Traffic Light Signalling Control Optimization

This is my take on optimizing traffic light controls as a discrete type problem with genetic algorithms.

Optimization process

How to run

1. Building with Docker

   This will build the project with Docker. This must be done before running the program.

   docker build -t tls_optimization .

2. Running with Docker

   This will run the project with Docker.

   docker run --rm -v $(pwd):/app -w /app tls_optimization

3. View map statistics

   Will display statistics of the downloaded map and traffic network

   docker run --rm -v $(pwd):/app -w /app tls_optimization python -m src.sumo_setup.statistics

4. Generate network data

   This will generate network data that will have phase durations assigned for individual TLS. This is required step to run the optimization algorithm.

   docker run --rm -v $(pwd):/app -w /app tls_optimization python -m src.sumo_setup.generation

5. Generate map

   This will generate a new map following osm.netccfg configurations.

   docker run --rm -v $(pwd):/app -w /app/src/sumo_setup tls_optimization netconvert -c osm.netccfg

Docker cleaning commands

Docker has build-in commands that are ment to be used for house keeping tasks:

• docker image prune: delete all dangling images (as in without an assigned tag)
• docker image prune -a: delete all images not used by any container
• docker system prune: delete stopped containers, unused networks and dangling image + dangling build cache
• docker system prune -a: delete stopped containers, unused networks, images not used by any container + all build cache