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Modelling and accessing trajectory data of moving vehicles in a road network.

機(jī)譯：在道路網(wǎng)絡(luò)中建模和訪問行駛中的車輛的軌跡數(shù)據(jù)。

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Trajectory data of moving vehicles becomes an important supplement to conventional traffic data. Using trajectory data to solve traffic problems is the background of this research. This thesis specifically deals with the management of trajectory data and seeks answers to the research question, "how to efficiently represent and access trajectory data." Correspondingly, the objectives of this research are to develop a trajectory data model and to develop a trajectory data access method. To fulfill these objectives, a LRS-based trajectory data model (LTDM) and a topology-based mixed index structure (TMIS) are developed. Given the importance of road networks, a trajectory-oriented, carriageway-based road network data model (CRNM) is also developed to provide the foundation for LTDM and TMIS. In order to integrate the developed approaches into the solution of real traffic problems, a tentative framework for trajectory data applications, namely, cooperative intelligent transportation system (CITS), is set up. Major contributions of this thesis consist of the following four points.; First, the CRNM provides network-based spatial references for location points of trajectory data. Based on the CRNM, the LTDM adopts a novel approach to select key points from location points. An experimental test shows that these models have a better performance than existing ones. These models, as extensions of geographic representation in the spatio-temporal domain, also compensate for the lack of capability of Geographical Information System for Transportation (GIS-T) to handle dynamic traffic features, e.g., moving vehicles.; Second, since the TMIS is based on the LTDM and CRNM, the number of spatial dimensions of trajectory data is decreased, which effectively reduces the complicacy of index structures. The TMIS consists of a number of small and classical index structures (e.g. R-tree and B-tree) instead of a huge and complicated index structure. These small index structures are linked by network topology. It is easy to implement and maintain the TMIS, and with different combinations of these small index structures, the TMIS can support more spatio-temporal query types of trajectory data than existing access methods. These ideas employed by the TMIS hopefully open a new horizon in building index structures of network-based trajectory data.; Third, the CITS, though still a conceptual framework at the current stage, can develop in a benign circle if being realized and can facilitate traffic management to a great extent. Especially, the proposed models and methods can be integrated into the framework and can provide the foundation for advanced applications, such as travel behavior analysis based on trajectory data mining.; Fourth, in order to avoid confusion, some concepts, including spatial attribute, aspatial attribute, spatio-temporal object (STO), point STO, region STO, spatio-temporal queries, etc., are redefined or extended into the spatio-temporal domain. An event-state analysis method is also developed to illustrate how the value of an attribute changes over time. These concepts and method may provide the foundation for relevant research in the future.
機(jī)譯：移動(dòng)車輛的軌跡數(shù)據(jù)成為傳統(tǒng)交通數(shù)據(jù)的重要補(bǔ)充。利用軌跡數(shù)據(jù)解決交通問題是本研究的背景。本文專門研究了軌跡數(shù)據(jù)的管理，并尋求研究問題“如何有效表示和訪問軌跡數(shù)據(jù)”的答案。相應(yīng)地，本研究的目的是開發(fā)一個(gè)軌跡數(shù)據(jù)模型并開發(fā)一種軌跡數(shù)據(jù)訪問方法。為了實(shí)現(xiàn)這些目標(biāo)，開發(fā)了基于LRS的軌跡數(shù)據(jù)模型（LTDM）和基于拓?fù)涞幕旌纤饕Y(jié)構(gòu)（TMIS）?？紤]到道路網(wǎng)絡(luò)的重要性，還開發(fā)了基于軌跡的，基于行車道的道路網(wǎng)絡(luò)數(shù)據(jù)模型（CRNM），以為LTDM和TMIS提供基礎(chǔ)。為了將開發(fā)的方法集成到實(shí)際交通問題的解決方案中，建立了用于軌跡數(shù)據(jù)應(yīng)用的暫定框架，即協(xié)作智能運(yùn)輸系統(tǒng)（CITS）。本論文的主要貢獻(xiàn)包括以下四個(gè)方面。首先，CRNM為軌跡數(shù)據(jù)的位置點(diǎn)提供基于網(wǎng)絡(luò)的空間參考。 LTDM基于CRNM，采用一種新穎的方法從位置點(diǎn)中選擇關(guān)鍵點(diǎn)。實(shí)驗(yàn)測試表明，這些模型比現(xiàn)有模型具有更好的性能。這些模型作為時(shí)空域中地理表示的擴(kuò)展，也彌補(bǔ)了運(yùn)輸?shù)乩硇畔⑾到y(tǒng)（GIS-T）處理動(dòng)態(tài)交通特征（如移動(dòng)車輛）的能力不足的問題。其次，由于TMIS基于LTDM和CRNM，因此減少了軌跡數(shù)據(jù)的空間維數(shù)，有效降低了索引結(jié)構(gòu)的復(fù)雜性。 TMIS由許多小型經(jīng)典索引結(jié)構(gòu)（例如R樹和B樹）組成，而不是龐大而復(fù)雜的索引結(jié)構(gòu)。這些小的索引結(jié)構(gòu)通過網(wǎng)絡(luò)拓?fù)滏溄印?TMIS易于實(shí)現(xiàn)和維護(hù)，并且通過這些小索引結(jié)構(gòu)的不同組合，與現(xiàn)有訪問方法相比，TMIS可以支持更多的時(shí)空查詢類型的軌跡數(shù)據(jù)。 TMIS所采用的這些思想有望為建立基于網(wǎng)絡(luò)的軌跡數(shù)據(jù)的索引結(jié)構(gòu)開辟新的視野。第三，CITS雖然在現(xiàn)階段仍是一個(gè)概念框架，但如果能夠?qū)崿F(xiàn)，則可以良性發(fā)展，并在很大程度上促進(jìn)交通管理。特別是，所提出的模型和方法可以集成到框架中，并且可以為高級(jí)應(yīng)用提供基礎(chǔ)，例如基于軌跡數(shù)據(jù)挖掘的旅行行為分析。第四，為了避免混淆，一些概念被重新定義或擴(kuò)展到時(shí)空域，這些概念包括空間屬性，空間屬性，時(shí)空對(duì)象（STO），點(diǎn)STO，區(qū)域STO，時(shí)空查詢等。 。還開發(fā)了一種事件狀態(tài)分析方法來說明屬性值如何隨時(shí)間變化。這些概念和方法可以為將來的相關(guān)研究提供基礎(chǔ)。

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