###### Abstract  
Maintaining smooth traffic during disaster evacuation is a lifesaving step. Traffic resilience is often used to define the ability of a roadway during disaster evacuation to withstand and recover its functionality from disturbances in terms of traffic flow caused by a disaster. However, a high level of variances due to system complexity and inherent uncertainty associated with disaster and evacuation risks poses great challenges in predicting traffic resilience during evacuation. To fill this gap, this study aimed to propose a new integrated data-driven predictive resilience framework that enables incorporating traffic uncertainty factors in determining road traffic conditions and predicting traffic performance using machine learning approaches and various space and time (spatiotemporal) data sources. This study employed an augmented Long Short-Term Memory (LSTM)-based approach with correlated spatiotemporal traffic data to predict traffic conditions, then to map those conditions to traffic resilience levels: daily traffic, segment traffic, and overall route traffic. A case study of Hurricane Irma's evacuation traffic was used to demonstrate the effectiveness of the proposed framework. The results indicated that the proposed method could effectively predict traffic conditions and thus help to determine traffic resilience. The data also confirmed that the traffic infrastructures along the US I-75 route remained resilient despite the disturbances during the disaster evacuation activities. The findings of this study suggest that the proposed framework is applicable to other disaster management scenarios to obtain more robust decisions for the emergency response during disaster evacuation. resilience; data-driven; LSTM; traffic; transportation; disaster; management; evacuation +
Footnote †: journal: Medical Engineering  
Article An Integrated Data-Driven Predictive Resilience for Disaster Evacuation Traffic Management  
and dynamic decisions from emergency responses often result in traffic congestion during evacuation [7]. This type of traffic congestion is known as non-recurring congestion, which can be potentially life-threatening if the impact of hazards increases in scale or changes in direction [8]. Thus, it is necessary to ensure efficient traffic flows during a hectic period of disaster evacuation to ensure a successful implementation of evacuation with minimal time and disaster casualties. This also translates to the requirement for traffic systems to be resilient. A resilient traffic system can manage disaster impacts by maintaining traffic operability pre-, during, and post-disaster, and by recovering from traffic disruptions [4]. Traffic resilience is often associated with road network conditions. [PERSON] et al., 2016 developed a dynamic equilibrium-restricted assignment model to evaluate the resilience of a road network [9]. [PERSON] and [PERSON], 2018 proposed a Link Performance Index for Resilience (LPIR) with robustness and vulnerability concepts to evaluate the resilience level of road sections [10]. Many researchers have worked toward advances in traffic systems to enable resilience concepts in the existing transportation infrastructure. [PERSON] et al., 2021 incorporated the spatiotemporal property into traffic resilience dynamics in a destination choice model for evacuation applications [11]. [PERSON] et al., 2021 optimized traffic signals with a semi-automatic cross-entropy optimization method to improve resilience in a disrupted road network [12]. [PERSON] and [PERSON], 2022 proposed an adaptive traffic signal control strategy based on dynamic phase selection coupled with queue length dissipation to improve resilience in intersections against disruptions [13]. With the technological advances in collecting various traffic data, machine learning and data-driven approaches are often employed to predict or analyze traffic situations due to their ability to handle and process various traffic data [14].