You should install SUMO, an urban traffic simulator. Please follow instructions in here

We strongly recommend to use conda environment. We can install required libraries using requirements.txt

conda create -n tsc python=3.8 -y
conda activate tsc
pip install -r requirements.txt

Our method consists of three main steps: Offline Data Collection, Meta Training, and Online Adaptation.

• Offline Data Collection

  To collect offline data, you should run the following command. You can set num_worker to accelerate the data collection process via parallelization.

  python collect_data.py --network 2by2 --scheme comb
  

  We provide script file for generating offline data across all networks and schemes in scripts/collect_data.sh

• Meta Training

  After collecting dataset, you should run the following command to train our ANP model. Model will be saved in results/<network>/<scheme>/anp/<exp_id>/ckpt.tar.

  python meta_train.py --network 2by2 --scheme comb --model anp --exp_id trial1
  

  We provide script file for meta-training ANP across all networks and schemes in scripts/meta_train.sh

• Online Adaptation

  Finally, we employ Bayesian optimization with the trained ANP as a surrogate model to find optimal scheme for traffic lights with unseen traffic patterns. You should run the following command to conduct online adaptation.

  python meta_test.py --network 2by2 --scheme comb --model anp --exp_id trial1 --scenario_id 0
  

  We provide script file for online adaptation across all networks and schemes in scripts/meta_test.sh