Network-Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: Deep-Learning Approach

Traffic prediction during hurricane evacuation is essential for optimizing the use of transportation infrastructures. Traffic prediction can reduce evacuation time by providing information in advance on future congestion. However, evacuation traffic prediction can be challenging because evacuation traffic patterns are significantly different than are those for regular period traffic. A data-driven traffic prediction model is developed in this study by utilizing traffic detector and Facebook movement data during Hurricane Ian, a rapidly intensifying hurricane. We select 766 traffic detectors from Florida’s four major interstates to collect traffic features. Additionally, we use Facebook movement data collected during Hurricane Ian’s evacuation period. The deep-learning model is first trained on regular period (May to August 2022) data to understand regular traffic patterns. Then, Hurricane Ian’s evacuation period data are used as test data. The model achieves 95% accuracy (RMSE=356) during regular period but underperforms with 55% accuracy (RMSE=1,084) during the evacuation period. Then, a transfer learning approach is adopted in which a pretrained model is used with additional evacuation-related features to predict evacuation period traffic. After transfer learning, the model achieves 89% accuracy (RMSE=514). Adding Facebook movement data further reduces the model’s RMSE value to 393 and increases accuracy to 93%. The proposed model is capable of forecasting traffic up to 6-h in advance. Evacuation traffic management officials can use the developed traffic prediction model to anticipate future traffic congestion in advance and take proactive measures to reduce delays during evacuation.