本文選題：城市公交 + 智能公交��； 參考：《長(zhǎng)安大學(xué)》2017年碩士論文

【摘要】：大力發(fā)展城市公交,實(shí)現(xiàn)城市公交智能化,是當(dāng)前公認(rèn)的解決城市交通問題的有效辦法。目前我國(guó)部分城市已經(jīng)引入了電子站牌、電子站臺(tái)等智能手段,但是整體智能化水平仍然不高。特別是運(yùn)營(yíng)積累的海量公交數(shù)據(jù)具有明顯的大數(shù)據(jù)特征,但基本沒有得到充分的發(fā)掘及應(yīng)用。有鑒于此,本論文圍繞傳統(tǒng)的城市公交管理業(yè)務(wù),設(shè)計(jì)并開發(fā)了一款面向大數(shù)據(jù)的城市智能公交管理系統(tǒng)。該系統(tǒng)具有公交智能調(diào)度、乘客出行服務(wù)與數(shù)據(jù)統(tǒng)計(jì)分析等功能,對(duì)于充分挖掘公交數(shù)據(jù)價(jià)值、提升公交管理智能化水平具有重要價(jià)值。具體而言,本論文的研究?jī)?nèi)容包括:(1)系統(tǒng)總體設(shè)計(jì)。通過對(duì)我國(guó)城市公交行業(yè)城市公交運(yùn)營(yíng)和管理需求進(jìn)行分析,設(shè)計(jì)了基于大數(shù)據(jù)的城市智能公交管理系統(tǒng)的整體框架。該框架圍繞公交大數(shù)據(jù)的采集、傳輸、存儲(chǔ)分析以及展示等方面進(jìn)行搭建,涵蓋了公交智能調(diào)度、到站時(shí)間預(yù)測(cè)和客流量預(yù)測(cè)等功能。(2)基于大數(shù)據(jù)的數(shù)據(jù)分析算法設(shè)計(jì)。通過利用MapReduce分布式計(jì)算框架對(duì)算法進(jìn)行改造,從而將復(fù)雜的數(shù)據(jù)分析處理過程解構(gòu)為相互獨(dú)立的細(xì)粒度過程,并由獨(dú)立的大數(shù)據(jù)處理節(jié)點(diǎn)進(jìn)行并行處理,從而滿足公交大數(shù)據(jù)的快速數(shù)據(jù)分析需要。實(shí)驗(yàn)證明,利用MapReduce改造的數(shù)據(jù)分析算法可以大大縮短數(shù)據(jù)處理時(shí)間,從而很好地滿足城市智能公交管理系統(tǒng)對(duì)公交大數(shù)據(jù)的處理要求。(3)系統(tǒng)研發(fā)。為高效地進(jìn)行系統(tǒng)開發(fā),借用Jeesite基礎(chǔ)架構(gòu),設(shè)計(jì)了基于JeeSite的城市智能公交管理系統(tǒng)。該系統(tǒng)采用分層架構(gòu),自下而上分別包括數(shù)據(jù)庫層、數(shù)據(jù)訪問層、業(yè)務(wù)邏輯層和展示層。并圍繞數(shù)據(jù)分析流程,分別實(shí)現(xiàn)了相應(yīng)的的大數(shù)據(jù)采集方案、數(shù)據(jù)處理算法以及數(shù)據(jù)分析結(jié)果的查詢與展示模塊。系統(tǒng)測(cè)試和模擬應(yīng)用表明,該系統(tǒng)有助于挖掘公交大數(shù)據(jù)價(jià)值并提升城市公交的運(yùn)營(yíng)和管理水平。
[Abstract]:It is an effective way to solve urban traffic problems to develop urban public transportation and realize urban transit intelligentization. At present, some cities in our country have introduced intelligent means such as electronic station board and electronic platform, but the overall intelligence level is still not high. Especially the mass transit data accumulated by operation has obvious big data characteristics, but it has not been fully explored and applied. In view of this, this paper designs and develops a big data oriented urban intelligent bus management system around the traditional urban transit management business. The system has the functions of bus intelligent dispatch, passenger travel service and statistical analysis of data. It is of great value to fully excavate the value of public transportation data and improve the intelligent level of bus management. Specifically, the research content of this paper includes the overall design of the system. Based on the analysis of the operation and management requirements of urban public transport in China's urban transit industry, the overall framework of urban intelligent bus management system based on big data is designed. The framework is built around the collection, transmission, storage analysis and display of the bus big data. It covers the functions of bus intelligent dispatch, arrival time prediction and passenger flow prediction, etc.) the data analysis algorithm based on big data is designed. By using the MapReduce distributed computing framework to transform the algorithm, the complex data analysis and processing process is deconstructed into an independent fine-grained process, and parallel processing is carried out by an independent big data processing node. In order to meet the bus big data rapid data analysis needs. Experimental results show that the data analysis algorithm modified by MapReduce can greatly shorten the time of data processing, and thus meet the requirements of urban intelligent bus management system for the processing of bus big data. In order to develop the system efficiently, the urban intelligent bus management system based on JeeSite is designed by using Jeesite infrastructure. The system adopts hierarchical architecture, including database layer, data access layer, business logic layer and presentation layer from bottom to top. Around the data analysis flow, the corresponding big data collection scheme, data processing algorithm and query and display module of data analysis results are implemented respectively. The system test and simulation show that the system is helpful to excavate the big data value of public transportation and improve the operation and management level of urban public transportation.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U495;TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李林波;康琳涓;王婧;;基于天氣影響分析的公交客流調(diào)查日期確定方法[J];交通科技;2016年01期

2 米根鎖;梁利;楊潤(rùn)霞;;灰色變異粒子群算法在公交客流量預(yù)測(cè)中的應(yīng)用[J];計(jì)算機(jī)工程與科學(xué);2015年01期

3 汪s，

本文編號(hào)：1958972