Dynamic congestion analysis for better traffic management using social media

Abstract

Social media has emerged as an imperative tool for addressing many real-life problems in an innovative way in recent years. Traffic management is a demanding problem for any populous city in the world. In the current paper, we explore how the dynamic data from social media can be employed for continuous traffic monitoring of cities in a better way. To accomplish this, congestion analysis and clustering of congested areas are performed. With the term congestion, we denote co-gatherings in an area for two different occasions within a defined time interval. While doing so, we introduce a novel measure for quantifying the congestion of different areas in a city. Subsequently, association rule mining is applied to find out the association between congested roads. To our surprise, we observe a major impact of various gatherings on the disorder of traffic control in many cities. With additional analyses, we gain some new insights about the overall status of traffic quality in India from the temporal analysis of data.