A New Method for Train Timetable Stability Control
sections is defined to be the timetable robustness. Then a Particle Swarm Optimization algorithm is employed to improve and control the timetable robustness. A timetable including six trains and ten sections is selected as the computing example in this paper. The robustness value increases to 1.0123 from 0.3535, reaching the designed benchmark. The definition accurately depicts the essence of the timetable robustness. The optimization model is accurate and the optimization algorithm has high efficiency. It proposes a new method to optimize and control the timetable robustness.
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