The repository gives case studies on short-term traffic flow forecasting strategies within the scope of my master thesis. After implementing the traditional (AR, ARMA, ARIMA and SARIMA), machine learning (SXGBoost and SSVR) and deep learning methods (SLSTM), one of main goals is to experiment on the uses of hybrid methods (SSVRARIMA, SSLSTMARIMA and SXGBoostARIMA). Besides analyzing approaches that were already used in the traffic flow literature, distinct strategies are also introduced and tested. Further, the point forecast results are supplemented with interval forecasts. In particular, quantile regression based intervals such as quantile regression averaging (QRA), quantile regression neural network (QRNN) and quantile regression long short-term memory (QRLSTM) are implemented. Both point and interval forecasts are evaluated via several evaluation metrics, and an extensive comparison is provided among the methodologies studied.
You can reach my master thesis by this link.