本文選題：GPS + 轉(zhuǎn)換點(diǎn)識(shí)別�。� 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：隨著我國社會(huì)經(jīng)濟(jì)發(fā)展和城市化進(jìn)程速度的加快,交通擁堵、交通事故、交通環(huán)境等問題已成為我國城市常見的"城市病"。科學(xué)的交通規(guī)劃和管理被大多數(shù)學(xué)者認(rèn)為是解決城市交通擁堵等問題的有效手段,而居民出行信息的掌握是科學(xué)的交通規(guī)劃和管理的信息支撐。居民出行信息獲取最廣泛的調(diào)查方法是傳統(tǒng)的居民出行OD調(diào)查。但該方式在實(shí)際中受被調(diào)查者主觀意識(shí)影響,出現(xiàn)漏報(bào)、錯(cuò)報(bào)的現(xiàn)象,影響調(diào)查數(shù)據(jù)的質(zhì)量,同時(shí)存在成本高、工作量大、回收率低和處理周期長(zhǎng)等問題,影響后續(xù)的交通規(guī)劃和管理工作�，F(xiàn)今,全球定位系統(tǒng)(GPS)的廣泛應(yīng)用,以及GPS出行數(shù)據(jù)不受被調(diào)查者主觀意識(shí)的影響,能夠體現(xiàn)更準(zhǔn)確、完整的個(gè)人出行行為信息,同時(shí)還具備調(diào)查效率高、數(shù)據(jù)精度高、獲取信息量大等優(yōu)點(diǎn),使得這個(gè)調(diào)查方式成為居民出行行為信息獲取的有效的新途徑。挖掘分析GPS數(shù)據(jù)獲得更為精確、完整的出行行為信息,進(jìn)行交通出行方式識(shí)別是獲取個(gè)人出行信息的重要組成部分,尤其轉(zhuǎn)換點(diǎn)的識(shí)別作為交通出行方式識(shí)別的重要組成部分,是當(dāng)前研究的難點(diǎn)所在。本文基于GPS軌跡數(shù)據(jù)的交通出行方式識(shí)別研究。首先提出兩種基于相似性度量和窗口的轉(zhuǎn)換點(diǎn)識(shí)別方法,即多段方法和移動(dòng)窗口兩種方法,然后比較兩種轉(zhuǎn)換點(diǎn)識(shí)別方法,獲取最優(yōu)的方法,最后把該方法運(yùn)用到交通出行方式識(shí)別中獲取出行方式段,提取所獲出行方式段的特征參數(shù),采用BP神經(jīng)網(wǎng)絡(luò)、決策樹、KNN和支持向量機(jī)(SVM)四種模式識(shí)別的方法識(shí)別交通出行方式。采用Geolife工程的GPS軌跡數(shù)據(jù)對(duì)本文所提出算法進(jìn)行驗(yàn)證。在基于相似性度量和窗口方法的轉(zhuǎn)換點(diǎn)識(shí)別中,比較多段和移動(dòng)窗口兩種算法結(jié)果,得到采用多段的方法使得識(shí)別結(jié)果最優(yōu),F-score取值接近80%,其中召回率接近90%。在交通出行方式的識(shí)別中,四種模式識(shí)別出的結(jié)果與實(shí)際交通出行方式進(jìn)行比較驗(yàn)證,得到采用SVM識(shí)別交通出行方式能夠取得最優(yōu)結(jié)果,訓(xùn)練集和測(cè)試集分別為92.75%和88.77%。最后,采用時(shí)間指標(biāo),進(jìn)行交通出行識(shí)別結(jié)果的綜合評(píng)價(jià),得到識(shí)別的準(zhǔn)確率由88.77%提高到91.84%。驗(yàn)證了本文所提出的算法具有較高的識(shí)別精度。本文提出基于GPS軌跡數(shù)據(jù)的交通出行方式識(shí)別算法,通過實(shí)驗(yàn)驗(yàn)證了該方法的有效性,其研究結(jié)論可應(yīng)用于分析居民出行行為特征,該研究結(jié)果所獲得的居民出行信息特征是后期交通規(guī)劃和管理的重要數(shù)據(jù)支撐。
[Abstract]:With the rapid development of social economy and urbanization in China, traffic congestion, traffic accidents, traffic environment and other problems have become a common "urban disease" in Chinese cities. Scientific traffic planning and management is considered by most scholars to be an effective means to solve problems such as urban traffic congestion, and the information of resident travel information is the information support of scientific traffic planning and management. The traditional OD survey is the most widely used method to obtain residents' travel information. However, this method is affected by the subjective consciousness of the respondents in practice, and the phenomenon of underreporting and misreporting affects the quality of the investigation data. At the same time, there are some problems, such as high cost, large workload, low recovery rate and long processing period, etc. Impact on subsequent traffic planning and management. Nowadays, with the wide application of GPS and the fact that GPS travel data are not affected by the subjective consciousness of the respondents, they can reflect more accurate and complete personal travel behavior information, at the same time, they also have high investigation efficiency and high data accuracy. Because of the large amount of information obtained, this investigation method is an effective new way to obtain the information of residents' travel behavior. Mining and analyzing GPS data to obtain more accurate and complete travel behavior information, the identification of traffic travel mode is an important part of obtaining personal travel information, especially the identification of transition points as an important part of the identification of traffic travel mode. It is the difficulty of current research. In this paper, the identification of traffic travel mode based on GPS track data is studied. At first, two methods based on similarity measurement and window are proposed, that is, multi-segment method and moving window method. Then, the two methods are compared to obtain the best method. Finally, the method is applied to obtain the travel mode segment in the identification of traffic travel mode, and the characteristic parameters of the obtained travel mode segment are extracted, and the BP neural network is used. Decision tree (KNN) and support vector machine (SVM) are used to identify traffic travel modes. The proposed algorithm is verified by using the GPS trajectory data of Geolife project. In the conversion point recognition based on similarity measurement and window method, the results of multi-segment and moving window are compared, and the optimal F-score of recognition results is obtained by using multi-segment method, in which the recall rate is close to 90. In the recognition of traffic travel mode, the results of the four patterns are compared with the actual traffic travel mode. The results show that SVM can obtain the best result, the training set and the test set are 92.75% and 88.77% respectively. Finally, the time index is used to evaluate the result of traffic trip identification, and the accuracy of identification is improved from 88.77% to 91.84%. It is verified that the proposed algorithm has high recognition accuracy. In this paper, an algorithm based on GPS trajectory data is proposed to identify traffic travel patterns. The validity of the method is verified by experiments. The research results can be used to analyze the characteristics of residents' travel behavior. The characteristics of resident travel information obtained from the results of this study are important data support for later traffic planning and management.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P228.4;U491.1

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本文編號(hào)：2078354