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The researchers evaluated their system by examining how their calculations changed as Beijing’s transportation network evolved during the two-year period they monitored. They found that when city planners added new connections between regions that algorithms had identified as flawed, conditions did indeed improve. Where flaws were identified but not fixed, traffic conditions did not improve.

Zheng says the system could easily be adapted for any city that has a large number of taxicabs—many of which struggle with traffic in their own right. Beijing ranks fourth in the world for number of cabs. The top 10 includes Mexico City, Bangkok, Tokyo, New York, Buenos Aires, and Moscow. Zheng says that, with enough data, his techniques would work as well there as they do for Beijing.

“I think this is an interesting direction, though I wonder to what extent the real problem in urban planning is not having the resources—money—to do anything about it,” says Sam Madden, an associate professor at the MIT Computer Science and Artificial Intelligence Laboratory who studies wireless sensor networks, including GPS units.

Madden adds that the huge quantity of data the researchers amassed—enough to analyze every road in a city—makes the work impressive. Even a few years ago, he says, it would have been a challenge to get so much information about road conditions. For his own research, Madden put GPS sensors on taxis to gather data, but cost and difficulty limited him to tagging tens of taxis, not thousands.

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Credit: Microsoft Research Asia

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

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