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Characteristic Analysis And Optimal Research On Urban Combined Public Transportation Network

Posted on:2021-03-06Degree:MasterType:Thesis
Country:ChinaCandidate:X Y YangFull Text:PDF
GTID:2392330605960922Subject:Transportation planning and management
Abstract/Summary:PDF Full Text Request
In recent years,with the rapid development of China's urbanization,socio-economic growth has also been very rapid,and the scale of small and medium-sized cities is also constantly expanding.At the same time,the travel demand of urban residents is also gradually increasing.The urban transportation problems in major small and medium-sized cities are becoming more and more serious.Solving citizens' travel and alleviating urban transportation pressure have always been hot issues in urban public transportation.Priority in the development of public transportation is one of the effective means to solve this problem.Due to the obvious advantages of rail transit,more and more cities in China have begun to attach importance to the development of rail transit.For cities with relatively backward economies,the development of rail transit started late,and the degree of public transportation network is low.Therefore,in the early days of rail transit operation,we should combine the actual operate situation of rail transit and conventional bus,and the integrated operation of rail transit and conventional bus should be the goal.Make the city's public transportation network better alleviate urban transportation problems and provide more convenient travel services for urban residents.Through combing the relevant domestic and foreign literatures on the optimization of urban combined public transportation network,analyzing the topological characteristics of the combined public transportation network before and after the opening of Lanzhou Rail Transit Line 1 based on the complex network theory.On this basis,a multi-objective optimization model was constructed,and the genetic algorithm was used to solve the model.This article is mainly composed of the following four parts:The first part is an overview of basic theory.This part sorted out the basic theory of complex networks,analyzed the characteristics and interrelationships of urban rail transit and conventional public transportation,and the influencing factors of urban combined public transportation network.These laid the theoretical foundation for the subsequent optimization work.The second part is the analysis of Lanzhou combined public transportation network construction and its characteristics.This part used the Space L modeling method in the complex network theory to construct the Lanzhou combined public transportation network,and used Pajek software to calculate and analyze various topological characteristics indicators.On this basis,preliminary traffic adjustments were made to the combined public transportation network in the study area.The third part is the optimization model and solution of Lanzhou combined public transportation network.By analyzing the influencing factors of the combined public transportation network optimization and determining the optimization goals(minimum passengers' travel time,minimum bus operation costs,and maximum passengers' number),the multi-objective optimization model was constructed.Through the dimensionless processing of the objective function,the multi-objective was changed into single-objective optimization,and the genetic algorithm was used to solve it,and the newly added feeder bus route was obtained.The fourth part is the improvement of Lanzhou combined public transportation network.This part based on the complex network theory and the newly added feeder bus route obtained by the optimization model.the topological characteristic indexes of the combined public transportation network after optimization were recalculated,and the feasibility of the optimization model was analyzed.It also shown that the research work in this paper has certain theoretical and practical significance.
Keywords/Search Tags:Urban combined public transportation network, Complex network, Network optimization, Optimization model, Genetic algorithm
PDF Full Text Request
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