Modeling the Impacts of Inclement Weather on Freeway Traffic Speed: Exploratory Study with Social Media Data. Issue 1 (January 2015)

• Record Type: Journal Article
• Authors: Lin, Lei; Ni, Ming; He, Qing; Gao, Jing; Sadek, Adel W.
• Is Part Of: Transportation research record. Volume 2482:Issue 1(2015)
• Page Start: 82
• Page End: 89
• Publication Date: 2015-01
• Subjects: Transportation -- Periodicals; Roads; Transport -- Périodiques; Routes -- Périodiques; Routes -- Conception et construction -- Périodiques; Roads; Transportation; 388.05
• DOI: 10.3141/2482-11
• Languages: English
• ISSNs: 0361-1981

Abstract

Recently, there has been increased interest in quantifying and modeling the impact of inclement weather on transportation system performance. One problem that the majority of research studies on the topic have faced was the great dependence on weather data merely from atmospheric weather stations, which lack 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. This study had two primary objectives: (a) examine whether real-world weather events can be inferred from social media data and (b) 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, New York, 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. Then, 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 indicated that Twitter data have a relatively high sensitivity for predicting inclement weather (i.e., snow), especially during the daytime and for areas with significant snowfall. The results also showed that the incorporation of Twitter-based weather variables could help improve the predictive accuracy of the models.