本文選題：自動(dòng)車牌識(shí)別數(shù)據(jù) + 路段行程時(shí)間�。� 參考：《浙江大學(xué)》2017年博士論文

【摘要】：作為描述道路交通狀態(tài)的一項(xiàng)重要參數(shù),行程時(shí)間的相關(guān)研究一直以來(lái)都是交通工程與交通科學(xué)領(lǐng)域的研究熱點(diǎn)。準(zhǔn)確、實(shí)時(shí)且可靠的行程時(shí)間信息,包括路段行程時(shí)間和路徑行程時(shí)間,是先進(jìn)的交通管理系統(tǒng)和先進(jìn)的出行者信息系統(tǒng)的重要支撐。然而交通需求的波動(dòng)性(如受季節(jié)效應(yīng)、人口特征、交通信息等的影響)、交通供給的波動(dòng)性(如受交通事件、道路施工、天氣因素、道路集合條件等的影響)以及信號(hào)控制交叉口車輛到達(dá)和離開的隨機(jī)性,導(dǎo)致城市道路路段行程時(shí)間在時(shí)間上、空間上以及不同方向上都具有較大的不確定性。因此,對(duì)于傳統(tǒng)的基于交通流模型的路段行程時(shí)間估計(jì)方法,其行程時(shí)間估計(jì)結(jié)果誤差較大,難以反應(yīng)實(shí)際道路交通狀態(tài)。隨著交通信息采集技術(shù)和處理技術(shù)的快速發(fā)展,交通數(shù)據(jù)在傳統(tǒng)的環(huán)形感應(yīng)線圈數(shù)據(jù)、微波雷達(dá)數(shù)據(jù)、紅外數(shù)據(jù)的基礎(chǔ)上,出現(xiàn)了浮動(dòng)車數(shù)據(jù)、自動(dòng)車輛識(shí)別數(shù)據(jù)、自動(dòng)車牌識(shí)別數(shù)據(jù)和藍(lán)牙數(shù)據(jù)等新型交通數(shù)據(jù)。其中基于高清智能卡口的自動(dòng)車牌識(shí)別數(shù)據(jù)包含有通過(guò)車輛的車牌號(hào)、通過(guò)時(shí)刻、進(jìn)口方向和進(jìn)口道編號(hào)等數(shù)據(jù),繼而可以獲得流量、單車行程時(shí)間及單車行駛方向等信息,而且高清智能卡口系統(tǒng)的布設(shè)日益廣泛。因此,本文利用基于高清智能卡口的自動(dòng)車牌識(shí)別數(shù)據(jù)對(duì)城市道路路段行程時(shí)間以及路徑行程時(shí)間進(jìn)行研究。首先對(duì)基于高清智能卡口的自動(dòng)車牌識(shí)別數(shù)據(jù)進(jìn)行數(shù)據(jù)質(zhì)量分析:介紹了高清卡口智能系統(tǒng)的工作原理、布設(shè)位置、檢測(cè)數(shù)據(jù)以及系統(tǒng)性能指標(biāo),在此基礎(chǔ)上展開了斷面數(shù)據(jù)質(zhì)量分析(包括流量精度和自動(dòng)車牌識(shí)別精度)和路段行程時(shí)間數(shù)據(jù)質(zhì)量分析。將封閉路段作為研究對(duì)象,通過(guò)分析路段行程時(shí)間估計(jì)結(jié)果(包括路段行程時(shí)間估計(jì)值和標(biāo)準(zhǔn)差的波動(dòng)性)與路段行程時(shí)間樣本率的變化關(guān)系,發(fā)現(xiàn)樣本率越大,路段行程時(shí)間估計(jì)值的平均絕對(duì)百分誤差越小,標(biāo)準(zhǔn)差的波動(dòng)性越小;當(dāng)樣本率大于0.414時(shí),基于樣本數(shù)據(jù)的路段行程時(shí)間參數(shù)滿足精度及穩(wěn)定性要求,繼而確定了路段行程時(shí)間的樣本率閾值。將非封閉路段作為研究對(duì)象,考慮路段開口,計(jì)算路段行程時(shí)間的實(shí)際匹配率,對(duì)其時(shí)空變化特征和顯著性差異進(jìn)行分析。實(shí)際數(shù)據(jù)表明天氣良好時(shí),行程時(shí)間的實(shí)際匹配率與觀測(cè)路段及觀測(cè)日期無(wú)關(guān),該值穩(wěn)定且均大于最小樣本率0.414;行程時(shí)間的實(shí)際匹配率與觀測(cè)時(shí)段有關(guān),20:00～6:00時(shí)段內(nèi)行程時(shí)間的實(shí)際匹配率相對(duì)于一天內(nèi)其它時(shí)段低,但仍大于最小樣本率;最終確定了天氣良好時(shí)高清智能卡口數(shù)據(jù)用于估計(jì)城市道路路段行程時(shí)間的可行性。其次,考慮交通流的不同方向,對(duì)路段行程時(shí)間進(jìn)行估計(jì):根據(jù)交通流在上游交叉口的駛?cè)敕较蚝驮谙掠谓徊婵诘鸟傠x方向,將路段交通流分為9種。受交通需求/供給的波動(dòng)性以及信號(hào)控制交叉口車輛到達(dá)和離開的隨機(jī)性等原因影響,同一路段上不同方向交通流的路段行程時(shí)間可能會(huì)有所不同,利用實(shí)際采集的自動(dòng)車牌識(shí)別數(shù)據(jù),對(duì)同一路段上不同方向交通流的路段行程時(shí)間進(jìn)行了一系列對(duì)比分析,驗(yàn)證了顯著性差異的存在;并融合行程時(shí)間回歸模型,提出了基于交通流方向的路段行程時(shí)間估計(jì)方法,實(shí)現(xiàn)了部分交通流數(shù)據(jù)缺失時(shí)的行程時(shí)間估計(jì);通過(guò)實(shí)際數(shù)據(jù)分析,驗(yàn)證了估計(jì)方法能夠有效地處理噪聲數(shù)據(jù),并且在數(shù)據(jù)缺失時(shí),估計(jì)結(jié)果能夠較為準(zhǔn)確地反映實(shí)際交通狀態(tài)。最后,基于路徑行程時(shí)間信息的分類和融合,提出了路徑行程時(shí)間分布的估計(jì)方法:根據(jù)路段交通流的定義對(duì)路徑進(jìn)行重新定義,并進(jìn)行觀測(cè)數(shù)據(jù)提取,實(shí)現(xiàn)部分無(wú)代表性數(shù)據(jù)的剔除;利用車輛行駛方向等信息對(duì)其路線進(jìn)行判別,而路線缺口較大的車輛,對(duì)其路線進(jìn)行拆分而非直接判別;在路線判別的基礎(chǔ)上,對(duì)部分路徑行程時(shí)間進(jìn)行擴(kuò)大,同樣忽略路徑缺口較大的車輛;根據(jù)行程時(shí)間的計(jì)算方式以及是否完整,將所有路徑下行程時(shí)間分為兩大類,完整的路徑行程時(shí)間(TTC)和部分路徑行程時(shí)間(TTP);不同類別的行程時(shí)間處理方法不同,當(dāng)TTC比例較高時(shí),將其經(jīng)驗(yàn)分布(TTCD)作為路徑行程時(shí)間分布的估計(jì)結(jié)果,當(dāng)TTC比例較低時(shí),利用霍普金斯統(tǒng)計(jì)量尋找實(shí)驗(yàn)路徑上的斷點(diǎn)交叉口,將各斷點(diǎn)之間部分路徑的行程時(shí)間分布的卷積作為基于TTP數(shù)據(jù)的行程時(shí)間分布(TTPD),并將TTC數(shù)據(jù)與基于TTP數(shù)據(jù)的行程時(shí)間分布TTPD進(jìn)行融合,得到路徑行程時(shí)間分布的估計(jì)結(jié)果;并利用實(shí)際路網(wǎng)和仿真環(huán)境下的256個(gè)實(shí)例,進(jìn)行了不同算法路徑行程時(shí)間估計(jì)結(jié)果誤差分析、識(shí)別精度的影響分析、參數(shù)mr的影響分析、路徑屬性的影響分析以及路徑行程時(shí)間估計(jì)結(jié)果分析,對(duì)本文路徑行程時(shí)間分布估計(jì)模型進(jìn)行了全面的評(píng)價(jià),驗(yàn)證了估計(jì)方法較其它方法的有效性。
