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Traffic agents dispatch optimization model implementation for minimizing the total response time to traffic incidents using Cvxpy, Pandas, Numpy, and Plotly

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Traffic Incident Optimization

Traffic agents dispatch optimization model for minimizing the total response time of traffic incidents.

For visualizing the maps without cloning the repository and running it locally, please download the traffic.html file and open it with the browser of your choice.

Link del video de presentación

imagen

Members:

  • Diego Romero Iregui
  • Juan Pablo Gutierrez
  • Alejandro Romero
  • David Felipe Mora

To replicate the environment using Anaconda on linux64 run:

$ conda create --name traffic-incident-optimization --file requirements.txt

In order to be able to consume the OSRM API do the following:

wget http://download.geofabrik.de/south-america/colombia-latest.osm.pbf
docker run -t -v "${PWD}:/data" osrm/osrm-backend osrm-extract -p /opt/car.lua /data/colombia-latest.osm.pbf
docker run -t -v "${PWD}:/data" osrm/osrm-backend osrm-partition /data/colombia-latest.osrm
docker run -t -v "${PWD}:/data" osrm/osrm-backend osrm-customize /data/colombia-latest.osrm
docker run -t -i -p 5000:5000 -v "${PWD}:/data" osrm/osrm-backend osrm-routed --algorithm mld /data/colombia-latest.osrm --max-table-size 10000

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Traffic agents dispatch optimization model implementation for minimizing the total response time to traffic incidents using Cvxpy, Pandas, Numpy, and Plotly

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