本文關(guān)鍵詞： 客流預(yù)測 灰色模型 發(fā)車間隔 調(diào)度優(yōu)化　出處：《鄭州大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：近幾年,由于城市機動車保有量的增長速度與道路建設(shè)速度的嚴重不匹配,交通擁堵、環(huán)境污染已成為制約城市健康有序發(fā)展的重要問題。加快公共交通系統(tǒng)建設(shè)和發(fā)展速度,是提高道路資源利用效率、緩解城市交通擁堵壓力的有效辦法之一。公交調(diào)度是提高公交企業(yè)運營效率和管理水平的關(guān)鍵所在,科學(xué)的公交調(diào)度可以提高公共交通資源利用效率,提升公交服務(wù)品質(zhì),提高公共交通對出行市民的吸引力。公交客流是進行公交調(diào)度優(yōu)化的數(shù)據(jù)基礎(chǔ)和前提條件。本文結(jié)合客流在時間和空間上的分布特性,在深入分析大量客流數(shù)據(jù)的基礎(chǔ)上,研究客流的周期性變化規(guī)律,并以公交客流在時間上的分布為有序樣本,采用Fisher有序聚類算法,進行客流時段劃分,然后在時段劃分的基礎(chǔ)上,結(jié)合灰色預(yù)測理論,建立分時段的公交客流預(yù)測模型,對客流在一個運營日的分布情況進行預(yù)測,為公交調(diào)度優(yōu)化提供數(shù)據(jù)支持。公交車輛發(fā)車時間間隔是否合適是決定調(diào)度工作是否科學(xué)合理的關(guān)鍵。本文在客流預(yù)測的基礎(chǔ)上建立了以公交企業(yè)運營成本與乘客等待成本加權(quán)和最小為目標函數(shù),以發(fā)車間隔、車輛滿載率、企業(yè)經(jīng)濟效益為約束條件,以發(fā)車時間間隔為決策變量的公交調(diào)度優(yōu)化模型。該模型在客流數(shù)據(jù)的基礎(chǔ)上,計算一個公交運營日各時段的發(fā)車時間間隔。公交調(diào)度優(yōu)化模型是一個有約束條件的、多變量的、非線性模型,本文引入粒子群算法求解模型,并給出了求解過程中引入罰函數(shù)處理約束條件的具體辦法。本文將公交客流預(yù)測模型應(yīng)用于鄭州公交45路,并在預(yù)測數(shù)據(jù)的基礎(chǔ)上進行優(yōu)化調(diào)度,得出了側(cè)重不同利益主體的調(diào)度方案。結(jié)果表明,對不同利益主體的側(cè)重會對發(fā)車間隔、運營日總社會成本、乘客等待時間帶來不同的影響,該優(yōu)化調(diào)度模型可為公交實際運營中調(diào)度人員制定發(fā)車時刻表、調(diào)整行車計劃提供參考方案。
[Abstract]:In recent years, due to the serious mismatch between the growth rate of urban motor vehicle ownership and the speed of road construction, traffic congestion and environmental pollution have become an important problem restricting the healthy and orderly development of the city. It is one of the effective methods to improve the utilization efficiency of road resources and relieve the pressure of urban traffic jams. Public transport dispatch is the key to improve the operation efficiency and management level of public transport enterprises. Scientific public transport scheduling can improve the efficiency of public transport resources, improve the quality of public transport services, Public transport is the data base and prerequisite for bus scheduling optimization. This paper combines the distribution characteristics of passenger flow in time and space, based on in-depth analysis of a large number of passenger flow data. This paper studies the regularity of periodic change of passenger flow, and takes the distribution of bus passenger flow in time as an ordered sample, adopts Fisher orderly clustering algorithm to divide the passenger flow period, and then combines the grey forecasting theory with the time division of passenger flow. The forecast model of bus passenger flow is established, and the distribution of passenger flow on a operation day is forecasted. It is the key to decide whether the dispatching work is scientific and reasonable or not. Based on the forecast of passenger flow, this paper establishes the operation cost and ride of the public transport enterprise based on the forecast of passenger flow. The guest waits for the cost weighted sum to be minimized as the objective function, Based on the passenger flow data, the bus dispatch optimization model is based on the train departure interval, the vehicle full load rate, the economic benefit of the enterprise, and the departure time interval as the decision variable. The bus scheduling optimization model is a constrained, multivariable, nonlinear model. The particle swarm optimization algorithm is introduced to solve the model. The method of introducing penalty function to deal with the constraint condition is given. In this paper, the forecast model of bus passenger flow is applied to Zhengzhou bus Route 45, and the optimal dispatching is carried out on the basis of the forecast data. The results show that the emphasis on different stakeholders will have different effects on the departure interval, the total social cost of operation day, and the waiting time of passengers. The optimal dispatching model can provide a reference scheme for the dispatcher to make the departure schedule and adjust the train plan in the actual operation of public transportation.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U491.17;U492.22

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