Scenario A – ROMA model Sample Clauses

Scenario A – ROMA model. Some tables and diagrams generated by the Matlab based tool from the results of the quantitative evaluation of Scenario A are briefly described and exemplified below, for the resilience KPI (see Figure 11). 7000 6000 5000 deviation (s) 4000 Timetable ROMA Resilience: reference scenario vs delay scenario deviation 00009904, T00009904 00009901, X00000000 00009171, T00009171 00041905, T00041905 00009926B, T00009926_1 00004005, T00004005 Resilience: reference scenario vs delay scenario deviation 00009904, T00009904 00009921B, T00009921 00004005, T00004005 00041905, T00041905 00009171, T00009171 00009901, X00000000 4500 4000 3500 3000 deviation (s) 2500 3000 2000 1000 0 -1000 2000 1500 1000 500 1 2 3 4 5 6 7 - 500 1 2 3 4 5 6 7 time (hours) time (hours) Figure 11. Iron Ore line Scenario A: Comparison between the timetable and ROMA in terms of resilience RS Timetable ROMA Deviation area [s2] 5.509e+07 4.849e+07 Maximum delay [s] 6354 4497 Time to recover [s] 13840 13540 Table 2. Quantitative values calculated from the simulation. The three measures of the resilience KPI are the maximum deviation during time pe- riod T [seconds], the time to recover [seconds], and the delay area [seconds2]. The ROMA algorithm has a rescheduling interval of 2 minutes and a prediction horizon of 2 hours. For an observation period of 7 hours based on the quantitative evaluation re- sults, in general the algorithm is able to strongly reduce the deviation area with re- spect to the case in which trains follow the scheduled passage order. Figure 11, along with the above given table, clearly illustrates that a positive effect is achieved in terms of resilience since the application of the ROMA algorithm reduces the deviation area, the maximum delay as well as the time to recover the delay.
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