本文選題：車輛軌跡數(shù)據(jù) + 車輛行程�。� 參考：《華東師范大學》2017年博士論文

【摘要】：近年來,隨著中國城市化水平的不斷提高,機動車數(shù)量有加速增長的趨勢,而道路基礎(chǔ)設(shè)施的建設(shè)卻相對緩慢,城市交通的供需矛盾日益加劇,在很多大中型城市中,交通擁堵正在逐漸成為常態(tài)。在這種情況下,出行者的旅行時間復雜多變,出行成本越來越高。如何準確預測未來的旅行時間,對于出行者和交通管理者,都是一個具有重大現(xiàn)實意義的問題。此時,智能交通系統(tǒng)(intelligent transportation system,ITS)的日益成熟和各種海量車輛軌跡數(shù)據(jù)的出現(xiàn)給旅行時間預測的研究帶來了巨大的機遇。在這種背景下,本文希望以海量車輛軌跡數(shù)據(jù)為數(shù)據(jù)支撐,對高度城市化地區(qū)的旅行時間(travle time)預測問題進行探索。雖然車輛軌跡數(shù)據(jù)能提供完整時空覆蓋的交通信息,但其海量性也給數(shù)據(jù)的維護和檢索帶來困難。另外,旅行時間預測的準確度不僅依賴于預測模型的性能,也受限于數(shù)據(jù)本身的復雜性。因此,在進行旅行時間預測研究之前,還需要從數(shù)據(jù)質(zhì)量的角度來研究歷史旅行時間序列的復雜度,對歷史旅行時間序列的可預測性進行評價�；诖�,針對海量車輛軌跡數(shù)據(jù)的旅行時間預測研究應涵蓋軌跡數(shù)據(jù)建模與索引、旅行時間可預測性分析、以及旅行時間預測的實現(xiàn)等多方面內(nèi)容�；谛谐痰能壽E數(shù)據(jù)模型能給車輛軌跡數(shù)據(jù)提供便于管理的組織形式,而針對車輛行程的索引結(jié)構(gòu)能明顯改善檢索效率,為行程信息的獲取提供可行的解決方案;在高效獲取行程數(shù)據(jù)的基礎(chǔ)上進行歷史旅行時間序列的可預測性分析,是對預測數(shù)據(jù)的檢驗和評價,為旅行時間的預測提供保障;而考慮影響交通的各種條件去設(shè)計預測模型實現(xiàn)旅行時間預測則是本文最終的研究目的�；诖�,本文針對車輛軌跡數(shù)據(jù)建模、車輛軌跡數(shù)據(jù)索引、旅行時間可預測性和旅行時間預測模型等四方面展開研究。在車輛軌跡數(shù)據(jù)建模方面,本文使用基于車輛行程的軌跡數(shù)據(jù)模型來組織車輛軌跡數(shù)據(jù),并根據(jù)基于車流方向的道路網(wǎng)絡(luò)模型提出了基于道路拓撲的軌跡數(shù)據(jù)地圖匹配算法。為了獲取用于旅行時間預測的行程數(shù)據(jù),本文定義"車輛行程"(vehicle-based trip)來表達出行者的一次出行經(jīng)歷,并以其為邏輯單位組織車輛軌跡數(shù)據(jù),通過軌跡提取、地圖匹配、行程劃分等步驟來實現(xiàn)軌跡數(shù)據(jù)的建模。其中,針對一般道路與快速路系統(tǒng)并存的復雜城市道路網(wǎng)絡(luò),本文提出了一種基于道路拓撲的軌跡數(shù)據(jù)地圖匹配算法,該方法通過最短距離法篩選軌跡的備選路段集;然后進行軌跡分段,消除軌跡中的環(huán)形結(jié)構(gòu);接著對每個軌跡段利用有向路段的拓撲關(guān)系選取匹配路徑,實現(xiàn)軌跡數(shù)據(jù)在復雜道路網(wǎng)絡(luò)中的地圖匹配。實證研究描述了數(shù)據(jù)建模的整個過程,不僅證實了本文使用的路網(wǎng)模型和軌跡數(shù)據(jù)模型的可用性,還對軌跡數(shù)據(jù)建模的性能進行了分析。針對海量車輛軌跡數(shù)據(jù)的高效存取問題,本文提出了一種面向行程的車輛軌跡數(shù)據(jù)索引方案——TripCube,它使用三維的時空索引立方體維護車輛行程數(shù)據(jù),并根據(jù)車輛行程的起止點和出發(fā)時間來快速檢索車輛行程信息。與通用索引結(jié)構(gòu)的多組性能對比實驗表明,TripCube結(jié)構(gòu)簡單、易于維護,且對車輛軌跡數(shù)據(jù)的存取效率遠遠優(yōu)于通用的索引結(jié)構(gòu)。接著,本文討論了歷史旅行時序列的復雜度對旅行時間預測的影響。在分析歷史旅行時間序列復雜性的基礎(chǔ)上,把"旅行時間可預測性"(travel time predictability)定義為使用歷史旅行時間序列正確預測旅行時間的概率,并提出一個基于熵的方法去測量旅行時間可預測性的最大值。首先,使用多尺度熵(Multiscale Entropy,MSE)的改進算法——RCMSE(the refined composite multiscale entropy algorithm)計算不同時間尺度下旅行時間序列的復雜度;然后,關(guān)聯(lián)旅行時間序列的熵和序列的旅行時間可預測性最大值,求解旅行時間可預測性的最大值。實證研究分析了時間尺度、容差和序列長度等因素對旅行時間序列的熵和旅行時間可預測性的影響,討論了旅行時間預測的精度,還進行了旅行時間可預測性與實際預測結(jié)果的對比實驗。實驗結(jié)果證實了本文提出的旅行時間可預測性的有用性及其計算方法的可靠性。在上述研究的基礎(chǔ)上,本文提出了面向行程的旅行時間預測模型。在出行者更關(guān)注特定起止點行程的旅行時間的背景下,本文使用基于反向傳播神經(jīng)網(wǎng)絡(luò)模型的預測方法,充分考慮多種影響旅行時間的因素(出行時間、天氣條件、空氣質(zhì)量等),實現(xiàn)城市道路網(wǎng)絡(luò)中任意起止點間的面向行程的旅行時間預測。實證研究使用13個月的海量出租車數(shù)據(jù)進行,其中12個月的數(shù)據(jù)用于訓練模型,1個月的數(shù)據(jù)用于驗證預測結(jié)果。實驗結(jié)果證實了本文提出的預測模型的有效性和準確性,同時也表達了氣象條件對旅行時間的影響是不可忽略的。最后,對上述研究成果進行總結(jié),明確了本文研究的主要貢獻和局限性,并對未來進一步的研究工作進行了展望。
[Abstract]:In recent years, with the continuous improvement of the level of urbanization in China, the number of motor vehicles has increased rapidly, but the construction of road infrastructure is relatively slow, the contradiction between supply and demand of urban traffic is increasing. In many large and medium-sized cities, traffic congestion is becoming normal. In this case, the traveler's travel time is complex and changeable. The travel cost is getting higher and higher. How to accurately predict the future travel time is a significant problem for both the traveler and the traffic manager. At this time, the growing maturity of intelligent transportation system (ITS) and the study of the occurrence of a variety of mass vehicle trajectory data to the travel time prediction In this context, this paper hopes to explore the problem of travle time prediction in highly urbanized areas with massive vehicle trajectory data as data support. Although vehicle trajectory data can provide traffic information with a complete and space-time coverage, the mass character also brings difficulties to the maintenance and retrieval of data. In addition, the accuracy of travel time prediction depends not only on the performance of the prediction model, but also on the complexity of the data itself. Therefore, the complexity of the history travel time series needs to be studied from the point of view of the data quality before the travel time prediction research, and the predictability of the history travel time series is evaluated. In this case, the travel time prediction for mass vehicle trajectory data should cover the modeling and index of the trajectory data, the predictability of travel time, and the realization of the travel time prediction. The trajectory data model based on the travel can provide a convenient management organization for the vehicle trajectory data, and for the vehicle travel. The index structure can obviously improve the retrieval efficiency and provide a feasible solution