy grail of urban transit planning is a system of traffic lights and mapping units that not only respond in real time to the changing patterns of traffic through a city, but can also predict how those patterns will change in the future. If you need to reach the beach in Los Angeles in an hour, how should your route calibrate for eventual traffic? Puentes says a lot of cities are building technology towards that system, but none are really there yet.

"In reality, you should be able to know a whole lot more people are coming this way and proactively change for the future," says Daniel Hobohm, head of intelligent transport systems at computing giant Siemens.

Almost every car on the road functions as a node on a grid because almost every driver has a smartphone. The problem? Private companies manage that data. For instance, Google Maps knows an astonishing amount about city driving patterns and, as the Guardian recently reported, Google parent company Alphabet now plans to capitalize on that data with Sidewalk Labs, a secretive division targeting urban efficiency issues.

"We've put chips on everybody in the form of a cell phone," says Bill Eisele, a research engineer at Texas A&M's Transportation Institute. "The private companies are on the cutting edge and they have a lot of information and frankly don't always know how the public sector might use that."

Traffic sensors paint part of the picture. But it’s knowing both where people are going when they turn the key in the ignition and their entire driving history that completes it.

Adds Puentes, "If public planners could use that data, it would change everything."

Are Self-Driving Cars the Answer?

Fully autonomous cars could provide the most complete solution to a city's congestion problem: control all traffic from one central driving mind. One integrated system could, in theory, know everyone's traveling habits and their destination at a given time and thus optimize for every journey.

Puentes points out, however, that even with the obvious privacy concerns of such a system, the mechanics are still very much untested and many questions unanswered: Who has ultimate control of the system? Would a fully autonomous car system decentralize cities further? What happens with idle cars?

"We haven't been able to model those kinds of changes," Puentes says.

He believes that the shipping industry will probably be the first vertical to really embrace autonomous vehicles—to cut the cost of expensive drivers—and will be an important testing ground for the future.

Local Problems, Local Solutions

City-level innovation sparks, like the Department of Transportation's Smart Cities competition, will continue to drive change, but analysts like Eisele, Hobohm and Puentes note that local governments will need to push for big transportation improvements, even though such projects don't earn much political capital with voters and city budgets are already strapped. Changes to traffic times are small on a person-to-person level—a minute or two here and there—so even if the city's less congested, people can't necessarily appreciate the changes. And, as the old rule goes, politicians like projects where they can cut ribbons.

Another wrinkle is that traffic problems could grow more complicated as adjacent municipalities integrate data. Cars move between cities, so each traffic department (plus regional and state bodies) will need to understand how each level works.

"You have to target solutions. What works in one locale may be different than another," Eisele says.

Lastly, traffic sensors—whether on lights, under the streets or in smartphones—will continue generating exponentially more data for central processing centers to analyze. Whether in cloud computing environments or private server farms, making smart predictions about traffic patterns requires heavy-duty computing. Municipalities will have to be sure they're using the best data-crunching environments possible to keep the lights and lanes moving correctly.

"That's essentially the world we're all in. There's always more data to be turned into knowledge," Eisele says.

This story was produced by the WIRED Brand Lab for Comcast B2B.

Urban planners are seeking high-tech solutions to ease today's traffic flow and prevent tomorrow's gridlock.

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