A Novel Movement Model for Pedestrians Suitable for Personal Navigation

In this paper a combination of two movement models, operating at the microscopic level and suitable for pedestrian navigation is developed and tested. The constituents are a Stochastic Behavioral Movement Model to characterize more random motion and a Diffusion Movement Model to characterize a geographic goal a pedestrian might walk towards. A top-level Markov process is used to determine whether to currently use the stochastic behavioral or the diffusion model; therefore, the model switches between motion that is more goal oriented (diffusion model) or stochastic. Advantages and disadvantages of both individual constituent models are demonstrated and discussed. The combined movement model is demonstrated to achieve the best of both worlds and to avoid the problems associated with using a single model. The properties and the performance of the resulting model will be explained in details.

[1]  Fan Weicheng,et al.  Simulation of bi-direction pedestrian movement using a cellular automata model , 2003 .

[2]  Hani S. Mahmassani,et al.  A Speed-Concentration Relation for Bi-directional Crowd Moments with Strong Interaction , 2001 .

[3]  Duncan Cavens,et al.  A Message-Based Framework for Real-World Mobility Simulations , 2005, Applications of Agent Technology in Traffic and Transportation.

[4]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

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

[6]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[7]  C. Y. Lee.  An Algorithm for Path Connections and Its Applications , 1961, IRE Trans. Electron. Comput..

[8]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[9]  Ramakant Nevatia,et al.  3D tracking of human locomotion: a tracking as recognition approach , 2002, Object recognition supported by user interaction for service robots.

[10]  K. Nagel,et al.  Generating complete all-day activity plans with genetic algorithms , 2005 .

[11]  Mohammed Khider,et al.  Implementation of a Simulator/Demonstrator for the SoftLocation Concept using Bayesian Filters , 2005 .

[12]  J. D. Addison.  Flow on links: Yesterday, today and tomorrow , 1998 .

[13]  Ling Guan,et al.  Quantifying and recognizing human movement patterns from monocular video Images-part I: a new framework for modeling human motion , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Shigeyuki Okazaki,et al.  A study of simulation model for pedestrian movement with evacuation and queuing , 1993 .

[15]  Serge P. Hoogendoorn,et al.  Gas-Kinetic Modeling and Simulation of Pedestrian Flows , 2000 .

[16]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[17]  Alex Pentland,et al.  Modeling and Prediction of Human Behavior , 1999, Neural Computation.

[18]  Jens Kammann,et al.  A new mobility model based on maps , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[19]  Dirk Helbing,et al.  A mathematical model for the behavior of pedestrians , 1991, cond-mat/9805202.

[20]  Weicheng Fan,et al.  Cellular automata pedestrian movement model considering human behavior , 2003 .

[21]  Michel Bierlaire,et al.  Simulation of Pedestrian Behaviour using a Discrete Choice Model Calibrated on Actual Motion Data , 2004 .

[22]  Andrew Adam andyadam,et al.  Identifying Humans by Their Walk and Generating New Motions Using Hidden Markov Models , 2004 .

[23]  David Charypar,et al.  Q-Learning for Flexible Learning of Daily Activity Plans , 2005 .

[24]  Duncan Cavens,et al.  Distributed intelligence in pedestrian simulations , 2007 .

[25]  Kianoush Azarm,et al.  Mobile robot path planning and execution based on a diffusion equation strategy , 1992, Adv. Robotics.

[26]  Taras I. Lakoba,et al.  Modifications of the Helbing-Molnár-Farkas-Vicsek Social Force Model for Pedestrian Evolution , 2005, Simul..

[27]  Mohammed Khider,et al.  Continuous location and direction estimation with multiple sensors using particle filtering , 2006, 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.