Collaborative Optimization Method Of High-speed Train Operation Based On Elastic Adjustment Strategy

The leapfrog development of high-speed railway plays a crucial supporting role in the progress of society and construction of our country's national economy.But meanwhile,with support and promotion of national policies on railway construction and development,carrying capacity of the high-speed railway is facing higher requirements in the context of One Belt And One Road strategy.Under the premise of ensuring the safe operation of the train,research of collaborative optimization problem of the high-speed train group focus on improving transport efficiency plays a significant role in promoting the level of next-generation train operation control system,undoubtedly.In this paper,a collaborative optimization method for decision-making of operation control scheme for train group is proposed,taking high-speed train group as the research object,to meet the efficient operational requirements of high density and short interval.On the premise of ensuring safety,comfort,punctuality and energy conservation of train operation,the operation control strategy curve and departure interval time of train group are both optimized to improve the overall transport efficiency of the railway line.The main research contents are listed as follows.(1)The thesis lucubrates the dynamic operation process of the high-speed train group.Under the moving block system,a train dynamic model is built.Meanwhile,the paper proposes a fixed tracking interval model and an elastic tracking model based on the analysis of train tracking operation process.A multi-objective operation optimization model is established,taking train group as the research object.The above models lay the foundation and knowledge reserve for the following research.(2)Aiming at the collaborative decision making problem of train group operation control,the thesis firstly uses the proposed hybrid gravitation search algorithm to solve the train offline multi-objective optimization problem.On the basis of the optimal result,the decision-making process of operation control strategy for train group is proposed under the fixed tracking model and the elastic tracking model,respectively.Furthermore,an on-line elastic adjustment mechanism of the operation control strategy is proposed based on the evaluation model of resilience and elastic conversion principle of operating conditions under the elastic tracking model.The thesis defines the validity criterion of departure interval and analyzes the optimization problem of departure interval under two tracking models.(3)The thesis lucubrates the interaction between the operation strategy decision and departure interval optimization and establishes an elastic integrated optimization problem of the train group operation control scheme under the elastic tracking model.Seeker Optimization Algorithm(SOA)and Artificial Fish Swarm Algorithm(AFSA)are adopted to solve the problem and the elastic integrated optimization algorithm based on SOA and AFSA are proposed.The performance of the two algorithms is compared by simulation.The results show that the integrated optimization algorithm based on SOA can solve the decision-making problem of train group operation control more effectively.Simulation examples are constructed based on the actual data of the "Chibi North-Changsha South" section of Wuhan-Guangzhou High-speed railway to verify the proposed models and algorithms.The performance of the train tracking process under the elastic and fixed models is compared and analyzed.The results indicate that the operating efficiency of two trains under the elastic model is improved by 48s,and energy consumption is optimized by 5.96%compared with the actual data.The results show that the elastic model can effectively improve the overall operating efficiency.And the security and impaired resilience of tracking operation process under the elastic model is also verified.The elastic integrated optimization algorithm based on SOA improves the overall efficiency of 144s and reduces the energy consumption by 4.90%for the decision-making problem of train group(the scenario of five trains tracking operation in the section)operation control scheme,which demonstrates the validity of the algorithm.This thesis contains 38 figures,19 tables and 73 references.