Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying
  Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning
  Approach
Md Mobasshir Rashid Rezaur Rahman Samiul Hasan
Traffic prediction during hurricane evacuation is essential for optimizing
the use of transportation infrastructures. It can reduce evacuation time by
providing information on future congestion in advance. However, evacuation
traffic prediction can be challenging as evacuation traffic patterns is
significantly different than 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 4 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-August 2022) data to understand regular traffic patterns
and then Hurricane Ian's evacuation period data is used as test data. The model
achieves 95% accuracy (RMSE = 356) during regular period, but it underperforms
with 55% accuracy (RMSE = 1084) during the evacuation period. Then, a transfer
learning approach is adopted where 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 model's RMSE value to 393 and increases
accuracy to 93%. The proposed model is capable to forecast traffic up to
6-hours 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.