Application Of Improved Particle Swarm Optimization Algorithm In Urban Rail Transit Train Adjustment

With the rapid development of China's economy,the process of urbanization is accelerating,and the urban population has increased rapidly.This has led to a series of problems such as urban traffic congestion and environmental pollution.Such problems have become the bottleneck restricting urban economic development.Urban rail transit has the characteristics of large transport volume,fast operation speed,safety and comfort,etc.It has become an effective way to solve urban traffic congestion,but the late train phenomenon is difficult to avoid.In order to solve the problem of late train,it is utmost importance to adopt scientific and effective methods of operation adjustment.For the problem of late trains in urban rail transit,the use of appropriate algorithms to adjust train operations to maximize the capacity of urban transport networks has become the focus of current urban rail transit operations.First of all,this paper introduces the system composition of urban rail transit ATS,and focuses on the ATS system's train operation adjustment function.The introduction of late trains in the operation of urban rail transit was introduced,including the late classification and the late train hat curve as well as the method of train operation adjustment and the process of train adjustment.Secondly,considering the particle swarm optimization algorithm has the disadvantage of local optimization,the crossover and mutation process of the genetic algorithm is introduced into the particle swarm algorithm to improve it,and the implementation flow of the improved particle swarm algorithm is elaborated in detail.At the same time,the inertial weights and learning factors in the improved GA-PSO algorithm are self-adaptively improved to achieve fine-tuning optimization and balance local search and global exploration capabilities,so as to make the improved GA-PSO algorithm converge at a fast speed.Stability has been improved.In order to establish an operation adjustment model that conforms to the characteristics of urban rail transit train operation,the adjustment system regards the reduction of the total train delay time as an optimization goal,and takes the train interval running time,train tracking interval time,station residence time,and station operation time as constraints.The adjustment system adopts an improved particle swarm optimization algorithm,GA-PSO algorithm combined with genetic algorithm and particle swarm optimization algorithm.The algorithm's coding,initial population,fitness function and other operations are designed in detail,and the technical process of the algorithm is given.Finally,in order to verify the validity and feasibility of the algorithm,by querying the operational data of Xi'an Metro Line 2 and using the operating schedule data of the six lines as the basic data of the system,the adjustment system was developed with object-oriented programming language VC++.The simulation system was adjusted to simulate the two conditions of late train and train late.The simulation results show that the train operation adjustment scheme based on the improved particle swarm algorithm proposed in this paper is highly effective.The simulation system results verify that the algorithm can reduce the total time of the train delay,reduce the deviation of the train's actual operation chart and plan operation chart,and verify the algorithm has feasibility and good practicability in the train operation adjustment model.