Demand-driven Operation Strategy Design And Optimization For Urban Railway Service

In this thesis,we tackle the operational problem for urban rail transit under different passenger demands.Operational strategies for urban rail systems during peak and off-peak periods are put forward respectively.We propose passenger flow control and express metro services to deal with the oversaturation during peak-hours and train operation re-schedule in off-peak periods,respectively.Mathematical models and algorithms are developed to solve the optimization problem.From the passenger flow aspect,during the peak hours,the large number of passengers accumulated at the platform,where train services cannot meet the travel demand,poses safety risks to the metro operations.Here,we study the congestion of metro system and develop a hybrid strategy integrating passenger flow control and bus-bridging service.A two-stage mathematical model is proposed: Stage 1 determines the stations and periods for taking passenger flow control strategy,and Stage 2 identifies the optimal bus-bridging services.We apply the proposed passenger flow control and bus-bridging strategy to a commuting metro line in Shanghai.The results show that the proposed strategy is effective in reducing the number of stranded passengers,releasing the overcrowding pressure and improving passenger satisfaction.From the train operation aspect,we proposed an express metro service,in which trains skip certain stations with a designed pattern.It can improve the speed of the train to achieve the goal of minimizing passenger travel time.We propose an exact Mixed Integer Programming(MIP)model to optimize cyclic station-skip patterns for express trains operating in a single-track subway system.The objective is to reduce passengers' total travel time(i.e.,the sum of waiting time and riding time),while considering demand intensity and distribution,train headway,frequency,and capacity.We implement the model in a set of numerical experiments using real data from Singapore.We observe that the model provides optimal solutions quickly,and the average travel time for passengers could be significantly reduced compared to local train service.