A New Method for Train Timetable Stability Control

摘要

The timetable plays a most important role in rail transportation operation system. And the timetable robustness is the key attribute to evaluate the timetable quality. To study the timetable robustness further and supply decision support for producing and evaluating the timetable, it defines the timetable robustness and qualifies the robustness based on the statistic theory. The variance of the relative adjustment allowance for trains operation in 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.