Oct 24, 2017

A Look Inside One of the Top 5 Intelligent Transportation System Projects in Canada

Top 5 Intelligent Transportation System Project in Canada

WeatherBrain by Campbell Scientific Canada named one of Canada’s Top 5 Intelligent Transportation System projects.

In December 2016, Campbell Scientific Canada (“CSC”) kicked off its intelligent transportation system pilot project in Magog, Quebec, with the installation of what are now known as WeatherBrain stations, along Magog’s approx. 550 kilometers of roadway. Individually, these stations act as traditional road weather information systems (“RWIS”) monitoring snow thickness (on and off roadways), air temperature and dew point, but collectively they make up a densified network of data points.

Now you’re probably thinking, “if these are just traditional RWIS stations, what’s the big deal?

And the answer is simple; the difference lies in the intelligence.

The problem with traditional RWIS data

Traditional RWIS is designed to collect meteorological data about current conditions, and deliver that data to the end-user. According to the National Academies of Science, Engineering, Medicine, however,

“a recent study to identify weather information needs for surface transportation showed that within any given surface transportation sector, all users did not clearly understand how weather information could make a positive, significant difference in their operations.”

Or in other words, meteorological data is great, but what’s the point when the end-user can’t make enough sense of it to use it to their advantage.

And that was the issue the City of Magog was facing, in addition to a lack of a traditional RWIS network.

New technologies take RWIS to the next level

Next generation RWIS, like RWIS networks powered by WeatherBrain, goes above and beyond traditional RWIS to provide actionable environmental intelligence to the end-user. WeatherBrain does this by collecting data from densified networks of stations, as well as geo-relevant 3rd party data for a more robust data set. Extensive algorithms use this geo-relevant data, combine it with the nowcasts and forecasts they create, and produce actionable indicators that show the user when they’ll need to take action, effectively putting them ahead of impending weather events.

It’s this environmental intelligence that sets WeatherBrain apart from traditional RWIS, because it removes the uncertainty and guesswork from meteorological data analysis, allowing users to make confident, pro-active decisions that save time, money and resources.

No more scheduling stand-by crews ‘just in case,’ needlessly depleting overtime budgets.

No more wasting money on spreading de-icers at the wrong times.

And no more making uncertain critical business decisions in reaction to unexpected weather events.

The impact of next generation RWIS on Intelligent Transportation Systems

Next generation RWIS technologies like WeatherBrain, are designed to integrate seamlessly with established intelligent transportation and weather systems, and have the power to:

  • automate decision-making,
  • optimize road weather maintenance programs,
  • create opportunities for proactive maintenance measures, like anti-icing, and
  • reduce weather-related traffic injuries and fatalities.

And it’s because of positive impacts like these that the WeatherBrain project in Magog, Quebec was named one of the Top 5 Intelligent Transportation System Projects in Canada.

To read the full article published by Traffic Technology International, click here.