An investigation on human dynamics in enclosed spaces

Abstract In this article, we introduce a method for analysing specific dynamical properties associated with the movement of people on a two-dimensional (compact) space. We focus on a variety of features defined by the topology and dynamics of the system to investigate human dynamics in enclosed spaces. This can potentially have significant applications within systems defined by human behaviour, particularly relevant to disaster management, internet of things (IoT), and big data analytics.

[1]  Alan Penn,et al.  Encoding Natural Movement as an Agent-Based System: An Investigation into Human Pedestrian Behaviour in the Built Environment , 2002 .

[2]  Ing-Jr Ding,et al.  Three-layered hierarchical scheme with a Kinect sensor microphone array for audio-based human behavior recognition , 2016, Comput. Electr. Eng..

[3]  Nik Bessis,et al.  Automated extraction of fragments of Bayesian networks from textual sources , 2017, Appl. Soft Comput..

[4]  Evangelos Pournaras,et al.  Self-regulatory information sharing in participatory social sensing , 2016, EPJ Data Science.

[5]  Bruce A. Whitehead,et al.  James J. Gibson: The ecological approach to visual perception. Boston: Houghton Mifflin, 1979, 332 pp , 1981 .

[6]  Nik Bessis,et al.  An influence assessment method based on co-occurrence for topologically reduced big data sets , 2016, Soft Comput..

[7]  Ciprian Dobre,et al.  Big Data and Internet of Things: A Roadmap for Smart Environments , 2014, Big Data and Internet of Things.

[8]  Bingbing Ni,et al.  Crowded Scene Analysis: A Survey , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  Jaroslaw Was,et al.  Towards realistic and effective Agent-based models of crowd dynamics , 2014, Neurocomputing.

[10]  Fei-Fei Li,et al.  Socially-Aware Large-Scale Crowd Forecasting , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Fabio Persia,et al.  Recognizing human behaviours in online social networks , 2018, Comput. Secur..

[12]  Andreas Schadschneider,et al.  Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics , 2002 .

[13]  Marcello Trovati,et al.  Reduced Topologically Real-World Networks: A Big-Data Approach , 2015, Int. J. Distributed Syst. Technol..

[14]  Dirk Helbing,et al.  How simple rules determine pedestrian behavior and crowd disasters , 2011, Proceedings of the National Academy of Sciences.

[15]  Katsuhiro Nishinari,et al.  Modelling of self-driven particles: Foraging ants and pedestrians , 2006 .

[16]  Nik Bessis,et al.  Buildings and Crowds: Forming Smart Cities for More Effective Disaster Management , 2011, 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[17]  Mo M. Jamshidi,et al.  System of Systems and Big Data analytics - Bridging the gap , 2014, Comput. Electr. Eng..

[18]  Angelo Chianese,et al.  Cultural heritage and new technologies: trends and challenges , 2016, Personal and Ubiquitous Computing.