Modeling the Impacts of Inclement Weather on Freeway Traffic Speed: An Exploratory Study Utilizing Social Media Data

Recently, there has been an increased interest in quantifying and modeling the impact of inclement weather on transportation system performance. One problem that the majority of previous research studies on the topic have faced is that they largely depended on weather data merely from atmospheric weather stations, which lacked information about road surface condition. The emergence of social media platforms, such as Twitter and Facebook, provides a new opportunity to extract more weather related data from such platforms. The current study has two primary objectives; first, to examine if real world weather events can be inferred from social media data, and secondly, to determine whether including weather variables, extracted from social media data, can improve the predictive accuracy of models developed to quantify the impact of inclement weather on freeway traffic speed. To achieve those objectives, weather data, Twitter data, and traffic information were compiled for the Buffalo-Niagara metropolitan area as a case study. A method called the Twitter Weather Events observation was then applied to the Twitter data, and the sensitivity and false alarm rate for the method was evaluated against real world weather data. Following this, linear regression models for predicting the impact of inclement weather on freeway speed were developed with and without the Twitter-based weather variables incorporated. The results indicate that Twitter data has a relatively high sensitivity for predicting inclement weather (i.e., snow) especially during the daytime and for areas with significant snowfall. They also show that the incorporation of Twitter-based weather variables can help improve the predictive accuracy of the models.