A Hybrid Framework Combining LSTM NN and BNN for Short-term Traffic Flow Prediction and Uncertainty Quantification

Short-term traffic flow prediction plays a critical role in Intelligent Transportation System (ITS), and has attracted continuous attention. Previous studies have focused on improving the prediction accuracy of mean traffic flow. Due to the dynamics and propagation of traffic system, reliable traffic control and induction measures have been considered to be dependent on prediction intervals of short-term traffic flows. The current parametric models used to quantify uncertainty in traffic flow prediction cannot well capture the nonlinear patterns of traffic flow series, and may not apply to situations without long-term continuous observations. This paper proposes a hybrid framework combining long short-term memory neural network (LSTM NN) and Bayesian neural network (BNN) for real-time traffic flow prediction and uncertainty quantification based on sequence data. Caltrans Performance Measurement System (PeMS) traffic flow data for 6 freeways in Sacramento city is aggregated at 15-min intervals to evaluate the proposed model. Compared to the SARIMA-GARCH model, the proposed LSTM-BNN model outperforms in predicting both the mean and interval of the traffic flow. Especially, the experiments show that the LSTM-BNN model is superior during the daytime and under non-seasonal traffic conditions. The proposed LSTM-BNN model can be utilized in ITS for making reliable management decisions.