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MASTER: A multiple aspect view on trajectories 总被引：1，自引：0，他引：1
Ronaldo dos Santos Mello Vania Bogorny Luis Otavio Alvares Luiz Henrique Zambom Santana Carlos Andres Ferrero Angelo Augusto Frozza Geomar Andre Schreiner Chiara Renso 《Transactions in GIS》2019,23(4):805-822
For many years trajectory data have been treated as sequences of space‐time points or stops and moves. However, with the explosion of the Internet of Things and the flood of big data generated on the Internet, such as weather channels and social network interactions, which can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions. The main challenge is how to integrate all this information with trajectories. In this article we introduce a new concept of trajectory, called multiple aspect trajectory, propose a robust conceptual and logical data model that supports a vast range of applications, and, differently from state‐of‐the‐art methods, we propose a storage solution for efficient multiple aspect trajectory queries. The main strength of our data model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario and compare its query performance against the state‐of‐the‐art spatio‐temporal database SECONDO extension for symbolic trajectories. 相似文献
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Andre Salvaro Furtado Despina Kopanaki Luis Otavio Alvares Vania Bogorny 《Transactions in GIS》2016,20(2):280-298
Most existing approaches aiming at measuring trajectory similarity are focused on two‐dimensional sequences of points, called raw trajectories. However, recent proposals have used background geographic information and social media data to enrich these trajectories with a semantic dimension, giving rise to the concept of semantic trajectories. Only a few works have proposed similarity measures for semantic trajectories or multidimensional sequences, having limitations such as predefined weight of the dimensions, sensitivity to noise, tolerance for gaps with different sizes, and the prevalence of the worst dimension similarity. In this article we propose MSM, a novel similarity measure for multidimensional sequences that overcomes the aforementioned limitations by considering and weighting the similarity in all dimensions. MSM is evaluated through an extensive experimental study that, based on a seed trajectory, creates sets of semantic trajectories with controlled transformations to introduce different kinds and levels of dissimilarity. For each set, we compute the similarity between the seed and the transformed trajectories, using different measures. The results showed that MSM was more robust and efficient than related approaches in the domain of semantic trajectories. 相似文献
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Lucas May Petry Carlos Andres Ferrero Luis Otavio Alvares Chiara Renso Vania Bogorny 《Transactions in GIS》2019,23(5):960-975
The large amount of semantically rich mobility data becoming available in the era of big data has led to a need for new trajectory similarity measures. In the context of multiple‐aspect trajectories, where mobility data are enriched with several semantic dimensions, current state‐of‐the‐art approaches present some limitations concerning the relationships between attributes and their semantics. Existing works are either too strict, requiring a match on all attributes, or too flexible, considering all attributes as independent. In this article we propose MUITAS, a novel similarity measure for a new type of trajectory data with heterogeneous semantic dimensions, which takes into account the semantic relationship between attributes, thus filling the gap of the current trajectory similarity methods. We evaluate MUITAS over two real datasets of multiple‐aspect social media and GPS trajectories. With precision at recall and clustering techniques, we show that MUITAS is the most robust measure for multiple‐aspect trajectories. 相似文献
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This article studies the analysis of moving object data collected by location‐aware devices, such as GPS, using graph databases. Such raw trajectories can be transformed into so‐called semantic trajectories, which are sequences of stops that occur at “places of interest.” Trajectory data analysis can be enriched if spatial and non‐spatial contextual data associated with the moving objects are taken into account, and aggregation of trajectory data can reveal hidden patterns within such data. When trajectory data are stored in relational databases, there is an “impedance mismatch” between the representation and storage models. Graphs in which the nodes and edges are annotated with properties are gaining increasing interest to model a variety of networks. Therefore, this article proposes the use of graph databases (Neo4j in this case) to represent and store trajectory data, which can thus be analyzed at different aggregation levels using graph query languages (Cypher, for Neo4j). Through a real‐world public data case study, the article shows that trajectory queries are expressed more naturally on the graph‐based representation than over the relational alternative, and perform better in many typical cases. 相似文献
