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

Work in progress ...

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

python cellular_automaton.py <optional arguments>

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)

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.

• 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.

the Asymmetric Simple Exclusion Process (ASEP)

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

python asep_slow.py <optional arguments>

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

• 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