Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

  • Natalia AndrienkoEmail author
  • Gennady Andrienko
  • Salvatore Rinzivillo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9853)


By applying spatio-temporal aggregation to traffic data consisting of vehicle trajectories, we generate a spatially abstracted transportation network, which is a directed graph where nodes stand for territory compartments (areas in geographic space) and links (edges) are abstractions of the possible paths between neighboring areas. From time series of traffic characteristics obtained for the links, we reconstruct mathematical models of the interdependencies between the traffic intensity (a.k.a. traffic flow or flux) and mean velocity. Graphical representations of these interdependencies have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. We present the process of data-driven generation of traffic forecasting and simulation models, in which each step is supported by visual analytics techniques.


Traffic Flow Traffic Intensity Street Segment Traffic Data Dependency Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Natalia Andrienko
    • 1
    • 2
    Email author
  • Gennady Andrienko
    • 1
    • 2
  • Salvatore Rinzivillo
    • 3
  1. 1.Fraunhofer Institute IAISSankt AugustinGermany
  2. 2.City University LondonLondonUK
  3. 3.Area della Ricerca CNRIstituto di Scienza e Tecnologie dell’InformazionePisaItaly

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