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Beijing is a city famous for traffic jams. In 2006, rush hour reportedly lasted 11 hours a day, and the city has been called a “virtual car park” during daylight hours. As in most major cities, urban planners have been trying for years to relieve the pressure by adding new roads or public transit lines, or providing better enforcement for traffic laws.

Now a group working at Microsoft Research Asia has shown that tracking the location of taxicabs could be a better way to identify the underlying problems with a city’s transportation network, helping officials determine how to best ease congestion.

The researchers used GPS data from more than 33,000 Beijing taxicabs. That data was collected in 2009 and 2010. The researchers were not just looking for bottlenecks—trouble spots that regular commuters may know only too well. “[Congested] road segments are only the appearance—they’re not the problem,” says Yu Zheng, who led the research. “We try to identify the true source of the problem in our work.”

The researchers presented their work last week at the 13th International Conference on Ubiquitous Computing, which took place in Beijing.

To get at underlying causes of traffic problems, the researchers needed to get information about the trips people are taking—where journeys start, finish, and how a commuter travels in between. The researchers divided Beijing into regions and analyzed the taxi data to find places where two regions weren’t properly connected.

Even if a taxi never encounters a slowdown, clues from the trip can indicate an underlying problem with urban planning. For example, the taxi driver might take a circuitous route from point A to point B, instead of a direct one. The added distance could indicate that the driver knows about a problem with what appears to be the fastest route.

The researchers’ algorithms indicate when the network of roads and subway lines between two regions cannot support the number of people traveling between those regions. By pointing out underlying problems, the system shows urban planners where to focus their attention, Zheng says.

In some cases, Zheng says, the busy regions aren’t really the ones that are flawed. For example, it may be that people from region 1 are going through region 2 on their way to region 3, in which case it may be better to connect region 1 and 3 directly, rather than trying to widen highways in region 2.

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Credit: Microsoft Research Asia

Tagged: Communications, data mining, algorithms, traffic, infrastructure, urban planning, wireless sensors

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