## 3 Case Study: Hurricane Irma
### Case Study Results  
After implementing the proposed framework detailed in Section 2, the hourly volume prediction \(V(t)\) for Station 1, S1(0350), is shown in Figure 6. The figure demonstrates the comparison plot of the actual data points and the predicted results for S1(0350). The post-storm hourly volume trend was predicted using the augmented LSTM framework, shown as the solid red line in Figure 6. The plot indicated that the augmented LSTM algorithm could successfully predict the post-storm recovery traffic condition with a relatively low mean absolute error of 39.312. The hourly volume data of the given study period of 4-7 September 2017 was compared with the hourly volume data of 4-7 September from the past two years, 2015 and 2016, to find the uncertainty factor value of \(\alpha\) over the study period. \(\alpha\) values for S1(350) are shown in Figure 6b. The \(\alpha\) values for S1(350) vary from \(-1\) to 15.7. Over the evacuation period, the \(\alpha\) value ranges from 0.3 on 4 September 2017, to 15.7 on 8 September 2017, and decreases in the next few days. During the hurricane period of 9-10 September, the \(\alpha\) value ranges from 4.1 to \(-0.8\). In the post-hurricane period of 11-15 September 2017, the \(\alpha\) value increased, ranging from \(-0.6\) to 0.6. The \(\alpha\) values can be either negative or positive, depending on the average change in hourly volume from the previous years' data. A positive \(\alpha\) value indicates an increasing trend in hourly traffic volume, whereas a negative \(\alpha\) value refers to decreasing hourly traffic activity compared to the previous data points. During the pre-storm evacuation period, the \(\alpha\) value increases drastically because no evacuation activity occurred during the last years. This \(\alpha\) value decreased to a negative value during the hurricane period due to a reduction in traffic activities on the same when compared to the previous years. The \(\alpha\) value recovers post-hurricane as the evacuation stops and eventually converges with minor fluctuation when approaching the normal flow. The same prediction was conducted for the other nine segments, but the graphs from the nine segments are not presented in this section due to the page limitation. As mentioned in Section 2, the traffic performance shows a cyclic trend daily. The predicted traffic performances of all the segments \(P_{TR}(t)\) were obtained using Equation (5), and the resilience curve for S1(0305) is shown in Figure 7a. The lowest traffic performances \(P_{TR}(t)\) are observed over the pre-storm evacuation period of 4-9 September 2017. The traffic performance increased close to the targeted performance \(P_{TT}(t)\), as there were hardly any vehicles during the hurricane. \(P_{TR}(t)\) again reduced as the evacuees returned to their homes post-hurricane, before the traffic flow became normal at the final stage. The change in average traffic resilience along the US I-75 route is shown in Figure 7b, which mimics the general four-stage resilience curve in Figure 3a. These results can be further interpreted to mean that within the study period of 4-15 September 2017, the traffic infrastructures along US I-75 can be deemed resilient against traffic evacuation disturbances caused by Hurricane Irma. Table 2 provides all the daily resilience values for each segment, the segment resilience, as well as the overall route resilience. For Segment 1 (S1), the daily traffic resilience on 4 September 2017 (prior to the hurricane) is found to be 0.834, and it is gradually reduced during the evacuation period, with the lowest resilience recorded being 0.738 on 8 September 2017. Another example is segment 3 (S3), which had the lowest daily traffic resilience recorded at 0.695 on 7 September 2017, two days prior to Hurricane Irma. Figure 6: Prediction results for S1 (0350). (**a**) Actual data and prediction result of hourly volume; and (**b**) Change of \(\alpha\) with time for S1(350) for S1(0350).