Pedestrian movement analysis in transfer station corridor: Velocity-based and acceleration-based

In this paper, pedestrians are classified into aggressive and conservative ones by their temper. Aggressive pedestrians’ walking through crowd in transfer station corridor is analyzed. Treating pedestrians as particles, this paper uses the modified social force model (MSFM) as the building block, where forces involve self-driving force, repulsive force and friction force. The proposed model in this paper is a discrete model combining the MSFM and cellular automata (CA) model, where the updating rules of the CA are redefined with MSFM. Due to the continuity of values generated by the MSFM, we use the fuzzy logic to discretize the continuous values into cells pedestrians can move in one step. With the observation that stimulus around pedestrians influences their acceleration directly, an acceleration-based movement model is presented, compared to the generally reviewed velocity-based movement model. In the acceleration-based model, a discretized version of kinematic equation is presented based on the acceleration discretized with fuzzy logic. In real life, some pedestrians would rather keep their desired speed and this is also mimicked in this paper, which is called inertia. Compared to the simple triangular membership function, a trapezoidal membership function and a piecewise linear membership function are used to capture pedestrians’ inertia. With the trapezoidal and the piecewise linear membership function, many overlapping scenarios should be carefully handled and Dubois and Prade’s four-index method is used to completely describe the relative relationship of fuzzy quantities. Finally, a simulation is constructed to demonstrate the effect of our model.

[1]  Benedetto Piccoli,et al.  Modeling self-organization in pedestrians and animal groups from macroscopic and microscopic viewpoints , 2009, 0906.4702.

[2]  G. Bortolan,et al.  A review of some methods for ranking fuzzy subsets , 1985 .

[3]  J. Baldwin,et al.  Comparison of fuzzy sets on the same decision space , 1979 .

[4]  Xiaoping Zheng,et al.  Conflict game in evacuation process: A study combining Cellular Automata model , 2011 .

[5]  Keemin Sohn,et al.  Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation , 2013 .

[6]  Aya Hagishima,et al.  Study of bottleneck effect at an emergency evacuation exit using cellular automata model, mean field approximation analysis, and game theory , 2010 .

[7]  B. D. Hankin,et al.  Passenger Flow in Subways , 1958 .

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

[9]  Andreas Schadschneider,et al.  Cellular Automaton Simulations of Pedestrian Dynamics and Evacuation Processes , 2003 .

[10]  Huang Huang,et al.  Simulation of bidirectional pedestrian flow in transfer station corridor based on multi forces , 2014 .

[11]  Ziyou Gao,et al.  Simulating the Dynamic Escape Process in Large Public Places , 2014, Oper. Res..

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

[13]  Roger L. Hughes,et al.  A continuum theory for the flow of pedestrians , 2002 .

[14]  F. Santambrogio,et al.  A MACROSCOPIC CROWD MOTION MODEL OF GRADIENT FLOW TYPE , 2010, 1002.0686.

[15]  Luca Bruno,et al.  Crowd dynamics on a moving platform: Mathematical modelling and application to lively footbridges , 2007, Math. Comput. Model..

[16]  N. Bellomo,et al.  ON THE MODELLING CROWD DYNAMICS FROM SCALING TO HYPERBOLIC MACROSCOPIC MODELS , 2008 .

[17]  Christian Dogbé,et al.  Modeling crowd dynamics by the mean-field limit approach , 2010, Math. Comput. Model..

[18]  P. Lions,et al.  Mean field games , 2007 .

[19]  Christophe Chalons Numerical Approximation of a Macroscopic Model of Pedestrian Flows , 2007, SIAM J. Sci. Comput..

[20]  R. Colombo,et al.  Pedestrian flows and non‐classical shocks , 2005 .

[21]  V. Coscia,et al.  FIRST-ORDER MACROSCOPIC MODELLING OF HUMAN CROWD DYNAMICS , 2008 .

[22]  Bin Ran,et al.  A cell-based study on pedestrian acceleration and overtaking in a transfer station corridor , 2013 .

[23]  Huibert Kwakernaak,et al.  Rating and ranking of multiple-aspect alternatives using fuzzy sets , 1976, Autom..

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

[25]  J L Adler,et al.  Emergent Fundamental Pedestrian Flows from Cellular Automata Microsimulation , 1998 .

[26]  R. Yager ON CHOOSING BETWEEN FUZZY SUBSETS , 1980 .

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

[28]  Benedetto Piccoli,et al.  Multiscale Modeling of Granular Flows with Application to Crowd Dynamics , 2010, Multiscale Model. Simul..

[29]  J. Zittartz,et al.  Cellular Automaton Approach to Pedestrian Dynamics - Applications , 2001, cond-mat/0112119.

[30]  Serge P. Hoogendoorn,et al.  Continuum modelling of pedestrian flows: From microscopic principles to self-organised macroscopic phenomena , 2014 .

[31]  Vincent Henn,et al.  Fuzzy route choice model for traffic assignment , 2000, Fuzzy Sets Syst..

[32]  Didier Dubois,et al.  Ranking fuzzy numbers in the setting of possibility theory , 1983, Inf. Sci..

[33]  Bin Ran,et al.  A Study on Pedestrian Choice between Stairway and Escalator in Transfer Station Based on Floor Field Cellular Automata , 2013 .

[34]  R. Colombo,et al.  A Macroscopic model for Pedestrian Flows in Panic Situations , 2010 .

[35]  Benedetto Piccoli,et al.  Pedestrian flows in bounded domains with obstacles , 2008, 0812.4390.

[36]  Debora Amadori,et al.  The one-dimensional Hughes model for pedestrian flow: Riemann—type solutions , 2012 .

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

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