Urban Rail Timetable Optimization And Train Regulation Based On Dynamic Passenger Flow

With the rapid development of cities,urban rail transit has become an important means for people to travel.The efficiency of metro operation and passenger travel experience have been challenged by the expansion of the scale of urban rail transit system.In urban rail transit system,train time schedule is the basis of train operation,so the optimization of train time schedule and the regulation of train operation are the key to improve the service level of urban rail transit train.However,due to the large scale of urban rail transit lines and trains,as well as the randomness and dynamic changes of passenger flow,especially in the peak period,the design and optimization of train timetables that can better meet passenger needs are still the focus and difficulty of current research.And with train delays causing by random events,the research and achievements of centralized train operation regulation are not optimal about the largescale train operation regulation.Therefore,in order to further improve the efficiency of train operation and passenger satisfaction in the case of large-scale metro network and dynamic passenger flow,the following work has been done in this paper:(1)Firstly,in order to improve the waiting experience of passengers,this paper proposes a timetable optimization model based on integer programming.The proposed timetable optimization model takes into account the demand of large passenger flow in peak period(that is,passenger flow exceeds the maximum passenger volume of train)and the general demand of passenger flow in non-peak period.Furthermore,this paper takes minimizing the total waiting time of passengers as the optimization objective,and uses genetic algorithm to solve the optimization objective.Based on the historical passenger flow data of Yizhuang line of Beijing Metro,this paper also designs a simulation comparison experiment to verify the validity of the proposed timetable optimization model.Compared with the current timetable,the total waiting time of passengers is shortened by 56.89% using the timetable optimization model in this paper.(2)Secondly,in order to improve the operation efficiency of metro trains in the case of delays and to restore the normal operation status of trains as soon as possible,this paper proposes a distributed train operation adjustment model based on multi-mode transformation and a distributed model predictive control(DMPC)algorithm for train operation adjustment.The distributed train operation adjustment model based on multimode transformation not only considers the dynamic change of passenger flow in different time periods,but also takes the timeliness and safety reliability of trains as the comprehensive optimization objective.In this paper,the DMPC algorithm is used to solve the multi-modal and multi-objective train operation adjustment problem.Based on the historical passenger flow data and operation data of Yizhuang Line of Beijing Metro,this paper also designs a simulation comparison experiment to verify the operation adjustment effect of the proposed algorithm.Compared with centralized model predictive control(CMPC)algorithm,DMPC algorithm not only has a positive effect on train operation adjustment,but also has fast calculation speed and high efficiency,which is more suitable for large-scale urban rail transit system.