Research On Train Operation Adjustment Of Urban Rail Oriented To Interoperability

With the rapid development of urban rail transit,interconnection operation has become one of the directions of high-quality development of urban rail transit in China,and interconnection technology has made a key breakthrough in China.Interconnection operation can optimize the allocation of line resources,make full use of the rich transport capacity of lines,and meet the fast and direct travel needs of passengers.However,the train will be delayed due to some factors in the actual operation,so it is necessary to adjust the train operation in time to avoid large-scale train delay.The existing research on train operation adjustment pays more attention to single-line independent operation,solving algorithm and so on,but pays less attention to interconnection operation.Therefore,in view of some challenges in the complex interconnection operation,this paper mainly analyzes the problems and establishes the model from the point of view of train operation and passenger travel.The research content of this paper mainly includes the following three aspects:(1)Aiming at the small-scale delay in interconnection operation,the connection relationship of train cross-line operation and the change of delay cross-line train connection relation are analyzed.This paper focuses on the construction of a train operation adjustment model aiming at minimizing the total train delay time from the aspect of operation organization.Based on the characteristics of the model,the coyote growth operation in the coyote optimization algorithm is improved,and a model solving method based on the improved coyote optimization algorithm is designed.(2)Aiming at the random dynamic passenger flow at flat peak,considering the influence of passenger flow on train operation,a train operation adjustment model aiming at minimizing the total passenger travel time is established,and the passenger travel time is reduced by adjusting the interval.The improved coyote optimization algorithm is used to solve the model for flat peak random dynamic passenger flow.(3)Aiming at the random dynamic passenger flow at peak time,the influence of dynamic passenger flow on train operation is analyzed,and a two-stage optimization model is proposed.In the first stage,the total passenger waiting time is minimized by optimizing the interval between intersections,and in the second stage,the total passenger travel time is minimized by adjusting the interval between adjacent trains.The improved coyote optimization algorithm is used to solve the two-stage model.The simulation case is designed and analyzed based on the subway line and passenger flow data of a city.The improved coyote optimization algorithm is compared with other methods,and the ideal adjustment result and the change critical value of convergence relation are obtained for small-scale delay simulation,and a simulation case is designed for the random dynamic passenger flow at flat peak.The simulation results show that the total travel time of passengers can be reduced by 3.11% by adjusting the interval.The simulation case of random dynamic passenger flow design during peak hours is verified,and the results show that the model can reduce the total passenger waiting time by 32.5%and the total passenger travel time by 3.93% during the study period.The simulation results verify the effectiveness of the model and the rapidity of the method.Figure 29,Table 10,Reference 83.