Improving Urban Transportation through Social Media Analytics

Abstract

Citizens tend to discuss issues in public forums, social media, and web blogs. Given that issues related to public transportation are most actively reported across web-based sources, we present a holistic framework for collection, categorization, aggregation and visualization of urban public transportation issues. The primary challenges in deriving useful insights from web-based sources, stem from -- (a) the number of reports; (b) incomplete or implicit spatio-temporal context; and the (c) unstructured nature of text in these reports. The work initiates with the formal complaint data from the largest public transportation agency in Bangalore, complemented by complaint reports from web-based and social media sources. Text data is categorized into different transportation related problems and spatio-temporal context is added to the text data for geo-tagging and identifying persistent issues. A well-organized dashboard is developed for efficient visualization. The dashboard is currently being piloted with the largest transportation agency in Bangalore.