Our project aims to conduct traffic congestion prediction using a Spatial-Temporal Graph Convolutional Network (STGCN) trained on Singapore traffic speed dataset by Land Transport Authority (LTA). Using the STGCN, we further conducted various analyses on its effectiveness across time and across geographical regions (different roads).
data/processed: Contains the traffic dataset used in this project
interactive-app: Code for deploying the model on the web-app
saved-models: Contains the weights for the models
Analysis.ipynb: Notebook to generate the analysis used in the Results and Analysis section of the report
Arima.ipynb: Notebook to train the alternative model used in the Comparison with Other Models section of the report
Data Visualisation.ipynb: Notebook to generate the visualisation images used in the Data Visualisations section of the report
Hyperparameter Tuning.ipynb: Notebook used to test the different hyperparameter settings for the model. The results were used and elaborated further in the Hyperparameter Tuning section of the report
STGCN Timestep Comparison.ipynb: Notebook used to test the effects of using different input and output timesteps for the model, as described in the Impact of Input and Output Timesteps section of the report
STGCN Traffic.ipynb: Main Notebook used to train the STGCN model
analysis.py: Supporting code that was used in the Analysis.ipynb notebook
model.py: Code for our STGCN model
model_utils.py: Supporting code that is used to train the model
preprocessing_utils.py: Code for preprocessing the traffic data
visualisation.py: Supporting code that was used in the Data Visualisation.ipynb notebook