本文關(guān)鍵詞：基于均衡流量的城市公交網(wǎng)絡系統(tǒng)優(yōu)化模型及算法　出處：《北京交通大學》2014年碩士論文　論文類型：學位論文

  更多相關(guān)文章： 城市公交 客流分配 發(fā)車頻率 線網(wǎng)優(yōu)化 遺傳算法

【摘要】：伴隨著我國城市化進程的不斷加快,城市化水平的不斷提升以及城市人口的不斷增加,城市出行需求大幅度地增加。與此同時,小汽車的快速發(fā)展一方面帶來了出行的便捷性,另一方面其數(shù)量的急劇上升也大大增加了現(xiàn)有路網(wǎng)的壓力。現(xiàn)有交通路網(wǎng)的規(guī)劃與建設遠遠滿足不了經(jīng)濟發(fā)展的步伐,城市交通問題層出不窮,城市交通擁堵問題成為了城市發(fā)展的絆腳石,亦成為現(xiàn)在不得不解決的關(guān)鍵問題。城市公交系統(tǒng)具備自身運力大、方便性等特點,大力發(fā)展城市公交成為解決城市交通問題的首選。 基于此,本文圍繞城市公交系統(tǒng)的優(yōu)化設計進行了以下幾個方面的研究。 1、城市公交客流需求是城市公交系統(tǒng)建設的重要依據(jù),合理的預測、模擬城市公交客流量在公交網(wǎng)絡中的分布,有利于奠定公交系統(tǒng)優(yōu)化設計的出行者數(shù)據(jù)基礎。城市公交客流具有多樣性、多變性、復雜性等特點,合理的構(gòu)建客流分配模型直接影響著對網(wǎng)絡中的客流分布的模擬效果�；诖吮疚慕⒘丝紤]換乘因素的城市公交系統(tǒng)隨機均衡配流模型,考慮換乘費用和換乘次數(shù)對于乘客出行路徑選擇的影響,采用隨機均衡配流模型更好地模擬乘客的出行路徑選擇行為,較好地模擬了乘客在公交網(wǎng)絡中的出行分布。 2、發(fā)車頻率優(yōu)化是城市公交系統(tǒng)優(yōu)化設計最重要的工作之一,其設置的合理性不僅影響著城市公交對廣大出行者的服務效率,也直接關(guān)系著公交運營企業(yè)自身的效益。本文從乘客和公交企業(yè)雙方的利益出發(fā),建立基于均衡流量的城市公交系統(tǒng)發(fā)車頻率優(yōu)化模型,以乘客出行總費用最小、公交運營企業(yè)收益最大為上層優(yōu)化目標,以考慮換乘費用的隨機均衡配流模型為下層優(yōu)化目標,采用改進的遺傳算法進行模型求解,改進后的公交發(fā)車頻率設置更加符合乘客和公交企業(yè)的利益。 3、城市公交線網(wǎng)規(guī)劃受到多方面條件、因素的制約,本論文介紹了城市公交線網(wǎng)優(yōu)化設計原則、目標及影響因素,在此基礎上,考慮公交系統(tǒng)中線路長度的限制、公交車運行的最小客流限制、非直線限制、斷面流量限制、站點距離限制、發(fā)車頻率限制等約束條件,以乘客直達率最大、公交運營企業(yè)收益最大為上層優(yōu)化目標,建立以城市公交客流隨機均衡分配模型為下層模型的基于均衡流量的城市公交線網(wǎng)優(yōu)化設計模型,盡最大可能地顧及了乘客和運營者的雙方面利益。 4、遺傳算法在優(yōu)化問題的求解中具有明顯的優(yōu)勢,本論文在采用遺傳算法的基礎上對遺傳算法做出了相應的改進,且在對遺傳算法自身改進的基礎上,針對公交線網(wǎng)優(yōu)化這一具體問題,提出了遺傳算法與其他優(yōu)化算法(模擬退火算法)結(jié)合的新算法,有效地避免了遺傳算法在求解過程中的缺點,改進的混合遺傳算法具有更好的收斂性和求解效率
[Abstract]:Along with the accelerating urbanization process of our country , the rising urbanization level and the increasing of the urban population , the urban travel demand is greatly increased . At the same time , the rapid development of the small car brings convenience to the travel , on the other hand , the rapid rise of the quantity of the small car greatly increases the pressure of the existing road network . Based on this , the paper studies the optimization design of urban public transport system in the following aspects . 1 . The urban public transport passenger flow demand is an important basis for the construction of the urban public transport system . The reasonable forecast and simulation of the distribution of the urban public transport passenger flow in the public transport network will help to lay a foundation for the simulation of the passenger flow distribution in the public transport system . The reasonable construction of the passenger flow distribution model affects the passenger flow distribution in the network directly . Based on this paper , a stochastic equilibrium distribution model is established to simulate the passenger ' s travel route selection behavior better , and the distribution of passengers in the public transport network is simulated well . 2 . The optimization of vehicle frequency is one of the most important tasks of urban public transport system optimization design . The rationality not only affects the service efficiency of urban public transport to the large number of travelers , but also directly affects the efficiency of the public transport operators . In this paper , based on the interests of both passengers and public transport enterprises , this paper sets up an optimized model of urban public transport system based on equilibrium flow . The model is solved by using the improved genetic algorithm . The improved bus departure frequency setting is more consistent with the interests of passengers and public transport enterprises . 3 . The urban public transport network planning is restricted by many conditions and factors . This paper introduces the principle , goal and influencing factors of urban bus network optimization design . On the basis of this , considering the limitation of the length of the line in the bus system , the minimum passenger flow restriction , the non - linear limitation , the cross - section flow restriction , the station distance limitation , the departure frequency limit and so on , the urban public transport network optimization design model based on the equilibrium flow of the urban public transport passenger flow stochastic equilibrium distribution model is established . 4 . Genetic algorithm has obvious advantages in solving the optimization problem . This paper improves the genetic algorithm on the basis of genetic algorithm , and proposes a new algorithm combining genetic algorithm and other optimization algorithms ( simulated annealing algorithm ) on the basis of the improvement of genetic algorithm . The disadvantages of genetic algorithm and other optimization algorithms ( simulated annealing algorithm ) are proposed . The improved hybrid genetic algorithm has better convergence and solving efficiency .

【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：U491.17

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本文編號：1383297