STAR: Real-time Spatio-Temporal Analysis and Prediction of Traffic Insights using Social Media

Abstract

The steady growth of data from social networks has resulted in wide-spread research in a host of application areas including transportation, health-care, customer-care and many more. Owing to the ubiquity and popularity of transportation (more recently) the growth in the number of problems reported by the masses has no bounds. With the advent of social media, reporting problems has become easier than before. In this paper, we address the problem of efficient management of transportation related woes by leveraging the information provided by social media sources such as -- Facebook, Twitter etc. We develop techniques for viral event detection, identify frequently co-occurring problem patterns and their root-causes and mine suggestions to solve the identified problems. We predict the occurrence of different problems, (with an accuracy of ≈ 80%) at different locations and times leveraging the analysis done above along with weather information and news reports. In addition, we design a feature-packed visualization that significantly enhances the ability to analyse data in real-time.