for the acquisition of travel information. On the basis of efficient acquisition of travel data, the predictability analysis of the history travel time series is the test and evaluation of the forecast data and the guarantee for the travel time prediction; and the various conditions that affect the traffic are considered. The purpose of this paper is to study the four aspects of vehicle trajectory data modeling, vehicle trajectory data index, travel time predictability and travel time prediction model. In the aspect of vehicle trajectory data modeling, this paper uses the number of trajectories based on vehicle travel. According to the model, vehicle trajectory data are organized, and a path map matching algorithm based on road topology is proposed based on road network model based on the direction of traffic flow. In order to obtain travel time prediction, this paper defines "vehicle travel" (vehicle-based trip) to express a traveler's trip experience and take it as logic. In this paper, a track data map matching algorithm based on road topology is proposed in this paper. In this paper, a path map matching algorithm based on road topology is proposed. The selected section of the path is selected, then the trajectory is segmented to eliminate the ring structure in the trajectory. Then, the matching path is selected for each track segment using the topology of the directed section to realize the map matching in the complex road network. The empirical study describes the whole process of modeling the data, which not only confirms the use of this paper. The availability of road network model and trajectory data model is also analyzed, and the performance of trajectory data modeling is also analyzed. In view of the efficient access problem of mass vehicle trajectory data, this paper proposes a travel oriented vehicle track data index scheme, TripCube, which uses a three-dimensional spatio-temporal index cube to maintain vehicle travel data, and The multi group performance comparison experiment with the general index structure shows that the TripCube structure is simple and easy to maintain, and the access efficiency of the vehicle trajectory data is far superior to the general index structure. Then, this paper discusses the complexity of the history travel time sequence. The influence of travel time prediction. On the basis of analyzing the complexity of historical travel time series, "travel time predictability" is defined as the probability of using historical travel time sequence to predict the travel time correctly, and an entropy based square method is proposed to measure the maximum predictability of travel time. First, An improved algorithm of Multiscale Entropy (MSE) - RCMSE (the refined composite multiscale entropy algorithm) is used to calculate the complexity of the travel time series at different time scales; then, the maximum predictability of the entropy and the travel time of the sequence of associated travel time series is calculated, and the most predictability of travel time is solved. The empirical study analyses the influence of time scale, tolerance and sequence length on the predictability of travel time sequence entropy and travel time. The accuracy of travel time prediction is discussed, and the comparison experiment between travel time predictability and actual prediction results is carried out. The test results confirm the travel time proposed in this paper. On the basis of the above study, the travel time prediction model of travel oriented is proposed on the basis of the above research. In the background of the traveler's more attention to the travel time of the specific stop point travel, this paper uses the prediction method based on the back propagation neural network model to take full consideration of many kinds of travel time. The factors (travel time, weather condition, air quality, etc.) are used to predict the travel time of travel time between any starting points in the urban road network. The empirical study uses 13 months of mass taxi data, of which 12 months of data are used for training model, and the data of 1 months are used to verify the prediction results. Experimental results confirm the results. The validity and accuracy of the prediction model presented in this paper also show that the influence of weather conditions on travel time can not be ignored. Finally, the above research results are summarized, the main contributions and limitations of the study are clarified, and the further research work in the future is prospected.
【學位授予單位】：華東師范大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：U491.14

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