基于多级网络的多模式交通配流研究

为解决多模式复合网络的交通配流问题,将复合网络转化为多级网络形式,并提出多级网络客流分配模型、约束条件,以及其求解算法.将多模式复合网络转化为多级网络结构.以三级网络结构为例,网络第一级为带有组合出行的方式选择网络;网络第二级为基于出行逻辑的平面拓展网络;网络第三级为小汽车出行的道路网络.分析多级网络中的路径阻抗,包括线段阻抗与换乘点阻抗,提出以N-L模型解决多级网络流量的分配问题.根据复合网络构建的实际情况,在模型中加入有效路径筛选、有效换乘筛选与典型组合方式筛选等约束条件,以提高模型求解效率.采用连续平均法对多级网络配流模型进行求解.以三模式叠加的复合网络配流为算例,模型迭代7次即达到收敛条件.结果显示,在算例网络中,轨道换乘出行方式具有一定的竞争力,约占总出行量的40%.通过SP调查验证了该结果的可靠性.该方法在考虑了路径选择、换乘点选择、组合交通方式选择的情况下,将客流分配到多模式复合网络中去,弥补了传统四阶段法在多模式交通配流应用中的不足.

A Study of Multimodal Traffic Assignment based on a Multi-level Network

To solve problems of multimodal traffic assignment on a composite traffic network,the network is trans-formed into a multi-level structure,and a model of traffic assignment together with its constraints and solving algorithms are proposed.Firstly,the multimodal composite network is explained by a multi-level network structure.A three-level network is taken as a case study,the first level is for mode choices in consideration of combined travels;the second level is a state-augmented multi-modal network based on travel logic;the third level is a road network for cars.Secondly,path impedance of a multi-level network is analyzed including link impedance and transfer impedance.An N-L model for traffic assignment on this network is developed.According to the actual structure of the composite network,the efficiency of solving this model is improved through adding constraints of effective paths,effective transfers,and typical combined travels.Thirdly,the Method of Successive Averages(MSA)is used to solve the problems of the three-level network.Fi-nally,a numerical example is proposed based on traffic assignment on a composite network with three types of traveling modes.The model iterates seven times to meet the convergence conditions.The results show that in the case study,rail transfer has certain competitiveness,accounting for 40% of the total travels.The reliability of results is verified through SP investigation.This method takes path choice,transfer node choice,and composite travel choice into consideration and therefore overcomes drawbacks of the traditional four-stage method in multimodal traffic assignment.