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Vania Bogorny Hercules Avancini Bruno Cesar de Paula Cassiano Rocha Kuplich Luis Otavio Alvares 《Transactions in GIS》2011,15(2):227-248
Enormous quantities of trajectory data are collected from many sources, such as GPS devices and mobile phones, as sequences of spatio‐temporal points. These data can be used in many application domains such as traffic management, urban planning, tourism, bird migration, and so on. Raw trajectory data, as generated by mobile devices have very little or no semantics, and in most applications a higher level of abstraction is needed to exploit these data for decision making. Although several different methods have been proposed so far for trajectory querying and mining, there are no software tools to help the end user with semantic trajectory data analysis. In this article we present a software architecture for semantic trajectory data mining as well as the first software prototype to enrich trajectory data with both semantic information and data mining. As a prototype we extend the Weka data mining toolkit with the module Weka‐STPM, which is interoperable with databases constructed under OGC specifications. We tested Weka‐STPM with real geographic databases, and trajectory data stored under the Postgresql/PostGIS DBMS. 相似文献
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Yujia Xie Meizhen Wang Xuejun Liu Ziran Wang Bo Mao Feiyue Wang Xiaozhi Wang 《Transactions in GIS》2021,25(1):450-467
Current studies on video trajectory retrieval focus on the retrieval and analysis of image content, neglecting the gap between the spatiotemporal continuity of retrieval conditions and the spatiotemporal discontinuity of multi‐camera video trajectories. In this study, we propose a method for the spatiotemporal retrieval of dynamic video object trajectories in geographic scenes. Based on the camera calibration, the proposed method organizes the scene, cameras, and trajectories, constructs the spatiotemporal constraints, and queries the trajectories using two measures: camera‐by‐camera retrieval and global trajectory retrieval. The proposed method was verified through experiments, and the results demonstrate that both measures can query trajectories effectively and reduce the spatiotemporal video review range under different spatiotemporal constraints. Furthermore, compared with camera‐by‐camera retrieval, global trajectory retrieval can reduce the spatiotemporal video review range further and return more accurate results. The proposed method may provide support for the spatial analysis and understanding of surveillance video data. 相似文献
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Trajectory similarity measurement is a basic and vital task in trajectory data mining, which has attracted extensive research in the past decades. Recent works focused on the sequence and hierarchy property of trajectories to construct similarity measurements. However, these methods ignore the user information on the visiting locations, such as semantic and time distribution. In light of this, a novel trajectory similarity measurement based on Node-Sequence Hierarchical Digraph (NSHD) framework is proposed in this article. We first propose a Time-Weighted Stay Point Detection (TWSPD) method to extract real visiting locations of users more accurately. Then, the nodes of digraph are obtained by clustering users' stay points and the edges of digraph are sequence information that users move between these nodes. An Advanced Earth Mover's Distance (AEMD) is proposed to measure the node similarity between users, considering visiting time distribution and semantic information simultaneously. Both node and sequence similarities are used to calculate the similarity score to obtain the final trajectory similarity measurement. Experiments on Geolife and T-Drive datasets show that our proposed method offers competitive performance with mean reciprocal rank values reaching 96.01 and 81.26%, which outperforms related trajectory similarity measurements by more than 10 and 15%. 相似文献
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Mobile user identification aims at matching different mobile devices of the same user using trajectory data, which has attracted extensive research in recent years. Most of the previous work extracted trajectory features based on regular grids, which will lead to incorrect feature representation due to lack of geographic information. Besides, most trajectory similarity models only considered one single distance measure to calculate the similarity between users, which ignore the connection between different distance measures and may lead to some false matches. In light of this, we present a novel user identification method based on road networks and multiple distance measures in this article. The proposed method segments a city map into several grids and road segments based on road networks. Then it extracts location and road information of trajectories to jointly construct user features. Multiple distance measures are fused by a discriminant model to improve the effect of user identification. Experiments on real GPS trajectory datasets show that our proposed method outperforms related similarity measure methods and is stable for mobile user identification. Meanwhile, our method can also achieve good identification results even on sparse trajectory datasets. 相似文献
