Developing an Interaction Ontology for characterising pedestrian movement behaviour

Since the introduction of Time Geography, the literature has witness a growing interest in representing and understanding human movement and its relationship with the environment. Although recent technology in personal tracking devices brought new potentialities in collecting and representing individual movements, methods to deal with the complexity and dynamism of collective movements are still lacking. This chapter introduces a spatial knowledge representation for the conceptualisation of pedestrian movement as a complex system based on the interactions. Movement interactions are defined and classified to represented global characteristics of the movement as emergent properties other than as a set of individual properties. The devised approach is exemplified through a case study on characterizing visitor behaviour in the Dwingelderveld National Park in The Netherlands.

[1]  Ralf Hartmut Güting,et al.  Moving Objects Databases , 2005 .

[2]  Michel Bierlaire,et al.  Discrete Choice Models for Pedestrian Walking Behavior , 2006 .

[3]  Alan Penn,et al.  Space syntax based agent simulation , 2001 .

[4]  Hongbo Yu,et al.  A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical–virtual space , 2009 .

[5]  Gennady L. Andrienko,et al.  A Visual Analytics Approach to Exploration of Large Amounts of Movement Data , 2008, VISUAL.

[6]  Hongbo Yu,et al.  A Space‐Time GIS Approach to Exploring Large Individual‐based Spatiotemporal Datasets , 2008, Trans. GIS.

[7]  H. Miller.  A MEASUREMENT THEORY FOR TIME GEOGRAPHY , 2005 .

[8]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[9]  Dino Pedreschi,et al.  Visually driven analysis of movement data by progressive clustering , 2008, Inf. Vis..

[10]  A. Turner,et al.  From Isovists to Visibility Graphs: A Methodology for the Analysis of Architectural Space , 2001 .

[11]  Serge P. Hoogendoorn,et al.  Controlled experiments to derive walking behaviour , 2002 .

[12]  Serge P. Hoogendoorn,et al.  Experimental Research of Pedestrian Walking Behavior , 2003 .

[13]  N. Andrienko,et al.  Basic Concepts of Movement Data , 2008, Mobility, Data Mining and Privacy.

[14]  Dino Pedreschi,et al.  Spatiotemporal Data Mining , 2008, Mobility, Data Mining and Privacy.

[15]  Sean Bechhofer,et al.  OWL: Web Ontology Language , 2009, Encyclopedia of Database Systems.

[16]  Robert Weibel,et al.  Towards a taxonomy of movement patterns , 2008, Inf. Vis..

[17]  Victor J. Blue,et al.  Cellular automata microsimulation for modeling bi-directional pedestrian walkways , 2001 .

[18]  Peter Szolovits,et al.  What Is a Knowledge Representation? , 1993, AI Mag..

[19]  Fausto Giunchiglia,et al.  Theories and uses of context in knowledge representation and reasoning , 2003 .

[20]  Fausto Giunchiglia,et al.  Introduction to Contextual Reasoning. An Artificial Intelligence Perspective , 1997 .

[21]  Fabio Porto,et al.  A conceptual view on trajectories , 2008, Data Knowl. Eng..

[22]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[23]  Dirk Helbing.  A Fluid-Dynamic Model for the Movement of Pedestrians , 1992, Complex Syst..

[24]  Robert Weibel,et al.  Discovering relative motion patterns in groups of moving point objects , 2005, Int. J. Geogr. Inf. Sci..

[25]  Torsten Hägerstraand.  WHAT ABOUT PEOPLE IN REGIONAL SCIENCE , 1970 .

[26]  Alan Penn,et al.  Natural Movement: Or, Configuration and Attraction in Urban Pedestrian Movement , 1993 .

[27]  Vania Bogorny,et al.  A model for enriching trajectories with semantic geographical information , 2007, GIS.

[28]  Joachim Gudmundsson,et al.  Reporting Leaders and Followers among Trajectories of Moving Point Objects , 2008, GeoInformatica.

[29]  Max J. Egenhofer,et al.  Modeling Moving Objects over Multiple Granularities , 2002, Annals of Mathematics and Artificial Intelligence.

[30]  Carl Stephen Smyth.  Mining mobile trajectories , 2001 .

[31]  Shih-Lung Shaw,et al.  Revisiting Hägerstrand’s time-geographic framework for individual activities in the age of instant access , 2007 .

[32]  Lubos Buzna,et al.  Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions , 2005, Transp. Sci..

[33]  A. Schadschneider,et al.  Simulation of pedestrian dynamics using a two dimensional cellular automaton , 2001 .

[34]  Hai Jin,et al.  From Principle to Practice , 2010 .

[35]  Joachim Gudmundsson,et al.  Reporting flock patterns , 2008, Comput. Geom..

[36]  Dirk Helbing,et al.  Self-Organizing Pedestrian Movement , 2001 .