From Twitter to Traffic Predictor

Carnegie Mellon University - May 05, 2021 Sean Qian and Weiran Yao have used information extracted from tweets to provide unparalleled accuracy for predicting morning traffic patterns. Qian, associate professor of civil and environmental engineering , and Yao, Qian's Ph.D. candidate, published their results in Transportation Research. 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. However, the majority of people spend the time preceding their commute sleeping or performing their morning routines at home, leaving a large gap in predictive traffic data. Qian and Yao's method solves this problem by pulling data from tweets sent between the evening prior and early morning of the following day.