A Real-Time Train Timetable Rescheduling Method Based on Deep Learning for Metro Systems Energy Optimization under Random Disturbances

Table 9

Performances with three strategies in the five-train metro system.

Strategy	Calculation time (s)	Net traction energy consumption (kWh)	Energy-saving percentage compared with no-action strategy (%)
No action	0	741.85	—
General GA	17941.36	696.22	6.15
MGA	21074.15	677.09	8.73
MGA-GRU	0.33	688.51	7.19