【摘要】：導(dǎo)航系統(tǒng)對(duì)于解決城市交通擁堵,緩解交通壓力具有重要意義,而結(jié)合路況的路徑通行時(shí)間估計(jì)是導(dǎo)航中的基礎(chǔ)和關(guān)鍵。隨著車輛軌跡數(shù)據(jù)的大量積累,使得估計(jì)動(dòng)態(tài)路網(wǎng)中路徑的通行時(shí)間變?yōu)榭赡?即針對(duì)給定的起點(diǎn)和終點(diǎn),對(duì)不同路徑的通行時(shí)間進(jìn)行預(yù)測(cè),從而找出通行時(shí)間最短的路徑。然而通行時(shí)間最短的路徑并不一定是累積概率分布最大的路徑。當(dāng)用戶需要在指定時(shí)刻前抵達(dá)的時(shí)候,獲得累積概率分布最大的路徑就能發(fā)揮很大的作用。為了找出動(dòng)態(tài)路網(wǎng)中累積分布最大的路徑,就需要對(duì)路徑通行時(shí)間的概率進(jìn)行估計(jì),而非僅得到一個(gè)單一值的估計(jì)結(jié)果。在現(xiàn)有的研究方案中,研究人員將整個(gè)道路網(wǎng)劃分為以路段為基本單位的網(wǎng)絡(luò)結(jié)構(gòu),基于路段對(duì)路徑的通行時(shí)間進(jìn)行估計(jì),然而這種依賴于路段組合的方式忽略了完整路徑通行中十字路口的拐彎時(shí)間和紅綠燈的等候時(shí)間等,導(dǎo)致路徑較長(zhǎng)時(shí)估計(jì)結(jié)果更不準(zhǔn)確。與基于路段的研究方案不同,為了提高估計(jì)的準(zhǔn)確度和效率,本文提出基于子路徑的路徑通行時(shí)間估計(jì)方案。為了提高估計(jì)的效率,本文利用歷史軌跡數(shù)據(jù)建立后綴索引樹的存儲(chǔ)結(jié)構(gòu),將實(shí)時(shí)獲取的軌跡通行時(shí)間存儲(chǔ)在后綴索引樹的節(jié)點(diǎn)上,對(duì)于數(shù)據(jù)稀疏的子路徑,由歷史數(shù)據(jù)提供通行時(shí)間的結(jié)果,通過(guò)這種存儲(chǔ)結(jié)構(gòu)可以快速地獲取查詢路徑的子路徑序列,及其相應(yīng)的軌跡通行時(shí)間。為了提高估計(jì)的準(zhǔn)確性,本文對(duì)子路徑序列采用線性插值算法和基于時(shí)空相關(guān)性的預(yù)測(cè)算法對(duì)其通行時(shí)間的概率估計(jì)進(jìn)行驗(yàn)證,并采用2016年及2017年1月哈爾濱市出租車的軌跡數(shù)據(jù)集驗(yàn)證了算法的準(zhǔn)確性和效率。
[Abstract]:Navigation system plays an important role in solving urban traffic congestion and relieving traffic pressure, and the estimation of road passage time combined with road condition is the basis and key of navigation. With the accumulation of vehicle track data, it is possible to estimate the passage time of the path in the dynamic road network, that is, to predict the passage time of different paths according to the given starting point and the end point, so as to find out the shortest path. However, the shortest path is not always the path with the largest cumulative probability distribution. When the user needs to arrive before the specified time, the path with the largest cumulative probability distribution can play a significant role. In order to find the path with the largest cumulative distribution in the dynamic road network, it is necessary to estimate the probability of the passage time of the path, rather than to get the result of a single value. In the existing research scheme, the researchers divide the whole road network into a network structure based on the road section, and estimate the passage time based on the road section. However, in this way, the intersection time and the waiting time of the traffic lights in the complete path are ignored, which leads to the inaccurate estimation results when the path is longer. In order to improve the accuracy and efficiency of the estimation, a subpath-based approach is proposed to estimate the passage time. In order to improve the efficiency of the estimation, the storage structure of the suffix index tree is established by using the historical track data, and the track passage time obtained in real time is stored on the node of the suffix index tree. By using the historical data to provide the result of the passage time, the subpath sequence of the query path and the corresponding path passage time can be obtained quickly by this storage structure. In order to improve the accuracy of the estimation, the linear interpolation algorithm and the prediction algorithm based on spatio-temporal correlation are used to verify the probability estimation of the passage time of the subpath sequence. The accuracy and efficiency of the algorithm are verified by using the track data set of Harbin taxis in 2016 and 2017.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U495

【參考文獻(xiàn)】

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1 趙新正;李夢(mèng)雪;李秋平;李同f;芮e，

本文編號(hào)：2351235