本文關(guān)鍵詞：支持智能交通的數(shù)據(jù)分析技術(shù)及其應(yīng)用研發(fā)　出處：《南京大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 大數(shù)據(jù) 歷史記錄 數(shù)據(jù)分析技術(shù) 軌跡檢測(cè)

【摘要】：隨著經(jīng)濟(jì)的發(fā)展、城市化進(jìn)程的不斷深入,傳感器技術(shù)、通信技術(shù)、地理信息系統(tǒng)(GIS)技術(shù)和計(jì)算機(jī)技術(shù)的不斷發(fā)展,智能交通技術(shù)應(yīng)運(yùn)而生。它將先進(jìn)的科學(xué)技術(shù)有效地綜合運(yùn)用于整個(gè)地面交通管理系統(tǒng),加強(qiáng)車輛、道路、使用者三者之間的聯(lián)系,全方位全天候發(fā)揮作用,從而形成一種保障安全、提高效率、改善環(huán)境、節(jié)約能源的綜合運(yùn)輸管理系統(tǒng)。物聯(lián)網(wǎng)和通信技術(shù)的快速發(fā)展使得交通信息的發(fā)布不再是瓶頸,要實(shí)現(xiàn)交通信息應(yīng)用的持續(xù)快速增長(zhǎng),如何獲取原始交通數(shù)據(jù)并處理成精準(zhǔn)的交通信息是其關(guān)鍵�？梢哉f(shuō),交通信息的采集和處理是智能交通系統(tǒng)的關(guān)鍵技術(shù)。目前對(duì)于實(shí)時(shí)交通數(shù)據(jù)的采集主要有兩種方式:一種是靜態(tài)交通探測(cè);一種是動(dòng)態(tài)交通探測(cè)。車牌識(shí)別屬于靜態(tài)交通探測(cè)的一種,由于它能自動(dòng)快速的提取車牌信息,得到了廣泛的應(yīng)用。然而,受制于光照、氣候、車輛速度、遮擋物體或傳感器失靈等不可控因素的干擾,自動(dòng)車牌識(shí)別的準(zhǔn)確度一直受到外部或內(nèi)部因素的干擾,在實(shí)際應(yīng)用中經(jīng)常出現(xiàn)識(shí)別效果不理想的情況。作為動(dòng)態(tài)交通流信息采集的主要手段,GPS技術(shù)在國(guó)內(nèi)外得到了廣泛的應(yīng)用,可以實(shí)時(shí)提供三維坐標(biāo)、速度等空間信息。交通領(lǐng)域使用GPS最主要群體是公交和出租車,大量的GPS記錄,包含了一個(gè)城市交通的客觀屬性和規(guī)律,也反映出出租車司機(jī)主觀上的行駛習(xí)慣,提供了一個(gè)供我們觀察分析出租車司機(jī)的窗口,而目前這些數(shù)據(jù)尚未得到充分應(yīng)用。鑒于上述問(wèn)題,本文從交通信息歷史記錄出發(fā),首先提出了自動(dòng)車牌糾錯(cuò)方法,該方法獨(dú)立于自動(dòng)車牌識(shí)別方法,而從一系列識(shí)別后的數(shù)據(jù)出發(fā),嘗試糾正識(shí)別錯(cuò)誤的車牌,并給出各個(gè)設(shè)備識(shí)別錯(cuò)誤的原因;然后就出租車GPS數(shù)據(jù),提出了一個(gè)在線的異常軌跡檢測(cè)方法,該方法分為路徑推薦和異常檢測(cè)兩部分,首先從出租車歷史軌跡數(shù)據(jù)出發(fā),給出起點(diǎn)和終點(diǎn)之間的路徑推薦,然后,在線的對(duì)出租車行駛過(guò)程進(jìn)行異常檢測(cè)。
[Abstract]:With the development of economy and urbanization, sensor technology, communication technology, geographic information system (GIS) technology and computer technology are developing continuously. Intelligent transportation technology emerges as the times require. It effectively applies advanced science and technology to the whole ground traffic management system, strengthens the connection among vehicles, roads and users, and plays an all-weather role in all directions. A comprehensive transportation management system is formed to ensure safety, improve efficiency, improve environment and save energy. With the rapid development of Internet of things and communication technology, the release of traffic information is no longer the bottleneck. In order to realize the continuous and rapid growth of traffic information application, how to obtain the original traffic data and process it into accurate traffic information is the key. The acquisition and processing of traffic information is the key technology of its. There are two main ways to collect real-time traffic data: one is static traffic detection; One is dynamic traffic detection. License plate recognition is a kind of static traffic detection. Because it can extract license plate information automatically and quickly, it is widely used. However, it is restricted by illumination, climate and vehicle speed. The accuracy of automatic license plate recognition is always interfered by external or internal factors due to the interference of uncontrollable factors such as occlusion object or sensor failure. As the main means of dynamic traffic flow information collection, GPS technology has been widely used at home and abroad, which can provide three-dimensional coordinates in real time. Speed and other spatial information. The most important group using GPS in the field of transportation is bus and taxi. A large number of GPS records include the objective attributes and laws of a city traffic. It also reflects the subjective driving habits of taxi drivers and provides a window for us to observe and analyze taxi drivers, and these data are not yet fully applied. Based on the history of traffic information, this paper first proposes an automatic license plate error correction method, which is independent of the automatic license plate recognition method, and from a series of data after recognition, try to correct the recognition of the wrong license plate. The causes of identifying errors for each equipment are also given. Then on the taxi GPS data, an online anomaly track detection method is proposed, which is divided into two parts: path recommendation and anomaly detection. Firstly, starting from the taxi history track data. The path recommendation between the starting point and the end point is given, and then the abnormal detection of taxi driving process is carried out online.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：U495;TP311.13

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本文編號(hào)：1407351