Pedestrian motion modelled by Fokker–Planck Nash games

A new approach to modelling pedestrians' avoidance dynamics based on a Fokker–Planck (FP) Nash game framework is presented. In this framework, two interacting pedestrians are considered, whose motion variability is modelled through the corresponding probability density functions (PDFs) governed by FP equations. Based on these equations, a Nash differential game is formulated where the game strategies represent controls aiming at avoidance by minimizing appropriate collision cost functionals. The existence of Nash equilibria solutions is proved and characterized as a solution to an optimal control problem that is solved numerically. Results of numerical experiments are presented that successfully compare the computed Nash equilibria to the output of real experiments (conducted with humans) for four test cases.

[1]  Lorenzo Pareschi,et al.  Mathematical Modeling of Collective Behavior in Socio-Economic and Life Sciences , 2010 .

[2]  M. Pachter,et al.  Optimal control of partial differential equations , 1980 .

[3]  K. Hoffmann,et al.  Optimal Control of Partial Differential Equations , 1991 .

[4]  L. F. Henderson On the fluid mechanics of human crowd motion , 1974 .

[5]  Nicola Bellomo,et al.  From the Microscale to Collective Crowd Dynamics , 2013, Multiscale Model. Simul..

[6]  Alfio Borzì,et al.  A Fokker-Planck control framework for multidimensional stochastic processes , 2013, J. Comput. Appl. Math..

[7]  J. Pettré,et al.  Minimal predicted distance: a common metric for collision avoidance during pairwise interactions between walkers. , 2012, Gait & posture.

[8]  N Bellomo,et al.  Crowd dynamics and safety: Reply to comments on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management". , 2016, Physics of life reviews.

[9]  Jennifer Prestigiacomo,et al.  A Hybrid Approach , 2018, How High the Sky?.

[10]  P. Neittaanmäki,et al.  Optimal Control of Nonlinear Parabolic Systems: Theory: Algorithms and Applications , 1994 .

[11]  Martin Buss,et al.  Understanding Human Avoidance Behavior: Interaction-Aware Decision Making Based on Game Theory , 2016, Int. J. Soc. Robotics.

[12]  Robert Gibbons,et al.  A primer in game theory , 1992 .

[13]  Cédric Sueur,et al.  Cultural influence of social information use in pedestrian road-crossing behaviours , 2017, Royal Society Open Science.

[14]  J. Lions Quelques méthodes de résolution de problèmes aux limites non linéaires , 1969 .

[15]  N Bellomo,et al.  Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management. , 2016, Physics of life reviews.

[16]  Marie-Therese Wolfram,et al.  On a mean field game approach modeling congestion and aversion in pedestrian crowds , 2011 .

[17]  Paola Goatin,et al.  A mixed system modeling two-directional pedestrian flows. , 2014, Mathematical biosciences and engineering : MBE.

[18]  B. Piccoli,et al.  Time-Evolving Measures and Macroscopic Modeling of Pedestrian Flow , 2008, 0811.3383.

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

[20]  Stefan Wendl,et al.  Optimal Control of Partial Differential Equations , 2021, Applied Mathematical Sciences.

[21]  J. Nash NON-COOPERATIVE GAMES , 1951, Classics in Game Theory.

[22]  A Colombi,et al.  Modelling human perception processes in pedestrian dynamics: a hybrid approach , 2017, Royal Society Open Science.

[23]  Marie-Therese Wolfram,et al.  Collision avoidance in pedestrian dynamics , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[24]  Alfio Borzì,et al.  A Fokker–Planck Feedback Control-Constrained Approach for Modelling Crowd Motion , 2016 .

[25]  Marco Caponigro,et al.  A control theoretical approach to crowd management: Comment on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management" by Nicola Bellomo et al. , 2016, Physics of life reviews.

[26]  Eric van Damme,et al.  Non-Cooperative Games , 2000 .

[27]  B. Piccoli,et al.  Multiscale Modeling of Pedestrian Dynamics , 2014 .

[28]  Stefan Glasauer,et al.  Adjustments of Speed and Path when Avoiding Collisions with Another Pedestrian , 2014, PloS one.

[29]  Serge P. Hoogendoorn,et al.  Pedestrian route-choice and activity scheduling theory and models , 2004 .

[30]  R. Colombo,et al.  Macroscopic Models for Pedestrian Flows , 2010 .

[31]  Alan J. Mayne,et al.  Some further results in the theory of pedestrians and road traffic , 1954 .

[32]  Serge P. Hoogendoorn,et al.  Simulation of pedestrian flows by optimal control and differential games , 2003 .

[33]  Alfio Borzì,et al.  Computational Optimization of Systems Governed by Partial Differential Equations , 2012, Computational science and engineering.