【摘要】：城市公共交通是城市建設和社會生活的重要組成部分,對城市經(jīng)濟和居民生活具有深遠性、全面性的影響。但是,當前交通資源利用率低、交通擁堵、交通污染等問題日益嚴重,這些現(xiàn)實問題直接關系著人民群眾的切身利益。公交客流預測作為一種科學的措施,能為城市公共交通政策制定、系統(tǒng)規(guī)劃、運營管理提供重要信息,能幫助公交管理者制定合理的公交運營計劃和政策,是提高交通資源利用率、增強城市功能的重要途徑,對緩解交通擁堵、降低交通污染具有十分重要的作用。隨機森林是基于多棵決策樹的組合模型,相比于其他算法有較多的優(yōu)勢。然而在單機模式下,隨機森林的決策樹構建和預測投票過程都是串行化的,運行效率較低。數(shù)據(jù)量規(guī)模較大時,傳統(tǒng)單機環(huán)境下的隨機森林算法會消耗大量時間。Spark是一個分布式計算平臺,能夠輕松處理海量數(shù)據(jù),使得大規(guī)模,分布式迭代計算成為可能。本文結合了隨機森林和Spark兩者的優(yōu)點,將隨機森林作為公交客流預測模型,Spark作為隨機森林的并行化實現(xiàn)平臺。本文在現(xiàn)有公交客流數(shù)據(jù)的基礎上,使用Spark SQL統(tǒng)計和提取有用信息,對公交客流的出行規(guī)律進行分析。分別研究了客流的時間分布特征和動態(tài)影響因素,分析了公交客流在工作日、周末的變化規(guī)律,同時分析了天氣、溫度、節(jié)假日等因素對公交短時客流的影響。為了解決單機環(huán)境下隨機森林耗時長的問題,本文提出了基于Spark平臺的隨機森林并行化方法,實現(xiàn)了建樹和投票兩個過程的并行化。實驗結果表明,并行化隨機森林的運行效率要好于傳統(tǒng)單機環(huán)境下的隨機森林。另外,本文通過對比多種回歸模型的實驗結果,證實了并行化隨機森林在模型擬合度和預測精度上都能取得較好的效果�，F(xiàn)有對隨機森林的改進研究大多用于分類問題上,對于回歸問題的改進研究較少。本文總結了以往各方面的研究經(jīng)驗,提出了改進型隨機森林樣本相似度計算方法,并基于該計算方法對隨機森林的投票過程進行優(yōu)化,提出了加權投票方法。同時實現(xiàn)了改進型特征選擇算法,該算法能縮小隨機森林進行特征選擇時抽取的特征子集,減小不重要的特征對隨機森林預測效果的影響。實驗結果表明,改進后隨機森林模型的客流預測精度較改進前有所提高。
[Abstract]:Urban public transportation is an important part of urban construction and social life, which has far-reaching and comprehensive influence on urban economy and residents' life. However, the current low utilization of traffic resources, traffic congestion, traffic pollution and other problems are increasingly serious, these practical problems directly related to the vital interests of the people. As a scientific measure, bus passenger flow prediction can provide important information for urban public transport policy making, system planning and operation management, and can help public transport managers to formulate reasonable bus operation plans and policies. It is an important way to improve the utilization rate of traffic resources and enhance the function of the city. It plays an important role in alleviating traffic congestion and reducing traffic pollution. Stochastic forest is a combination model based on multiple decision trees, which has more advantages than other algorithms. However, in the single machine mode, the decision tree construction and prediction voting process of stochastic forest are serialized, and the operation efficiency is low. When the amount of data is large, the traditional stochastic forest algorithm in single computer environment will consume a lot of time. Spark is a distributed computing platform, which can easily process massive data, making large-scale and distributed iterative computing possible. Combining the advantages of stochastic forest and Spark, this paper takes stochastic forest as bus passenger flow prediction model and Spark as parallel implementation platform of stochastic forest. Based on the existing bus passenger flow data, this paper analyzes the travel rules of bus passenger flow by using Spark SQL statistics and extracting useful information. This paper studies the time distribution characteristics and dynamic influencing factors of passenger flow, analyzes the changing law of bus passenger flow on weekdays and weekends, and analyzes the influence of weather, temperature, holidays and other factors on the short-time passenger flow of public transport. In order to solve the problem of long time consuming of random forest in single machine environment, this paper proposes a parallel method of stochastic forest based on Spark platform, which realizes the parallelization of building and voting processes. The experimental results show that the operational efficiency of parallel random forest is better than that of traditional random forest in single machine environment. In addition, by comparing the experimental results of various regression models, it is proved that parallel stochastic forest can achieve good results in model fitting and prediction accuracy. Most of the existing researches on the improvement of stochastic forests are used for classification problems, but few researches on the improvement of regression problems. This paper summarizes the previous research experiences and proposes an improved method for calculating the similarity of random forest samples. Based on this method, the voting process of random forest is optimized and a weighted voting method is proposed. At the same time, an improved feature selection algorithm is implemented, which can reduce the feature subset extracted from the random forest for feature selection, and reduce the influence of the unimportant features on the prediction effect of the stochastic forest. The experimental results show that the prediction accuracy of passenger flow in the improved stochastic forest model is higher than that before the improvement.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.17;TP181;TP311.13

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