https://github.com/mwong009/simulation

Discrete event traffic simulation using simpy
https://github.com/mwong009/simulation

simpy sioux-falls

Last synced: about 2 months ago
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Discrete event traffic simulation using simpy

• Host: GitHub
• URL: https://github.com/mwong009/simulation
• Owner: mwong009
• Created: 2017-05-31T17:53:57.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2021-03-31T12:13:34.000Z (almost 4 years ago)
• Last Synced: 2024-08-02T17:33:03.197Z (5 months ago)
• Topics: simpy, sioux-falls
• Language: Python
• Homepage:
• Size: 817 KB
• Stars: 27
• Watchers: 3
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
simulation

==========

Requriements

------------

Requirements can be installed with Conda and pip

Python (3.5+) https://www.python.org/downloads/

NumPy (1.13+) https://scipy.org/install.html

Matplotlib (2.02+) https://matplotlib.org/

SimPy (3.0.10+) https://simpy.readthedocs.io/

opencv (3.0+) http://www.opencv.org/

Conda setup

-----------

Get miniconda from [here](https://docs.conda.io/en/latest/miniconda.html)

Install git:

    > conda install git 

Clone this repository

    > git clone https://github.com/mwong009/simulation.git simulation

cd into the directory

    > cd simulation

Installing

----------

From the project directory:

Windows:

    C:\...\simulation> pip install -r requirements.txt

Linux:

    /.../simulation> pip3 install -r requirements.txt

Usage and Documentation

-----------------------

SimPy is a process-based discrete-event simulation framework based on standard Python. Processes in SimPy are defined by Python generator functions and may, for example, be used to model active components like customers, vehicles or agents. SimPy also provides various types of shared resources to model limited capacity congestion points (like servers, checkout counters and tunnels).

From the project directory, run the command:

    > python script.py

On linux:

    > python3 script.py

**Hit spacebar to start**

Sample output:

    car 1 arrived on link 1E at 0.03s (Q=0)

    car 2 arrived on link 2S at 0.63s (Q=0)

    car 3 arrived on link 6N at 0.73s (Q=0)

    ...

    car 1 departed link 4E at 1.29s (Q=0)

    car 6 arrived on link 6N at 1.71s (Q=0)

    car 6 departed link 6N at 1.83s (Q=0)

    ...

    car 96 arrived on link 7E at 37.20s (Q=0)

    car 96 departed link 7E at 37.20s (Q=0)

    car 99 departed link 6N at 38.10s (Q=0)

    car 99 arrived on link 7E at 39.06s (Q=0)

    car 99 departed link 7E at 39.06s (Q=0)

Statistics can be generated

    [514 rows x 6 columns]

           totalTravelTime  totalSegments  meanWaitTime

    carID                                              

    1             3.759368              3      0.372207

    2             7.340689              3      1.282778

    3             2.007794              2      0.064265

    4             1.956925              1      1.140182

    5             5.664470              3      0.986793

    6             1.404893              2      0.055849

    7             4.237412              1      3.289740

    8             3.490860              1      3.139994

    9             2.345139              1      1.451333

    10            4.175994              1      3.244251

    11            7.098955              3      1.487988

    ...

Plotting done on Matplotlib