本文關(guān)鍵詞：基于智能手機(jī)GPS的大學(xué)生出行方式識(shí)別研究　出處：《江蘇大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 大學(xué)生 智能手機(jī)GPS 出行方式識(shí)別 改進(jìn)粒子群算法 支持向量機(jī)

【摘要】：隨著我國高等教育規(guī)模的快速發(fā)展,高等院校遷至城郊結(jié)合部甚至遠(yuǎn)郊已成為普遍現(xiàn)象,但城郊結(jié)合部的交通基礎(chǔ)設(shè)施很難滿足高校師生的出行需求。為降低大學(xué)生出行對(duì)校園周邊交通乃至城市交通網(wǎng)絡(luò)的影響,需對(duì)交通網(wǎng)絡(luò)進(jìn)行優(yōu)化設(shè)計(jì),對(duì)大學(xué)生進(jìn)行出行調(diào)查是必須開展的一項(xiàng)基礎(chǔ)性工作。傳統(tǒng)出行調(diào)查方法主要依賴被訪問者對(duì)行程的回憶及其主觀認(rèn)知,調(diào)查數(shù)據(jù)往往存在較多缺陷:如被訪問者的負(fù)擔(dān)較重、訪問回應(yīng)率低、數(shù)據(jù)質(zhì)量差、存在漏報(bào)錯(cuò)報(bào)等�；诒銛y式GPS的出行調(diào)查同樣存在一定缺陷:調(diào)查成本高和因忘帶便攜式GPS而導(dǎo)致出行數(shù)據(jù)遺漏等。隨著手機(jī)技術(shù)的不斷發(fā)展,GPS定位系統(tǒng)已成為智能手機(jī)標(biāo)配。如今大學(xué)生出門攜帶智能手機(jī)已成為日常習(xí)慣。因此,基于智能手機(jī)GPS的大學(xué)生出行調(diào)查不僅能夠降低出行調(diào)查費(fèi)用,而且能減少出行數(shù)據(jù)遺漏現(xiàn)象的發(fā)生。從智能手機(jī)GPS記錄的出行軌跡數(shù)據(jù)中提取出行信息以及識(shí)別出行方式成為分析大學(xué)生出行行為一種新途徑。(1)本文先對(duì)大學(xué)生出行方式調(diào)查方案及實(shí)施方案進(jìn)行研究,認(rèn)真分析其優(yōu)缺點(diǎn),為大學(xué)生出行軌跡數(shù)據(jù)處理與出行方式識(shí)別提供數(shù)據(jù)保障。(2)對(duì)利用智能手機(jī)GPS收集的大學(xué)生出行軌跡數(shù)據(jù)進(jìn)行處理:首先是對(duì)軌跡數(shù)據(jù)進(jìn)行預(yù)處理,包括數(shù)據(jù)過濾和數(shù)據(jù)格式轉(zhuǎn)換;然后是在GPS信號(hào)缺失情況下選擇停留時(shí)間和平均速度這兩個(gè)參數(shù),在GPS信號(hào)正常情況下選擇臨界距離、最小停留時(shí)間和最大停留時(shí)間這三個(gè)參數(shù),基于混合方法利用這五個(gè)參數(shù)進(jìn)行出行段識(shí)別;最后提取出行特征變量,并利用箱線圖法和組間均值等式檢驗(yàn)法驗(yàn)證其有效性。(3)對(duì)大學(xué)生出行方式進(jìn)行識(shí)別。針對(duì)粒子群算法容易早熟收斂的缺陷,本文利用改進(jìn)粒子群來優(yōu)化支持向量機(jī)。然后利用IPSO-SVM模型對(duì)步行、自行車、電動(dòng)車、校園公交、公交車和出租車進(jìn)行識(shí)別研究。選取線性核函數(shù)、多項(xiàng)式核函數(shù)和徑向基核函數(shù)分別作為SVM的核函數(shù),得出不同核函數(shù)下IPSO-SVM模型的出行方式識(shí)別精度,并選擇平均識(shí)別精度最高的作為IPSO-SVM模型最終識(shí)別精度。將IPSO-SVM模型識(shí)別精度與其他常用出行方式識(shí)別模型的識(shí)別精度進(jìn)行對(duì)比。研究結(jié)果表明,本文提出的IPSO-SVM模型在基于智能手機(jī)GPS的大學(xué)生出行方式識(shí)別研究中具有更好的識(shí)別精度。研究結(jié)論對(duì)智能手機(jī)GPS在大學(xué)生出行研究領(lǐng)域的推廣,科學(xué)地分析大學(xué)生的出行規(guī)律,診斷高校周邊的交通問題,促進(jìn)高校周邊交通系統(tǒng)的健康發(fā)展具有深遠(yuǎn)意義。
[Abstract]:With the rapid development of China's higher education scale, it has become a common phenomenon that institutions of higher learning move to suburban areas or even suburbs. In order to reduce the impact of college students' travel on campus traffic and even urban traffic network, the traffic network should be optimized. It is necessary to carry out a trip survey for college students. The traditional travel survey method mainly depends on the visitors' recollection of the trip and their subjective cognition. Survey data often have more shortcomings: such as the heavy burden of the interviewee, low response rate and poor data quality. There are false reports and so on. The trip survey based on portable GPS also has some defects: high cost of investigation and omission of travel data due to forgetting portable GPS. With the development of mobile phone technology. The GPS location system has become the standard for smartphones. Nowadays, it is a daily habit for college students to go out and carry their smartphones. The trip survey of college students based on smart phone GPS can not only reduce the cost of travel survey. It can also reduce the occurrence of trip data omission. Extracting trip information from trip track data recorded by GPS of smart phone and identifying trip mode become a new way to analyze college students' trip behavior. In this paper, first of all, the investigation scheme and implementation plan of college students' travel mode are studied. Carefully analyze its advantages and disadvantages. Provides data guarantee for the data processing and identification of college students' trip path. (2) processing the data of college students' trip path collected by smart phone GPS: firstly, preprocessing the path data. Including data filtering and data format conversion; Then the two parameters of residence time and average velocity are selected in the absence of GPS signal, and the three parameters of critical distance, minimum residence time and maximum residence time are selected under the normal condition of GPS signal. The five parameters are used to identify the travel segment based on the hybrid method. Finally, the travel characteristic variables are extracted, and the validity of the method is verified by the box-line graph method and the mean equality test method between groups. The paper aims at the shortcomings of particle swarm optimization (PSO), which is easy to converge prematurely. This paper uses improved particle swarm optimization (PSO) to optimize support vector machine (SVM). Then the IPSO-SVM model is used for walking, bicycle, electric vehicle and campus bus. The linear kernel function, polynomial kernel function and radial basis kernel function are selected as the kernel functions of SVM. The identification accuracy of IPSO-SVM model under different kernel functions is obtained. The recognition accuracy of IPSO-SVM model is compared with that of other common travel modes. The results show that. The IPSO-SVM model proposed in this paper has better recognition accuracy in the research of college students' trip pattern identification based on smart phone GPS. The conclusion of the research is that the smart phone GPS can be used in the field of college students' travel research. Promotion. It is of great significance to scientifically analyze the travel rules of college students, to diagnose the traffic problems around colleges and universities, and to promote the healthy development of traffic system around colleges and universities.
【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 Lei Gong;Hitomi Sato;Toshiyuki Yamamoto;Tomio Miwa;Takayuki Morikawa;;Identification of activity stop locations in GPS trajectories by density-based clustering method combined with support vector machines[J];Journal of Modern Transportation;2015年03期

