Research code used in the manuscript von Schantz, A., Ehtamo, H., & Hostikka, S. Minimization of mean-CVaR evacuation time using rescue guides: a scenario-based approach. arXiv:2010.03922 [physics.soc-ph]. 2020. (submitted manuscript).
The manuscript presents a procedure for solving the minimum time evacuation using rescue guides when accounting for different behavioral scenarios. The problem is formulated as a bi-objective scenario optimization problem, and it is solved with a combined numerical simulation and genetic algorithm (GA) procedure. The GA iteratively searches for the optimal evacuation plan, while the evacuation plan is evaluated with numerical simulations. This repository is its implementation. The numerical simulation model is the physics-inspired agent-based social force model. The GA is the Nondominated Sorting Genetic Algorithm II (NSGA-II) [2,3]. It is suited for solving bi-objective optimization problems; this repository includes my own implementation of it.
The numerical evacuation simulation and its GUI is based on research assistant Jaan Tollander's codes https://github.com/jaantollander/crowddynamics and https://github.com/jaantollander/crowddynamics-qtgui, which he created when he was working in our research group in Aalto University School of Science, Department of Mathematics and Systems Analysis summers 2016 and 2017.
The repository includes codes for NSGA-II, simulation and a graphical user interface (GUI). The folders in the repository:
- crowddynamics-simulation contains files for running the GUI
- crowddynamics-qtgui contains the files that build the GUI
- crowddynamics contains all files for simulating the movement of a crowd
- genetic algorithm includes files to run the combined numerical simulation and NSGA-II
- misc includes files used in configuring the optimization problem
The numerical evacuation simulations are implemented in Python and the NSGA-II is implemented in Python and Bash that were run on a high performance computing cluster. It should be noted that the procedure is currently computationally very demanding.
See the readme.txt file in each folder for a more detailed overview of the codes in each folder.
Using Linux is recommended. The code works at least on Ubuntu 16.04. Do the following steps to install the repository:
- Install anaconda (https://docs.anaconda.com/anaconda/install/linux)
- Set environment variables
export PATH=/.../anaconda3/bin:$PATHandexport PYTHONPATH=/.../anaconda3/bin:$PYTHONPATH - Clone the repository
- On terminal run
conda config --add channels conda-forge - Create a conda environment from the file crowddynamics/environment.yml
- On terminal run
source activate multiobj-guided-evac - On terminal, in folder crowddynamics,
run pip install --editable . - Change to folder crowddynamics-qtgui and run
pip install -r requirements.txt - Run
conda install pyqt=4 - Run
conda install pyqtgraph==0.10.0 - Run
conda install scikit-fmm==0.0.9 - Run
pip install anytree==2.1.4 - Run
pip install --editable .
You might occur problems in installing some of the python packages. You can install these packages manually using conda install or pip install.
[1] von Schantz, A., Ehtamo, H., & Hostikka, S. (2020). Minimization of mean-CVaR evacuation time using rescue guides: a scenario-based approach. manuscript.
[2] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
[3] Fortin, F. A., & Parizeau, M. (2013, July). Revisiting the NSGA-II crowding-distance computation. In Proceedings of the 15th annual conference on Genetic and evolutionary computation (pp. 623-630).