###### Abstract
Recently, out of various machine learning algorithms, LSTM-based algorithms have been widely proposed to predict different traffic properties during a hurricane evacuation. In their study, [PERSON] et al., 2021 utilized the LSTM neural network and traffic data from Hurricanes Irma and Matthews to accurately predict traffic demand up to 24 h before the evacuation [15]. In addition to predicting traffic demand, [PERSON] and [PERSON], 2018 applied LSTM to short-term traffic speed prediction during evacuation [16]. In their updated work, [PERSON] and [PERSON] (2022) also developed a dynamic graph convolution LSTM (DGCN-LSTM) model to analyze the network dynamics during hurricane evacuation [17]. Incorporating uncertainty in the prediction framework is important because it reflects the reality of unpredictable events during evacuations, such as natural disasters and traffic incidents. However, there are many different sources of uncertainty, such as: (1) Data uncertainty. Accurate data is essential to make predictions, but the data may be incomplete or unreliable during a disaster, leading to uncertainty in the prediction model [18]. (2) Model uncertainty. The models used for prediction and evacuation planning are often based on assumptions and simplifications that may not always hold true [18]. (3) Response uncertainty. In a disaster, people's responses to an evacuation order can be unpredictable. Some may choose to evacuate early, while others may delay or refuse to evacuate, leading to uncertainty in the number of people that need to be evacuated and the routes they may take [2]. Failing to account for uncertainty in disaster evacuation traffic management can lead to underestimating the potential risks and not allocating adequate resources to handle the situation [3]. This can result in delayed response times, inefficient evacuation routes, and inadequate allocation of resources such as emergency services, which can ultimately lead to increased casualties and damage [19]. To overcome this gap, the aim of this paper is to propose a new integrated data-driven predictive resilience framework that incorporates traffic uncertainty factors to assess traffic resilience during disaster evacuation. The research objective is to utilize machine learning approaches and spatiotemporal data sources, specifically an augmented LSTM-based approach, to predict traffic conditions and map them to traffic resilience levels, demonstrating the effectiveness of the proposed framework through a case study of Hurricane Irma's evacuation traffic. LSTM provides a short- and long-term memory component that is useful for predicting a sequential scenario [20]. Before predicting traffic performance, data uncertainty factors are introduced in measuring the current traffic condition. Incorporating uncertainty allows for the development of more robust and adaptive prediction models that can adjust to changing conditions and make more accurate predictions. Then, the augmented LSTM with the Kalman Filter estimation algorithm is employed in the proposed framework to estimate correlated spatiotemporal traffic data. Lastly, various levels of traffic resilience are quantified based on the predicted parameter value obtained from the framework. The proposed integrated data-driven predictive framework for disaster evacuation traffic management will allow the decision-makers to make more informed decisions by considering the uncertainty and potential risks associated with different courses of action. The contributions of this paper to the infrastructure preservation and resilience community include: (1) integrating uncertainty factors in road traffic performance measures; (2) applying an improved LSTM approach to predicting traffic scenarios; and (3) quantifying traffic resilience with spatiotemporal data. The information presented in this paper is structured as follows. Section 2 introduces the proposed predictive traffic resilience framework by quantifying road conditions, predicting traffic performance under uncertainty, and measuring traffic resilience.