This post was written by Stephen Goldsmith and originally appeared on the Data-Smart Cities Solutions website on June 11, 2013. Stephen Goldsmith is the Daniel Paul Professor of the Practice of Government and the Director of the Innovations in Government Program at the Harvard Kennedy School. He is a former mayor of Indianapolis and Deputy Mayor of New York City.
Ask a resident of the city of Boston what they like least about the city, and there’s a decent shot that they’ll mention traffic.
Boston’s notorious congestion stems from a “perfect storm” of several factors: high population density coupled with tightly packed suburbs, few methods of egress from the city proper, a difficult-to-navigate non-grid street layout, and a multitude of one-way and non-perpendicular streets. Taken together, it’s no wonder why Boston consistently clocks in near the top in rankings of worst cities to drive in, and why Bostonians sit in traffic, on average, for 53 hours each year.
The problem is well-known, and solutions are routinely proposed. Most famously, the city dug up a giant swath of downtown real estate in the 1990s—entirely rerouting the city’s chief highway—in an effort to make driving a little more bearable.
More recently, Boston Mayor Thomas Menino has been examining the issue through a different lens. His new approach hinges not on any major construction project, but on analyzing and applying public data to boost efficiency in traffic flow and fix physical road problems before they cause crashes or necessitate street closures.
“We don’t do a good job of moving traffic,” Menino said in 2012 in his announcement of several such initiatives. “We’ve got to modernize.”
Backing up that promise, in March of 2013 the city announced a partnership with IBM for a pilot initiative focused on making the city run—and drive—better across the board. The partnership will entail the creation of a central software hub that, using disparate data sets from the city’s major agencies, will help administrators monitor citywide operations, anticipate problems that could disrupt transportation flow, and coordinate deployment of cross-agency resources to help the city function even when its infrastructure is overtaxed by major events like a Red Sox-Yankees home game.
Beyond this program, data is also being used to tackle street repairs, which if left alone can lead to dangerous driving conditions, car accidents, and road closures. The city is currently rolling out tracking tools and an asset management system to help maintain its more than 60,000 street lights—three percent of which are in need of replacement or rewiring at any given time. Additionally, Boston is crowdsourcing massive amounts of information from its citizens through programs like Street Bump, a project of the Mayor’s Office of New Urban Mechanics that allows drivers to transmit road quality data back to the city; or Citizens Connect, which allows users to share information about accidents, downed signs, and other issues requiring cleanup or repair.
For far more cities than just Boston, street congestion—and the air pollution, noise, and headaches that come with it—is a central concern. Even barring the psychological toll of a taxing commute, the cost of traffic is immense, amounting in the U.S. to roughly $100 billion every year in wasted fuel, excess carbon emissions, and lost productivity.
Luckily, because transportation of people and goods is so fundamental to city life and because the traffic problem is so ubiquitous, many governments are looking for solutions, many of which hinge on how data can drive efficiencies in transportation infrastructure.
While many urban centers are incorporating data into certain aspects of that infrastructure, though, the ultimate goal is one that cities such as Los Angeles and even entire states like New Jersey are pursuing, often working in tandem with companies like Xerox or Inrix.
By combining various sets of public data—mass transit statistics, GPS and mobile information, toll routes, traffic sensors, weather data, and more—into a central hub, these cities will be able to paint constantly evolving pictures of what a region’s transportation landscape looks like at any given time. With this information in tow, administrators will be equipped to create dynamic pricing schedules for toll lanes based on real-time traffic, develop more intelligent stoplights, create apps that automatically guide drivers toward alternate routes when accidents are detected, and much more.
Of course, when it gets really bad out there, those systems might also give you another option as well: When to just take your bike.