Study On Energy-efficient Timetable In Urban Rail Transit Under Time-dependent Demand

In recent years,the urban rail transit operating mileage is on the increase with the network scale growing rapidly.As a result,the intensity of the passenger flow has a steady growth and the passenger volume increases continuously,which make the urban rail transit system face a more complex situation and put forward higher requirements on the operation and management of the urban rail transit system.The timetable,as the foundation and program of train operation of urban rail transit,can effectively reduce the energy consumption and the operating cost by controlling and adjusting the train operation strategy under the high quality travel services.Consequently,the research on energy-efficient timetable are of great significance to the future development of urban rail transit.In this paper,the AFC data is discretized to make time more refined,which can depict the passenger travel process more accordant with the actual.The model based on these data describes the passengers’ up and down process more reasonably.The energy-efficient optimization model of timetable is established.Paying attention to the energy consumption and the demand for passenger service quality,the model aims to minimize the energy consumption and the total waiting time of passengers.Constraints are in relation to the energy saving control strategy and the train capacity.Use genetic algorithm to solve the problem.Based on the energy-efficient optimization model of timetable,we take this situation into account.The situation is the frequent occurrence of the disturbances in the operation of urban rail transit often makes the train deviate from the planning timetable.Focus on the problem of train delays that is absolutely a research hotspot of urban rail transit.Particularly,today,the continuous expansion of the line network and increasing passenger flow make the random disturbances more serious.The robust timetable can effectively avoid the train delay and solve the problems caused by the disturbances.In this paper,a two-stage programming model that solves the robust timetable is established.The first stage takes the total waiting time of passengers as the optimization target to optimize the train departure intervals,which combines with the robust timetable of each train obtained from the second stage on the basis of the energy-efficient optimization model.The inner and outer nesting genetic algorithm is applied to solve the two-stage programming problem.Finally,taking Beijing Yizhuang Metro Line as an example,the models and solution methods that arc proposed in the above chapter 3 and chapter 4 are verified.Comparing actual timetable with optimized timetable by the energy-efficient optimization model not only reduces the total waiting time of passengers,but can save energy consumption 9.67%.As for the optimization model of robust timetable,robustness evaluation index shows the ability to resist random disturbances has been improved.The validity of the models show certain reference value of optimizing and adjusting timetable.