When the snow starts to fall, hashtags like #snow and #weather start to pop up on Twitter. Experts think it might be possible to track all that data to manage traffic during storms and make winter driving a little safer.
“It doesn’t matter if someone tweets about how beautiful the snow is or if they’re complaining about unplowed roads. Twitter users provide an unparalleled amount of hyperlocal data that we can use to improve our ability to direct traffic during snowstorms and adverse weather,” says Adel Sadek, director of the Institute for Sustainable Transportation and Logistics at the University at Buffalo.
Traffic planners rely on models that analyze vehicular data from cameras and sensors, as well as weather data from nearby weather stations. While the approach works, its accuracy is limited because traffic and weather observations don’t provide information on road surface conditions. For example, the model doesn’t consider ice that lingers after a storm, or that snowplows 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 GPS.
For a new study, published in the journal Transportation Research Record, researchers examined more than 360,000 tweets in the Buffalo Niagara region from 19 days in December 2013 and identified roughly 3,000 relevant tweets by tagging keywords such as “snow” and “melt.”
Next, they refined the data via a method they call Twitter Weather Events Observation which put events into two categories:
- Weather utterances, like “The roads are a hot mess out in the burbs all over. Snowing like CRAZY up in here … drive safe everyone.”
- Weather reports, like #BuffaloNY #Weather #Outside. #Cold #Snowing #Windy. @Parkside Candy http://t.co/IfyzICtGPW
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 don’t match actual weather. Because 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 (in the study’s case, Buffalo has roughly five times more people than Niagara Falls, New York).
More precise models can usher in a host of improvements to freeways during inclement weather, the researchers say. For example, they can help traffic planners recommend better safe driving speeds, which roads need to be cleared of snow or avoided, and expected arrival times for motorists.
Researchers plan to continue improving their model by acquiring additional Twitter data for longer periods of time and at different locations.
The Transportation Informatics Tier I University Transportation Center provided funding for the study.