TripVista: Triple Perspective Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection

  • Authors:
  • Hanqi Guo;Zuchao Wang;Bowen Yu;Huijing Zhao;Xiaoru Yuan

  • Affiliations:
  • Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University, Beijing, China;Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University, Beijing, China

  • Venue:
  • PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
  • Year:
  • 2011

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Abstract

In this paper, we present an interactive visual analytics system, Triple Perspective Visual Trajectory Analytics (TripVista), for exploring and analyzing complex traffic trajectory data. The users are equipped with a carefully designed interface to inspect data interactively from three perspectives (spatial, temporal and multi-dimensional views). While most previous works, in both visualization and transportation research, focused on the macro aspects of traffic flows, we develop visualization methods to investigate and analyze microscopic traffic patterns and abnormal behaviors. In the spatial view of our system, traffic trajectories with various presentation styles are directly interactive with user brushing, together with convenient pattern exploration and selection through ring-style sliders. Improved ThemeRiver, embedded with glyphs indicating directional information, and multiple scatterplots with time as horizontal axes illustrate temporal information of the traffic flows. Our system also harnesses the power of parallel coordinates to visualize the multi-dimensional aspects of the traffic trajectory data. The above three view components are linked closely and interactively to provide access to multiple perspectives for users. Experiments show that our system is capable of effectively finding both regular and abnormal traffic flow patterns.