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README.md

Purpose

Implementation of some known cellular automata. Use for academic purposes only. Work in progress ...

Floor Field Model (cellular_automaton.py)

Reference

Cellular Automaton. Floor Field Model [Burstedde2001] Simulation of pedestriandynamics using a two-dimensional cellular automaton Physica A, 295, 507-525, 2001

Usage

python cellular_automaton.py <optional arguments>

Optional arguments

option value description
-h, --help show this help message and exit
-s, --ks KS sensitivity parameter for the Static Floor Field (default 2)
-d, --kd KD sensitivity parameter for the Dynamic Floor Field (default 1)
-n, --numPeds NUMPEDS Number of agents (default 10)
-p, --plotS plot Static Floor Field
--plotD plot Dynamic Floor Field
--plotAvgD plot average Dynamic Floor Field
-P, --plotP plot Pedestrians
-r, --shuffle random shuffle
-v, --reverse reverse sequential update
-l, --log LOG log file (default log.dat)
--decay DECAY the decay probability of the Dynamic Floor Field (default 0.2)
--diffusion DIFFUSION the diffusion probability of the Dynamic Floor Field (default 0.2)
-W, --width WIDTH the width of the simulation area in meter, excluding walls
-H, --height HEIGHT the height of the simulation room in meter, excluding walls
-c, --clean remove files from directories dff/ sff/ and peds/
-N, --nruns NRUNS repeat the simulation N times (default 1)
--parallel use multithreading
--moore use moore neighborhood (default Von Neumann)
--box from_x to_x from_y to_y Rectangular box defined by 4 numbers, where agents will be distributed. (default the whole room)

Simulation results

With the following parameter:

  • Width of room: 30 m
  • Hight of the room: 30 m
  • Number of runs: 1
  • Number of pedestrians 2000
  • Diffusion parameter: 2
  • Decay parameter: 0.2
  • Static FF parameter: 2
  • Dynamic FF parameter: 5

Call:

python cellular_automaton.py -W 30 -H 30  -N 1 -n 2000 --diffusion 2 --plotAvgD  --plotD -d 5 -s 2 -P
  • Video simulation

  • Dynamics floor field (averaged over time)

Comparison of different neighborhoods

Call the script with the option -moore to use the moore neighborhood. Otherwise, von Neumann neighborhood will be used as default.

The choice of the neighborhood has an influence on the evacuation time, as can seen below.

evactime

Moore neighborhood (video)

./figs/SFF_moore.png

von Neumann neighborhood (video)

./figs/SFF_neumann.png

Todos:

  • todo: Different update schemes: sequential, shuffle sequential, reverse sequential.
  • todo: visualisation of the cell states: (option -P)
  • todo: make a video from the png's
  • todo: track cells with id for further trajectory analysis.
  • todo: implement the dynamic floor field
  • todo: implement the parallel update
  • todo: implement the conflict friction mu (in case of the parallel update)
  • todo: read geometry from a png file. See read_png.py.

ASEP model (asep_fast.py)

the Asymmetric Simple Exclusion Process (ASEP)

Reference

Rajewsky, N. and Santen, L. and Schadschneider, A. and Schreckenberg, M. The asymmetric exclusion process: Comparison of update procedures Journal of Statistical Physics, 1998

Usage

python asep_slow.py <optional arguments>

Optional arguments

option value descrption
-h, --help show this help message and exit
-n, --np NUMPEDS number of agents (default 10)
-N, --nr NRUNS number of runs (default 1)
-m, --ms MS max simulation steps (default 100)
-w, --width WIDTH width of the system (default 50)
-p, --plotP plot Pedestrians
-r, --shuffle random shuffle
-v, --reverse reverse sequential update
-l, --log LOG log file (default log.dat)

Simulation results

the theoretical fundamental diagram can be reproduced, see figure. The size of the system should be reasonably high and the simulation time also.

Todos

  • todo: implement TASEP
  • todo: implement sequential update with all its variants.
  • Remarque: There are two implementations of the asep. One it optimized using vector-operations from numpy (asep_fast.py) and the other implementation is using explicit loops (asep_slow.py). The naming of the two variations is justified when measuring their execution time:
    python make_fd.py asep_fast.py:         0:56.71 real,   52.12 user,     4.03 sys
    python make_fd.py asep_slow.py:         1:15.42 real,   70.55 user,     4.23 sys
    

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cellular automata for pedestrian dynamics

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