By Dan Carroll
May 13, 2021
Researchers have used information extracted from tweets to provide unparalleled accuracy for predicting morning traffic patterns.
The morning commute period is one of the busiest times of day for traffic; however, it has also proven to be the most difficult time to predict traffic patterns. This is because most methods for traffic prediction rely on having a consistent flow of traffic data from the time leading up to the predicted period.
The majority of people, however, spend the time preceding their commute sleeping or performing their morning routines at home, leaving a large gap in predictive traffic data.
Researchers have used information extracted from tweets to provide unparalleled accuracy for predicting morning traffic patterns.
The morning commute period is one of the busiest times of day for traffic; however, it has also proven to be the most difficult time to predict traffic patterns. This is because most methods for traffic prediction rely on having a consistent flow of traffic data from the time leading up to the predicted period.
The majority of people, however, spend the time preceding their commute sleeping or performing their morning routines at home, leaving a large gap in predictive traffic data.
The researchers’ method solves this problem by pulling data from tweets sent between the evening prior and early morning of the following day. They first used Twitter’s application programming interface (API) to identify tweets within a given area (in this case, the city of Pittsburgh) with geotags indicating from where they were sent. They then used another application