Optimization Model And Algorithm For Collaborative Passenger Flow Control In Urban Rail Transit

With the deepening of China's urbanization,traffic demand has a sharp increase.As the advantages of punctuality,safety,environmental protection and large volume,urban rail transit has gradually become the preferred mode of travel for urban residents.However,along with the continuous expansion of the scale and the surge in passenger flow,the problem of passenger traffic congestion in rail transit operation has also emerged,which has become one of the main problems affecting the operation security of urban rail transit.Based on this,an important measure is to formulate a reasonable passenger flow control strategy for urban rail transit operation,which can alleviate passenger traffic congestion and improve the safety of operation.In response to this problem,by analyzing the spatio-temporal distribution characteristics of rail transit passenger flow,and taking collaborative passenger flow control methods as the main theme,in this thesis the theoretical researches on passenger flow control are successively carried out with the consideration of spatiotemporal networks,under random demands and integrated optimization.In addition,for generating optimal collaborative passenger flow control strategy of the rail trnsit system,this dissertation addresses the construction of optimization models and the efficient algorithms based on different evaluation rules.Specifically,the contents of this dissertation can be concluded as the following four aspects:(1)Based on passenger flow characteristics,the operation organization in urban rail transit is analyzed in this section.The passenger flow demands in urban rail transit system are extremely imbalanced in time and space,which will aggravate the passenger traffic congestion in peak period.On this matter,the organization elements of passenger flow and train operation are analyzed.Considering the current situation of rail transit operation,the collaborative passenger flow control strategy in urban rail transit is recognized as necessarily.On this basis,the influencing factors are discussed from three aspects,including characteristics of passenger flow characteristics,facilities and train capacity.This section is the foundation of the full text,providing a theoretical basis for the collaborative passenger flow control optimization in later chapters.(2)Collaborative passenger flow control optimization based on time-space networks.With the consideration of danamics of passenger demands,a timetable-oriented space-time network for the trajectory choices of all passengers is constructed with the time discretization and pre-given train timetable.Based on the flow balance constraints and train loading capacity constraints,the problem of interest is finally formulated as a constrained shortest path model for individual passengers' path finding processes,in which the objective function aims to minimize the total passenger waiting time at different stations.Then,an efficient heuristic algorithm,which integrates the Lagrangian relaxation approach is developed to solve the proposed model.By dualizing the train loading constraint into the objective function,the relaxed model can be further decomposed into a series of sub-problems which can be easily solved.Finally,a series of numerical examples are implemented on a sample metro line and the Beijing metro Batong line using historical passenger demands data.The computational results show that the generated passenger flow control strategies can reduce the traffic congestion on an oversaturated urban metro system,and provide scientific theoretical support for secure operation of the urban rail transit.(3)Robust passenger flow control optimization based on random passenger demands scenarios.Considering the dynamics and randomness of passenger flow demands,this study aims to generate a robust control strategy for peak hours.This is,taking robust control variable for different scenarios and actual control variable for each scenario as decision variables,this study constructs a stochastic optimization model with the goal of minimizing the total expected passenger waiting time over the entire metro line,in which the train loading capacity constraints and coupling constraints are formulated.To solve the proposed model,a set of Lagrangian multipliers are introduced to dualize the coupling constraints into the objective function.Then the primal model is decomposed into a series of scenario-related passenger flow control models.Finally,two sets of numerical experiments,including a small-scale case and a real-world instance with operation data of the Beijing metro Batong line,are implemented to validate the performance of the proposed approaches.The computational results show that the proposed method has a good adaptability,and can significantly improve the robustness of passenger flow organization in urban rail transit.(4)Collaborative optimization of passenger flow control and train schedule based on skip-stop pattern.Influencd by the homogeneity of passenger travel demands,the passenger flow demands on metro line are extremely imbalanced at different stations,and this unbalanced characteristic lead to low matching degree between passenger travel demands and rail transit resource supply.For these reasons,an integer optimization model is constructed to generate passenger flow control strategy,train schedule and skip-stop scheme.As the proposed model is a mixed integer nonlinear programming model,we reconstruct the model based on the linearization method,so that the model can be sloved by a branch and bound algorithm embedded in the CPLEX solver.A series of numerical experiments are designed on the background of a small-scale rail transit line and Beijing metro Batong line.The computational results show that the proposed model can obtain optimized train schedule,skip-stop scheme and passenger flow control strategy.This part of the research has certain significance for guiding urban rail transit to balance the resource allocation,improve the efficiency of train operation,and achieve the best matching between the line capacity and passenger flow demands.This thesis includes 73 figures and 31 tables,and refers to 123 literatures.