Collaborative Optimization Strategy Of Urban Rail Transit Short-Turning Plan And Train Scheduling Under Uncertain Environment

The continuous expansion of the urban scale and the growing demand for passenger traffic on the urban rail system has led to heavy passenger congestion during peak hours becoming a regular phenomenon.Short-turning trains can better adapt to the uneven spatial distribution of passenger flow.Dynamic headway can meet the diverse needs of passengers.The rational formulation of full-length and short-turning plans and train schedules can improve the matching degree of transport capacity resources with passenger demand,meet the needs of passengers,and improve transport efficiency.In addition,there is usually uncertainty about passenger demand in actual urban rail operations.The train schedule based on static passenger flow may have poor anti-interference.Therefore,this paper investigates the problem of collaborative optimization of train routing planning and timetable compilation for urban rail transit,taking into account uncertain passenger demand,as follows:(1)The concepts related to large passenger flow in urban rail transits and its countermeasures are explained,and the optimal strategies for using full-length and short-turning plans and passenger flow control under the phenomenon of large passenger flow during peak periods are clarified.The principles associated with the preparation of train timetables are introduced,on the basis of which train circulation plans are introduced to improve the utilization of train resources.In addition,the uncertain optimization theory is summarized to provide the theoretical basis for solving the uncertain optimization models.(2)A stochastic collaborative optimization method for urban rail transit train routing plan and timetable is proposed.Considering the passenger arrival rate subject to a normal distribution,combined with the passenger flow control strategy,based on train variations and passenger flow variation constraints,a stochastic collaborative optimization model of urban rail train routing plan and one-way timetable is constructed to alleviate the platform congestion and reduce operating costs.To cope with the uncertainty in the model,a scenario-based chance-constrained optimization approach is used,and numerical experiments are designed to validate the model and algorithm based on the Beijing Metro Batong Line.The experimental results show that the proposed stochastic collaborative optimization strategy effectively balances platform congestion and enterprise operation cost.Further,a sensitivity analysis of the weight factor of the objective function shows that passenger costs are affected by each weighting factor,while the optimization target values are more sensitive to the weighting factors of the enterprise operating costs.(3)A robust collaborative optimization method for urban rail transit train routing plan and timetable is proposed.Considering the fluctuating passenger arrival rate within the interval range,combined with the train circulation plan,a stochastic collaborative optimization model of urban rail train routing plan and two-way timetable is constructed based on the train variations and passenger flow variations constraints to reduce the number of stranded passengers while improving train utilization.The robust counterpart transformation method is used to transform the optimization model into a deterministic model,and numerical experiments are designed to validate the model and its robustness based on the Beijing Metro Batong Line.The results indicate that the proposed robust collaborative optimization strategy can better balance the number of stranded passengers and train utilization while also providing good anti-disturbance properties.This paper contains 36 figures,15 tables and 80 references.