[Abstract]:As an important parameter for describing road traffic state, the study of travel time has always been a hot spot in the field of traffic engineering and traffic science. Accurate, real-time and reliable travel time information, including road travel time and path travel time, is an advanced traffic management system and advanced traveler information system. Important support. However, the volatility of traffic demand (such as seasonal effects, demographic characteristics, traffic information, etc.), the volatility of traffic supply (such as the impact of traffic events, road construction, weather factors, road assembly conditions, etc.) and the randomness of the arrival and departure of the vehicles at the signal control intersection, resulting in the travel of the urban road section. Time is more uncertain in time, space and different directions. Therefore, for the traditional road travel time estimation method based on traffic flow model, the error of the travel time estimation result is large and it is difficult to respond to the actual road traffic state. With the rapid development of traffic information collection technology and processing technology, the traffic information acquisition technology and processing technology are developed. On the basis of the traditional loop induction coil data, microwave radar data, and infrared data, the datum appeared the floating car data, automatic vehicle identification data, automatic license plate recognition data and Bluetooth data. The automatic vehicle identification data based on high definition intelligent card port include the license plate number passing through the vehicle, through the vehicle license number, At the moment, the import direction and the import number and so on, then we can get the information of the traffic, the travel time and the direction of the single car. Moreover, the high definition intelligent card port system is widely distributed. Therefore, this paper uses the automatic license plate recognition data based on the high-definition intelligent card port for the travel time and the path travel time of the urban road section. Firstly, the data quality analysis of automatic license plate recognition data based on high definition intelligent card port is carried out. The working principle, layout position, detection data and system performance index of high definition card mouth intelligent system are introduced. On this basis, the quality analysis of cross section data (including flow accuracy and automatic license plate recognition accuracy) is developed. The link travel time data quality analysis. The closed section is taken as the research object. By analyzing the relationship between the estimate of the travel time of the section (including the estimated value of the travel time and the fluctuation of the standard deviation) and the sample rate of the section travel time, it is found that the larger the sample rate is, the more the average absolute percentage error of the estimated value of the road travel time is. When the sample rate is greater than 0.414, when the sample rate is greater than 0.414, the link travel time parameters based on the sample data meet the requirements of precision and stability. Then the sample rate threshold of the section travel time is determined. The non closed section is taken as the research object, and the actual matching rate of the section travel time is calculated and the time and space of the section travel time is calculated. The actual data show that the actual matching rate of the travel time is independent of the observed section and the observation date when the weather is good, and the value is stable and greater than the minimum sample rate of 0.414; the actual matching rate of the travel time is related to the observation