| Train tracking interval is significant index that restrict high-speed railway passage capacity,and the headway of large stations is the bottleneck of it.Besides the unchanging factors such as station facilities and equipment,the headway of stations is also affected by the train speed profile.In order to integrate the macro arrive-departure time and micro train speed profile,a discrete framework under different spatial and temporal resolutions is studied.On this basis,we develop a mixed integer linear program model to analyze high speed railway with quasimoving blocking mechanism by optimizing the speed profile of adjacent trains,which also takes the railway technical condition and the block sections into account.Considering the high time complexity of the model,a rolling horizon algorithm(RHA)and a step adjustment algorithm(RHA-SA)is designed.Based on the discretization characteristics of train speed profile optimization model,A large scale original problem is turned into several smaller discrete subproblems,which could reduce computing scale greatly.The test results show that the quality of the algorithm solution is the same as that of the model solution obtained by a commercial solver,e.g.CPLEX,and the efficiency and precision of the algorithm solution are greatly improved,which makes it possible to solve high-precision and large-scale examples.Numerical experiments are conducted using data of Shanghai-Kunshan high-speed railway,to analyze how does signaling time of successive train,railway technical condition impact the headway.One impressive instance shows that the optimized train trajectory could help to achieve a 135 s headway on the test railway line,and cost only 3% of extra train travel time.The results of the example are better than those of previous studies,which provides theoretical support for analyzing the influencing factors of tracking interval and studying the compression measures based on optimal train speed profile. |
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