本文選題：數(shù)據(jù)挖掘　切入點(diǎn)：常規(guī)公交服務(wù)水平　出處：《西南交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著城市規(guī)模的急劇擴(kuò)大與人口數(shù)量的快速增長(zhǎng),常規(guī)公交已成為城市居民日常出行的主要方式,如何評(píng)價(jià)與提升常規(guī)公交服務(wù)水平已成為城市交通發(fā)展的主要目標(biāo)之一。為了將評(píng)價(jià)結(jié)果有針對(duì)性地應(yīng)用到改進(jìn)措施中,論文以一條公交線路為研究對(duì)象,采用數(shù)據(jù)挖掘技術(shù)對(duì)常規(guī)公交服務(wù)水平進(jìn)行評(píng)價(jià)。論文首先介紹了六種公交數(shù)據(jù)調(diào)查與獲取的常用方法,并對(duì)各種方法的優(yōu)劣與適用性進(jìn)行分析。結(jié)合公交服務(wù)水平評(píng)價(jià)的要求與公交數(shù)據(jù)樣本的特點(diǎn),論文明確了公交數(shù)據(jù)挖掘的目標(biāo)與工作流程,重點(diǎn)介紹分類分析與聚類分析兩種任務(wù)與相應(yīng)的工具模型,并對(duì)其進(jìn)行實(shí)例探討,決定采用決策樹模型對(duì)公交服務(wù)水平評(píng)價(jià)體系進(jìn)行分類挖掘研究。接著,論文以一條公交線路為研究對(duì)象,選取線路、車輛與營(yíng)運(yùn)三個(gè)層次共9個(gè)指標(biāo)構(gòu)建常規(guī)公交服務(wù)水平評(píng)價(jià)體系,并利用層次分析法對(duì)所選指標(biāo)進(jìn)行合理性檢驗(yàn)。基于信息傳遞理論與決策樹基本理論,論文提出了常規(guī)公交服務(wù)水平評(píng)價(jià)數(shù)據(jù)挖掘模型(DRBSE模型),并詳細(xì)闡述了該模型的挖掘流程：建立決策樹、決策樹剪枝與提取模型規(guī)則。在建立決策樹時(shí),對(duì)指標(biāo)的順序選擇進(jìn)行探討,使用信息增益率代替信息增益計(jì)算該屬性的信息傳遞值,同時(shí)選擇后剪枝法對(duì)決策樹進(jìn)行剪枝,并按照模型生成的枝葉結(jié)構(gòu)進(jìn)行規(guī)則提取。論文的最后以四川省207條公交線路為樣本實(shí)例,以SPSS-Clementine為軟件操作平臺(tái),利用DRBSE模型對(duì)常規(guī)公交服務(wù)水平評(píng)價(jià)體系進(jìn)行建模,運(yùn)行結(jié)果按照數(shù)據(jù)概率比高于70%的標(biāo)準(zhǔn)整理提取10條樹形規(guī)則與指標(biāo)重要度排序,經(jīng)分析得出不同環(huán)境下提升常規(guī)公交服務(wù)水平的相應(yīng)建議,驗(yàn)證了數(shù)據(jù)挖掘技術(shù)對(duì)常規(guī)公交服務(wù)水平評(píng)價(jià)的優(yōu)越性。
[Abstract]:With the rapid expansion of urban scale and the rapid growth of population, bus routine has become the main way of daily travel for urban residents. How to evaluate and improve the service level of conventional public transport has become one of the main goals of urban traffic development. In order to apply the evaluation results to the improvement measures, the paper takes a bus route as the research object. Data mining technology is used to evaluate the service level of conventional public transport. Firstly, six common methods of public transportation data investigation and acquisition are introduced in this paper. Combined with the requirements of bus service level evaluation and the characteristics of bus data samples, the paper clarifies the goal and workflow of bus data mining. This paper mainly introduces two kinds of tasks and corresponding tool models of classification analysis and cluster analysis, and discusses them by examples, and decides to use the decision tree model to study the classification and mining of the evaluation system of bus service level. This paper takes one bus route as the research object, selects the line, the vehicle and the operation three levels altogether 9 indexes constructs the conventional public transport service level appraisal system. Based on the theory of information transfer and the basic theory of decision tree, the rationality of the selected index is tested by AHP. In this paper, a DRBSE model for the evaluation of bus service level is proposed, and the mining process of the model is described in detail: establishing decision tree, pruning decision tree and extracting model rules. The sequential selection of the index is discussed. The information gain rate is used instead of the information gain to calculate the information transfer value of the attribute. At the same time, the decision tree is pruned by selecting the pruning method. At the end of this paper, 207 bus routes in Sichuan Province are taken as sample examples, SPSS-Clementine as software operating platform, and DRBSE model is used to model the evaluation system of bus service level. According to the standard of data probability ratio higher than 70%, the operation results extract 10 tree rules and index importance ranking, and through the analysis of the corresponding suggestions to improve the general bus service level in different environments. The superiority of data mining technology in the evaluation of bus service level is verified.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP311.13;U491.17

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本文編號(hào)：1622174