Short-Term Traffic Speed Prediction for Freeways During Hurricane Evacuation: A Deep Learning Approach

Abstract

Hurricane evacuation plays a critical role for effective disaster preparations. Giving accurate traffic prediction to evacuees enables a safe and smooth evacuation. Moreover, reliable traffic state prediction allows emergency managers to proactively respond to changes in traffic conditions. In this paper, we present a deep learning model to predict traffic speeds in freeways under extreme traffic demand, such as a hurricane evacuation. For prediction, we adopt a Long Short-Term Memory Neural Network (LSTM-NN) model. The approach is tested using real-world traffic data collected during hurricane Irma's evacuation for the interstate 75 (I-75), a major evacuation route in Florida. Using LSTM-NN, we perform several experiments for predicting speeds for 5 min, 10 min, and 15 min ahead of current time. The results are compared against other traditional prediction models such as KNN, ANN, ARIMA. We find that LSTM-NN performs better than these parametric and non-parametric models. The proposed method can be integrated with evacuation traffic management systems for a better evacuation operation.