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

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.

A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation

Proactive evacuation traffic management largely depends on real-time monitoring and prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic prediction is challenging due to the uncertainties caused by sudden changes in projected hurricane paths and consequently household evacuation behavior. Moreover, modeling spatiotemporal traffic flow patterns requires extensive data over a longer time period, whereas evacuations typically last for 2 to 5 days. In this paper, we present a novel data-driven approach for predicting evacuation traffic at a network scale. We develop a dynamic graph convolution LSTM (DGCN-LSTM) model to learn the network dynamics of hurricane evacuation. We first train the model for non-evacuation period traffic data showing that the model outperforms existing deep learning models for predicting non-evacuation period traffic with an RMSE value of 226.84. However, when we apply the model for evacuation period, the RMSE value increased to 1440.99. We overcome this issue by adopting a transfer learning approach with additional features related to evacuation traffic demand such as distance from the evacuation zone, time to landfall, and other zonal level features to control the transfer of information (network dynamics) from non-evacuation periods to evacuation periods. The final transfer learned DGCN-LSTM model performs well to predict evacuation traffic flow (RMSE=399.69). The implemented model can be applied to predict evacuation traffic over a longer forecasting horizon (6 hour). It will assist transportation agencies to activate appropriate traffic management strategies to reduce delays for evacuating traffic.

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network

We present a novel data-driven approach of learning traffic flow patterns of a transportation network given that many instances of origin to destination (OD) travel demand and link flows of the network are available. Instead of estimating traffic flow patterns assuming certain user behavior (e.g., user equilibrium or system optimal), here we explore the idea of learning those flow patterns directly from the data. To implement this idea, we have formulated the traffic-assignment problem as a data-driven learning problem and developed a neural network-based framework known as Graph Convolutional Neural Network (GCNN) to solve it. The proposed framework represents the transportation network and OD demand in an efficient way and utilizes the diffusion process of multiple OD demands from nodes to links. We validate the solutions of the model against analytical solutions generated from running static user equilibrium-based traffic assignments over Sioux Falls and East Massachusetts networks. The validation result shows that the implemented GCNN model can learn the flow patterns very well with less than 2% mean absolute difference between the actual and estimated link flows for both networks under varying congested conditions. When the training of the model is complete, it can instantly determine the traffic flows of a large-scale network. Hence this approach can overcome the challenges of deploying traffic assignment models over large-scale networks and open new directions of research in data-driven network modeling.