本文關(guān)鍵詞： 公共自行車 需求預(yù)測 調(diào)度優(yōu)化 BP神經(jīng)網(wǎng)絡(luò) 關(guān)聯(lián)規(guī)則 K-medoids 聚類 蟻群算法　出處：《北京交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：城市公共自行車兼具承擔(dān)公共交通"最后一公里",服務(wù)短距離出行和服務(wù)旅游景區(qū)及校園三大主要功能,是新時代人文、科技、綠色交通的產(chǎn)物。面對目前諸多城市出現(xiàn)的自行車站點(diǎn)無車可借,無位可還的現(xiàn)象,究其主要原因,是調(diào)度的不及時所致。因此,如何提前準(zhǔn)確預(yù)知各站點(diǎn)出行需求及安排合理的調(diào)度路徑方案對于解決調(diào)度的滯后性具有重要意義。由于國內(nèi)城市可獲取的PBS(public bicycle system)運(yùn)營數(shù)據(jù)較少,因此本文選取美國紐約市的出行數(shù)據(jù)進(jìn)行PBS出行需求及調(diào)度優(yōu)化的研究。在對紐約市的公共自行車出行需求分析時,發(fā)現(xiàn)其出行規(guī)律和國內(nèi)城市相似——工作日均存在早晚高峰的現(xiàn)象。另外,加上數(shù)據(jù)樣本較大,可靠性強(qiáng),因此基于紐約市出行數(shù)據(jù)的預(yù)測方法同樣適用于國內(nèi)城市。本文具體的研究工作包含以下幾個方面:(1)對公共自行車的系統(tǒng)功能定位進(jìn)行了闡述,并結(jié)合美國紐約市2016年6月份146萬條公共自行車出行數(shù)據(jù)對出行需求進(jìn)行了主客觀因素分析,發(fā)現(xiàn)周末與工作日出行規(guī)律明顯不同。(2)簡單介紹了 BP神經(jīng)網(wǎng)絡(luò)理論,提出一種新的考慮日期屬性的站點(diǎn)租還車需求預(yù)測方法。在利用BP神經(jīng)網(wǎng)絡(luò)預(yù)測時,輸入變量的日期屬性應(yīng)與輸出變量保持一致,即同為工作日或同為周末。使用SQL Server數(shù)據(jù)庫和Matlab軟件對實(shí)例進(jìn)行了預(yù)測,效果較好,預(yù)測相對誤差均較低,最低預(yù)測相對誤差可達(dá)到1%。(3)概述了車輛調(diào)度問題的定義、分類及常用的優(yōu)化算法,并將城市公共自行車的調(diào)度問題分為調(diào)度分區(qū)和區(qū)域內(nèi)調(diào)度兩步。首先對整個城市的自行車站點(diǎn)進(jìn)行了調(diào)度區(qū)域的劃分,在此提出了一種新的將關(guān)聯(lián)規(guī)則與聚類分析相結(jié)合的劃分方法,通過三次聚類,將城市所有站點(diǎn)劃分成不同的靜態(tài)調(diào)度區(qū)域。其次,對于每個靜態(tài)區(qū)域內(nèi)有調(diào)度需求的站點(diǎn)建立調(diào)度模型,目標(biāo)是使調(diào)度車數(shù)量最少和總調(diào)度時間最短。并介紹了帶2-opt局部搜索的蟻群算法求解模型的步驟與方法。(4)以美國紐約市21個站點(diǎn)數(shù)據(jù)為例,進(jìn)行調(diào)度路徑的優(yōu)化仿真。通過MapGIS軟件轉(zhuǎn)換坐標(biāo)和Matlab編程求解,發(fā)現(xiàn)帶2-opt局部搜索的蟻群算法比常規(guī)蟻群算法總調(diào)度時間更短,效果更好。
[Abstract]:Urban public bicycles have the three main functions of undertaking the "last kilometer" of public transport, serving short distance travel, serving scenic spots and campus. They are the humanities and science and technology of the new era. The product of green traffic. Facing the phenomenon that there is no car to borrow or return to the bicycle station in many cities at present, the main reason is that the dispatch is not in time. Therefore, How to predict the travel demand of each station in advance and how to arrange a reasonable scheduling path plan is of great significance to solve the lag of scheduling. Because of the lack of PBS(public bicycle system operation data available in domestic cities, Therefore, this paper selects the travel data of New York City to study the PBS travel demand and scheduling optimization. It is found that the travel pattern is similar to that of domestic cities and that there are morning and evening peaks on weekdays. In addition, the data samples are large and reliable. Therefore, the prediction method based on New York City travel data is also applicable to domestic cities. Combined with the data of 1.46 million public bicycle trips in New York City on June 2016, this paper analyzes the subjective and objective factors of travel demand, and finds that the travel law of weekends is obviously different from that of weekdays.) the BP neural network theory is briefly introduced. In this paper, a new forecasting method of station rental and return demand considering date attribute is proposed. When using BP neural network, the date attribute of input variable should be consistent with that of output variable. Using SQL Server database and Matlab software to predict the example, the result is better, the relative error of prediction is lower, and the lowest relative error of prediction can reach 1 / 3) the definition of vehicle scheduling problem is summarized. The scheduling problem of urban public bicycle is divided into two steps: the scheduling partition and the intra-area scheduling. Firstly, the scheduling area of the bicycle station in the whole city is divided. In this paper, a new partitioning method combining association rules with clustering analysis is proposed, which divides all stations into different static scheduling areas through three clusters. Establish a scheduling model for each site with scheduling requirements within a static region, The aim is to minimize the number of dispatching vehicles and the shortest total scheduling time. The steps and methods of solving the model of ant colony algorithm with 2-opt local search are introduced. The optimization simulation of scheduling path is carried out. Through MapGIS software coordinate transformation and Matlab programming, it is found that the total scheduling time of the algorithm with 2-opt local search is shorter and the effect is better than that of the conventional ant colony algorithm.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：U491.225

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