## 4 Further Discussion and Future Work  
The framework implemented in this paper is a novel approach to determining traffic resilience. The proposed integrated predictive resilience framework defines the traffic resilience of route US I-75 based on the predicted traffic volume during Hurricane Irma. As traffic data demonstrates a cyclic trend, implementing a conventional resilience quantification approach might not be sufficient to evaluate the traffic resilience of a roadway. To overcome this challenge, the proposed framework quantifies the daily and associated segments' resilience to compute the overall route resilience. Moreover, the proposed framework adopts a modified _V/C_ to define roadway performance by introducing uncertainty factors. The modified _V/C_ can capture the uncertainty in traffic conditions due to extreme weather that are often not considered in the regular _V/C_ metric in Equation (1). Note that the resilience value obtained may vary with several factors, such as the scope of the study period, study area, resilience performance indicator, and prediction algorithm. The resilience concept fundamentally depends on time [35]. If the study period is shortened or lengthened, this may affect the resilience values obtained for the traffic segments and routes. The case study presented only includes ten stations that make up a portion of US I-75. These ten segments cover the area under mandatory or voluntary evacuation orders, but do not represent the entire US I-75. Due to the limited data obtained, other traffic routes were not considered in the case study. Suppose the study area was expanded to include more traffic routes, for example, interstates US I-4 or US I-95 and other local highways; in that case, the resilience values for the routes would be expected to change. Additionally, segment resilience would need to be recalculated for the segments associated with that route. The presented segment resilience in this case study is only valid for the US I-75 route under evacuation orders. As mentioned in ref. [31], various performance indicators can be used as a resilience index in the transportation system. This paper focuses on a roadway's loss of service, which can be quantified with a volume-to-capacity (_V/C_) ratio. The resilience value will change if another performance indicator is implemented, such as travel time or demand. Moreover, since this paper focuses on the predicted resilience value, the data-driven algorithm used to perform the prediction may alter the resilience value. The inherent nature of the prediction algorithm may not always result in the same prediction value based on the input data [36]. In traffic applications, the fundamental LSTM algorithm can be enhanced in conjunction with other methods, such as optimization-based decision-making, and may offer several advantages [37; 38]. These combinations may improve efficiency by finding optimal solutions and optimizing decision-making processes. Data processing for training the algorithm may also affect the predicted value, and this will be studied further in the future. In the future, this study will also incorporate references to studies conducted in different countries or continents, such as Australia [37] or Africa [8], to ensure that the significance of this research can be further emphasized. This approach will broaden the scope of this study, showcasing its relevance and applicability beyond specific regions or events. It will demonstrate the transferability of findings and highlight the global relevance of addressing traffic resilience in disaster management. Additionally, referencing studies from different contexts enhances the overall knowledge base and provides a comprehensive understanding of the subject matter, contributing to advancing research in this field. Although the proposed integrated data-driven framework approach can predict various levels of traffic resilience, one drawback is that the prediction of traffic volumes does not consider future weather data.