2 汪磊;左忠義;傅軍豪;;基于SVM的出行方式特征分析和識(shí)別研究[J];交通運(yùn)輸系統(tǒng)工程與信息;2014年03期

3 陳云鳳;云挺;周宇;鄧玉和;王嫻;;基于PSO優(yōu)化SVM的紋理圖像分割[J];計(jì)算機(jī)應(yīng)用與軟件;2014年04期

4 王曉霞;王濤;谷根代;;基于改進(jìn)粒子群優(yōu)化的神經(jīng)網(wǎng)絡(luò)及應(yīng)用[J];華北電力大學(xué)學(xué)報(bào)(自然科學(xué)版);2009年05期

5 劉飛,周琳琳,益建芳;GPS大地坐標(biāo)向地方坐標(biāo)轉(zhuǎn)換的實(shí)用方法研究[J];華東師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2005年01期

6 張偉宏,胡勁松,王力強(qiáng);GPS系統(tǒng)在交通領(lǐng)域中的應(yīng)用及展望[J];黑龍江交通科技;2003年02期

相關(guān)博士學(xué)位論文 前1條

1 張治華;基于GPS軌跡的出行信息提取研究[D];華東師范大學(xué);2010年

相關(guān)碩士學(xué)位論文 前8條

1 白玉;基于GPS數(shù)據(jù)的出行方式識(shí)別方法研究[D];吉林大學(xué);2016年

2 胡程磊;數(shù)據(jù)驅(qū)動(dòng)的建筑電能耗預(yù)測(cè)方法研究[D];江蘇大學(xué);2016年

3 王瀟;基于GPS數(shù)據(jù)的出行—活動(dòng)識(shí)別方法研究[D];吉林大學(xué);2015年

4 楊三華;一種改進(jìn)的PSO-SVM及其在氣體絕緣系統(tǒng)中的應(yīng)用[D];華南理工大學(xué);2014年

5 王園;基于SVM_AdaBoost模型的上市公司退市預(yù)警研究[D];華南理工大學(xué);2013年

6 閆彭;基于AGPS手機(jī)的交通方式識(shí)別研究[D];北京交通大學(xué);2012年

7 張俊峰;基于GPS技術(shù)的出行OD調(diào)查研究[D];北京交通大學(xué);2011年

8 曹鴻雁;高校大學(xué)生的出行行為研究[D];天津大學(xué);2008年

，

本文編號(hào)：1398066