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Linux Setup

Instructions for running the project directly with Python 3 on Linux (no Docker).

Prerequisites

Before following the steps below, install the system dependencies, set up SUMO, and install the Python requirements.

  1. Install system dependencies (requires sudo)

    Installs the libraries required by the libsumo C++ extension as well as SUMO and its tools.

    apt-get update && apt-get install -y --no-install-recommends \
    libx11-6 \
    libxext6 \
    libxrender1 \
    libgl1 \
    libatomic1 \
    libfontconfig1 \
    sumo \
    sumo-tools \
    && rm -rf /var/lib/apt/lists/*

  2. Set the SUMO_HOME environment variable

    Point SUMO_HOME to the installed SUMO directory.

    export SUMO_HOME=/usr/share/sumo

  3. Install Python dependencies

    pip install --no-cache-dir -r requirements.txt

How to run

  1. Run GA with PyGad

    This will run the PyGad Implementation of the Genetic Algorithm.

    python3 -m src.algorithms.simple_genetic_algorithm

  2. View map statistics

    Will display statistics of the downloaded map and traffic network

    python3 -m src.sumo_setup.statistics

  3. 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.

    python3 -m src.sumo_setup.generation

  4. Generate map

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

    cd src/sumo_setup && netconvert -c osm.netccfg

  5. Configure Simulator variables

    Configure SUMO Simulator variables when running every simulation. We are now using precalculated routes. this step is not necessary.

    cd src/sumo_setup && python3 $SUMO_HOME/tools/randomTrips.py -n osm.net.xml.gz -o [name of your routes file].rou.xml

    You can change from random trips to a specific configuration such as setting the specific number of cars generated per second or setting the total number of cars within every simulation.

  6. Discover TLS linkage

    This will discover linkage of TLS by Direct Linkage Empirical Discovery.

    python3 -m src.decomposition.dled_optimizer

  7. Execute DG2 Grouping

    This will execute Differential Grouping method as an alternative to linkage discovery. Theoretically it is faster than Embpirical Linkage Learning.

    python3 -m src.decomposition.DG2_grouping

  8. Execute IRRG

    python3 -m src.decomposition.IRRG

  9. Execute Random Search

    Performs random search of n solutions and m evaluations for each solutions.

    python3 -m src.algorithms.random_search

  10. Execute Custom Optimizer

    Runs the Custom Optimizer algorithm which was implemented based on LT-GOMEA.

    python3 -m src.algorithms.custom_optimizer

  11. Generate Distance Matrices

    Calculates the network distance matrices (Shortest, Euclidian, Fastest) used by the optimizer.

    python3 -m src.plot.tls_distances

  12. Generate Clustering Dendrograms

    Generates hierarchical clustering dendrogram plots to visualize the linkage tree structure.

    python3 -m src.plot.plot_dendrograms

  13. Analyze Linkage Statistics

    Analyzes the calculated distance matrices to determine the optimal clustering thresholds, calculating max valid non-singleton clusters and the median cluster size for each distance metric.

    python3 -m src.algorithms.linkage_analyzer

  14. Run Baseline Configuration

    Evaluates the current configuration of the instance map.

    python3 -m src.algorithms.evaluate_baseline

  15. Run LT-GA with PyGad

    This will run the PyGad Implementation of the custom LT - Genetic Algorithm.

    python3 -m src.algorithms.lt_genetic_algorithm

  16. Run Differential Evolution (SHADE)

    Runs the EvoX SHADE implementation on the baseline TLS configuration. Behavior depends on NOVEL_MUTATION in config.py: when False, a single plain SHADE run is performed; when True, three runs are executed (one per Ward distance tree: shortest / euclidian / fastest) with end-of-generation pair-cluster mutation applied to a MUTATION_RATE fraction of the population. Requires step 11 (distance matrices) when NOVEL_MUTATION=True.

    python3 -m src.algorithms.differential_evolution

Quick Start Pipeline (Rental Machine Setup)

If you would like to setup the whole pipeline, please complete the prerequisites above, then run steps 3, 4, 11, and 10 in order.