Unleashing analytics technology to smooth traffic congestion

Traffic congestion has been a problem for decades. As the automobile surpassed trains and buses as the primary mode of transportation in the 1950s and 1960s, the impact on cities across North America and Europe was quickly felt: gridlock and more gridlock. But now, we have tools that traffic planners in the 1960s couldn't even imagine.

The latest IBM report on global traffic congestion paints a picture of frustration across all major cities on the globe, but also points to technology solutions that can greatly ease this pain. (My colleague Andrew Nusca provides an overview of the report.)

Lots of positive steps have been taken or are underway -- more fuel-efficient cars, more public transportation, more ridesharing, more telecommuting. But this isn't enough, the report states.  Time to apply technology solutions against the problem:

"For the first time in history, digital and physical infrastructures are converging. As a result, we are now able to understand large, complex systems that previously resisted investigation – systems as diverse as waterways, oilfields, and transportation networks. Transportation officials are now able to collect real-time data on traffic conditions and instantaneously analyze that data and deploy strategies that minimize delays and congestion. Thanks to the proliferation of data-gathering devices on our roads and recent advances
in business analytics – large volumes of data can be quickly synthesized and actionable insights extracted that allow for active management of our transportation networks to keep people moving more efficiently."

How is this put into place? Such systems can provide transportation officials with "detailed, real-time traffic information; sophisticated analytics of that information that can predict traffic jams; and thus planners can use the resulting insights to proactively deploy traffic management strategies that would minimize delays and congestion."

What kinds of traffic management strategies can be employed? Examples from the report include "changes to signal timings, dynamic toll adjustments, incentives to change mode of travel, and incentives for changing time of travel."

The report cites the example of Singapore, where controllers receive real-time data through sensors to model and predict future traffic flows "with 90% accuracy."

Such technology enables financial carrots and sticks, now seen in Europe, of "congestion charging systems." Stockholm, Sweden, for example, has been able to cut queuing times on access roads to the city in the mornings by half. City traffic overall is down by 18%, and CO2 emissions in the inner city have been cut by between 14% and 18%, based on estimates by Stockholm City Traffic authorities.  During 2008, approximately 82 million vehicle passages were handled by the congestion charge system, which proved to be almost 100% accurate.

Not mentioned in the report are efforts to make cars smarter, thus more able to help drivers navigate more efficiently. For example, as part of a project initiated by Ford Motor Company, students at the University of Michigan developed a series of experimental apps that combine social networks, GPS location awareness, and real-time vehicle data in ways that help drivers get where they want to go efficiently. New ideas include caravan tracking technology thatenables clusters of vehicles traveling together to track each other along the journey, and fuel-tracking technology that provides drivers with real-time feedback about fuel economy and driving habits based on past drivers on a specific route.

(Photo credit: US National Institutes of Health, Noisy Planet site)

This post was originally published on Smartplanet.com