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IBM using big data to learn about best, worst places to drive

Richard Jones

April 3, 2013

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Big data's uses may seem endless as long as huge piles of diverse content are being collected and checked for data quality. Doctors are looking to identify the source and path of flu epidemics, retailers hope to predict which colors will make for the hottest fashion trends in upcoming seasons and governments aim to spot the traffic patterns that cause the most accidents and could stand for improvements. 

The latter was the focus of the recently released IBM Social Sentiment Index, which revealed the best and worst cities for driving in Canada. By analyzing social media chatter, the IT firms found that Toronto was the epicenter of traffic tweets. During an 11-month reporting period, drivers in the city had crafted nearly 10,000 comments about their driving experiences - with an estimated 40 percent focusing on its shortfalls. On the other hand, driving conditions in Halifax generated the least buzz, just shy of 1,000 social media posts. Drivers in the capital of Nova Scotia had negative things to say only 20 percent of the time.

"The ability to effectively analyze data will define the next few decades of transportation, within cities and beyond," said John Longbottom, Canadian smarter cities leader for IBM. "Worldwide, cities are using these kinds of data insights...to better plan routes, schedules and optimize vehicles, equipment and facilities to expand capacity."

In fact, a Federal Big Data Outlook released by Deltek revealed that the United States government is already on a route to smarter, data-influenced cities. During fiscal year 2012, the government spent an estimated $4.9 billion and is expected to spend as much as $7.2 billion on big data in 2017. 

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