Visual Exploration of Changes in Passenger Flows and Tweets on Mega-City Metro Network - 2016


Transportation systems in mega-cities are typically affected by various kinds of events like natural disasters, accidents, and public gatherings. Highly dense and difficult networks within the transportation systems propagate confusion within the network as a result of they provide varied attainable transfer routes to passengers. Visualization is one in all the most vital techniques for examining such cascades of uncommon situations in the massive networks. This paper proposes visual integration of traffic analysis and social media analysis using two forms of huge knowledge: good card information on the Tokyo Metro and social media knowledge on Twitter. Our system provides multiple coordinated views to visually, intuitively, and simultaneously explore changes in passengers' behavior and abnormal things extracted from sensible card information and situational explanations from real voices of passengers such as complaints regarding services extracted from social media information. We demonstrate the chances and usefulness of our novel visualization environment employing a series of real information case studies and domain experts' feedbacks concerning varied types of events.

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