New York: Your rants about the weather on Twitter could help other motorists avoid slippery, congested roads during inclement weather, a new study suggests. Weather-related tweets can be analysed to bolster computer models that recommend safe driving speeds and which roads motorists should avoid during snow or rains, researchers said.
Traffic planners rely on models that analyse vehicular data from cameras and sensors, as well as weather data from nearby weather stations. However, the accuracy of this approach is limited because traffic and weather observations do not provide information on road surface conditions, the researchers said.
For example, the model does not consider ice that lingers after a storm, or that snowploughs have cleared a road. Twitter can help address this limitation because its users often tweet about the weather and road surface conditions, and many opt to share their location via Global Positioning System (GPS).
The study conducted by researchers at the University of Buffalo in New York examined more than 360,000 tweets in the Buffalo Niagara region from 19 days in December 2013. Researchers identified roughly 3,000 relevant tweets by tagging keywords such as ‘snow’ and ‘melt.’
They then refined the data via a method they call Twitter Weather Events Observation which classifies events in two ways – ‘weather utterance’ and ‘weather report’.
Once the number of events reach a threshold for a given time, they are counted as a ‘Twitter weather event.’ Researchers tested the reliability of these events through metrics designed to eliminate tweets that do not match actual weather. Because the tweets contain geographic coordinates, researchers were able to map the exact locations of where the inclement weather was reported.
Next, they looked at the timing of the tweets and saw a pattern. When snow falls, the number of weather-related tweets increases, the average motor vehicle speed drops and traffic volumes slowly decrease.
Researchers then inserted the Twitter data into a model containing traffic and weather information, and found that the incorporation of such data improved the accuracy of such models.
In particular, researchers found Twitter data to be more effective during the day (when more people tweet), and where the population is bigger. “It doesn’t matter if someone tweets about how beautiful the snow is or if they’re complaining about unplowed roads,” said Adel Sadek of the University of Buffalo, who led the study.
“Twitter users provide an unparallelled amount of hyperlocal data that we can use to improve our ability to direct traffic during snowstorms and adverse weather,” Sadek said. The research was published in the journal Transportation Research Record.