Stigmergy-Based Modeling to Discover Urban Activity Patterns from Positioning Data

  • Antonio Luca Alfeo
  • Mario Giovanni C. A. Cimino
  • Sara Egidi
  • Bruno Lepri
  • Alex Pentland
  • Gigliola Vaglini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)

Abstract

Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high-density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015.

Keywords

Urban mobility Stigmergy Emergent paradigm Hotspot Pattern mining Taxi-GPS traces 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Antonio Luca Alfeo
    • 1
  • Mario Giovanni C. A. Cimino
    • 1
  • Sara Egidi
    • 1
  • Bruno Lepri
    • 2
  • Alex Pentland
    • 3
  • Gigliola Vaglini
    • 1
  1. 1.University of PisaPisaItaly
  2. 2.Bruno Kessler FoundationTrentoItaly
  3. 3.M.I.T. Media LaboratoryCambridgeUSA

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