period, and the actual matching rate of the travel time within the 20:00 to 6:00 period is relative. At the rest of the day, it is low, but still larger than the minimum sample rate. Finally, the feasibility of the high definition intelligent card data is used to estimate the travel time of the urban road section. Secondly, considering the different directions of the traffic flow, the travel time of the section is estimated: according to the direction of the traffic flow in the upstream intersection and the downstream. The direction of departure of the intersection is divided into 9 kinds of traffic flow, which are influenced by the fluctuation of traffic demand / supply and the randomness of the arrival and departure of the vehicle at the intersection. The travel time of the traffic flow in different directions on the same section may be different. A series of contrasts and analyses on the travel time of traffic flow in different directions on the road have been carried out to verify the existence of significant difference, and the estimation method of road travel time based on the direction of traffic flow is put forward by combining the travel time regression model, and the estimation of the travel time of some traffic flow data is realized, and the analysis of the time of the travel time is realized by the actual data analysis. It is proved that the estimation method can effectively deal with the noise data, and the estimation results can reflect the actual traffic state more accurately when the data is missing. Finally, based on the classification and fusion of path travel time information, the estimation method of path travel time distribution is proposed. The path is redefined according to the definition of the road traffic flow. It also carries out the extraction of the observation data, realizes the elimination of some non representative data, uses the vehicle direction and other information to distinguish its route, and the vehicle with the larger path gap is divided instead of directly judging the route. On the basis of the route discrimination, the route travel time is enlarged and the path gap is ignored. Large vehicles; according to the way of calculation and completeness of travel time, the travel time of all paths is divided into two categories, complete path travel time (TTC) and partial path travel time (TTP); different types of travel time processing methods are different, and when the proportion of TTC is high, their experience distribution (TTCD) is used as path travel time distribution. When the ratio of TTC is low, the Hopki statistic is used to find the intersection of the breakpoint on the experimental path, and the convolution of the travel time distribution of the partial path between the breakpoints is used as the travel time distribution (TTPD) based on the TTP data, and the TTC data is fused with the travel time distribution based on the TTP data, and the path is obtained. The estimated result of the travel time distribution, and using 256 examples under the actual road network and the simulation environment, the error analysis of the path travel time estimation results of different algorithms, the influence analysis of the recognition accuracy, the influence analysis of the parameter Mr, the influence analysis of the path attribute and the analysis of the path travel time estimation results are made. A comprehensive evaluation of the inter estimation model is carried out to verify the effectiveness of the estimation method compared with other methods.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491

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