Research On Modeling And Algorithms For Capacity Optimization Of Multi-modal Passenger Transportation Network In Urban Agglomeration

The development of China's urban agglomerations has entered the fast lane.With the continuous optimization of population and industrial layout in urban agglomerations,the economic and industrial links among cities are gradually closer,and the spatial and temporal distribution of transportation demand are also undergoing great changes.At the same time,the incompatibility and imbalance between passenger transportation supply and demand have caused fierce competition among different modes,so the coordination of multi-modal transportation capacity is difficult to fully exert,resulting in serious resource waste and environmental pollution,which to some extent affects the spatial evolution of urban agglomerations to a more reasonable structure,and hinders the formation and development of urban agglomerations.In this case,in order to match the capacity of the transportation system with the changing travel demand,we should first consider how to use and improve the existing transportation network capacity and services to meet the.short-term travel demand of urban agglomerations,which involves the collaborative optimization of the transportation capacity of multiple modes in urban agglomerations.Therefore,this paper studies the capacity optimization of multi-modal passenger transportation network in urban agglomerations.Firstly,the concept of passenger trip in urban agglomeration is defined based on the analysis of urban agglomeration and its traffic system,which specifies the travel process includes three stages:urban travel in the city of origin,intercity travel between cities and urban travel in the city of destination.Then,the mechanism and characteristics of passenger travel demand in urban agglomerations are analyzed,and on this basis,an passenger flow distribution model based on industrial correlation degree is proposed to study the spatial distribution law of passenger transportation demand in urban agglomerations by improving the traditional gravity model.Secondly,corresponding to the urban agglomeration transp.ortation system structure and the passenger trip process,the super network model of multi-modal transportation system is constructed based on the super network theory.According to the characteristics of network structure and passenger trip,the generalized costs of different links are defined respectively.Considering the passenger travel demand will change with the continuous optimization and improvement of network travel conditions,the multi-modal stochastic equilibrium assignment model under elastic demand is established based on traffic and transportation network equilibrium analysis theory and variational inequality theory.In addition,the search algorithm of effective hyper-path and MSA algorithm are used to design the solution algorithm.The feasibility and effectiveness of model and algorithm are illustrated through a numerical example.Finally,a multi-objective bi-level programming model for capacity optimization of multi-modal passenger transportation network in urban agglomerations is established.Because of the multi-objectivity of transportation planning management,the upper model optimizes the allocation of passenger transportation capacity of various intercity transportation modes taking into account the maximum social welfare,the minimum network emissions and the minimum spatial inequality,and the lower model is a multi-modal stochastic user equilibrium assignment model under elastic demand.Meanwhile,in order to efficiently search and generate pareto optimal solution set in parallel,multi-objective bilevel programming algorithm based on GAB algorithm and O-K algorithm is applied to solve the model.A numerical example is given to illustrate and verify the effectiveness of the model and algorithm in the end.