Synthetizing Qualitative (Logical) Patterns for Pedestrian Simulation from Data

  • Gonzalo A. Aranda-Corral
  • Joaquín Borrego-Díaz
  • Juan Galán-PáezEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


This work introduces a (qualitative) data-driven framework to extract patterns of pedestrian behaviour and synthesize Agent-Based Models. The idea consists in obtaining a rule-based model of pedestrian behaviour by means of automated methods from data mining. In order to extract qualitative rules from data, a mathematical theory called Formal Concept Analysis (FCA) is used. FCA also provides tools for implicational reasoning, which facilitates the design of qualitative simulations from both, observations and other models of pedestrian mobility. The robustness of the method on a general agent-based setting of movable agents within a grid is shown.


Agent-based modelling Knowledge acquisition Qualitative spatial reasoning Formal concept analysis 



Work partially supported by TIN2013- 41086-P (Spanish Ministry of Economy and Competitiveness), co-financed with FEDER funds.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Gonzalo A. Aranda-Corral
    • 1
  • Joaquín Borrego-Díaz
    • 1
  • Juan Galán-Páez
    • 2
    • 3
    Email author
  1. 1.Department of Information TechnologyUniversidad de HuelvaPalos de La FronteraSpain
  2. 2.Department of Computer Science and AIUniversidad de SevillaSevillaSpain
  3. 3.Datrik IntelligenceSevillaSpain

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