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由于数据传输和存储成本的限制,大多数轨迹数据采样率低且不确定,而城市精细模型往往需要高频轨迹数据,例如,微观交通碳排模型需要时间间隔为1 s的轨迹数据。因此,对低频轨迹数据进行高频重构有非常重要的意义。提出了一种顾及交叉路口和车辆模态的轨迹重构方法,采用高频轨迹数据训练车辆运动模态的理论概率模型,结合交叉路口来确定低频轨迹点之间的模态序列,并通过遗传算法求解理论概率模型来完成各模态时间和距离的分配,进而完成轨迹点的高频重构。结果表明,所提方法重构轨迹的均方根误差（root mean square error,RMSE）值相较于传统的数学插值方法降低了62.9%,相较于未考虑交叉路口的模态方法,降低了12.2%。因此,该方法在低频轨迹数据重构中具有很好的应用价值。 相似文献
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轨迹数据处理与分析是目前空间信息和数据库等相关领域的研究热点之一。本文从Stop-Move轨迹模型出发,通过集成地理空间上下文信息来建模轨迹数据,并研究轨迹时空模式的查询处理技术。首先分析Stop/Move对象与点/线/面地理空间要素之间的时空关联关系,据此提出显式表达该关联语义的地理关联轨迹模型,在此基础上利用关系-对象数据库技术,为地理关联轨迹模型设计独立于应用的关系模式,接着定义轨迹时空模式查询,并提出基于地理关联轨迹关系模式的SQL处理框架,最后以典型性检索请求为例,讨论分析位置-时间、位置-顺序和位置-关系等三类轨迹时空模式查询的纯SQL处理技术,并以样例轨迹数据验证了本文方法的可行性。 相似文献
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为识别城市交通中的频繁路径,本文提出了一种出租车轨迹数据的频繁轨迹识别方法。该方法首先对轨迹数据进行轨迹压缩,以降低计算复杂度;然后基于最长公共子序列和动态时间规整算法进行轨迹相似性度量计算,利用计算得到的轨迹间相似度生成距离矩阵;最后将生成的距离矩阵结合HDBSCAN算法进行聚类得到频繁轨迹。选取厦门岛内两个区域进行试验分析,结果表明,该方法能够识别出轨迹数据集中的频繁轨迹,进而得到城市区域之间通行的频繁路径,对道路规划、路径优化与推荐、交通治理等应用提供帮助。 相似文献
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Geographic services based on GPS trajectory data, such as location prediction and recommender services, have received increasing attention because of their potential social and commercial benefits. In this study, a Geographic Service Recommender Model (GSRM) is proposed, which loosely comprises three essential steps. Firstly, location sequences are obtained through a clustering operation on GPS locations. To improve efficiency, a programming model with a distributed algorithm is employed to accelerate the clustering. Secondly, in order to mine spatial and temporal information from the cluster trajectory, an algorithm (MiningMP) is designed. Last but not least, the next possible location to which the user will travel is predicted. An integrated framework of GSRM could then be constructed and provide the appropriate geographic recommendation service by considering location sequences as well as other related semantic information. Experiments were conducted based on real GPS trajectories from Microsoft Research Asia (182 users within a period of five years). The experimental results clearly demonstrate that our proposed GSRM model is effective and efficient at predicting locations and can provide users with personalized smart recommendation services in the following possible position with excellent performance in scalability, adaptability, and quality of service. 相似文献
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针对人工监测无法及时、高效地发现视频中车辆和行人违规情况的不足,构建了地理空间视角下,兼顾动态目标在地理场景下的属性、时空关系和语义信息的轨迹模型(trajectory model,Tra-Model),设计了视频与GIS协同的交通违规行为分析方法,基于轨迹与规则几何约束条件对目标逆行、压线、禁止进入3类违规情况进行实时检测。以某高校为实验场地,分析不同交通场景下目标的3类违规情况。实验结果表明：（1）Tra-Model模型提取的轨迹包含目标类型、轨迹序列等语义信息,相比于现有动态目标跟踪算法,精准率提高15.6%;(2)所提方法对多个摄像机序列违规分析准确率均在70%以上,相对于现有方法具有更好的性能;（3）实现了地理场景下多种交通违规行为综合分析的全局性、高精度和探测类型多样性。 相似文献
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Zhixian Yan Jose Macedo Christine Parent Stefano Spaccapietra 《Transactions in GIS》2008,12(Z1):75-91
Many real world applications today are built on analyses of movement and related features. Examples of such applications include transportation management, urban planning, tourism services, and animal migration monitoring, among others. Recent database modeling and management research prototypes have the capability to store and manipulate movement data in terms of point or region geometries that evolve over time (moving point or moving and deforming region). This captures the spatio‐temporal trace left by a moving object, but ignores its links with non‐geometric information that enable a semantic interpretation of the movement of moving objects. The concept of trajectory has been introduced to express a more semantic understanding of movement, taking it closer to the perception of applications. This article describes a framework for a semantics‐oriented structuring, modeling and querying of trajectory data. The framework relies on the definition of trajectory‐related ontologies, addressing domain‐independent and application‐specific geometric and semantic facets. Last we briefly discuss how the proposed approach has been applied for a traffic management application. 相似文献
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Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint 
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下载免费PDF全文 With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead. 相似文献
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结合室内轨迹数据的特点,研究了室内移动对象语义轨迹序列处理方法,以及利用LDA主题模型与用户历史轨迹进行室内商场用户分类的方法。提出了通过关联规则分析挖掘用户语义位置模式的方法,并以北京某大型商场的实际用户轨迹数据为例,对所提出的方法进行了实验验证。 相似文献
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Considering the attempts to model spatiotemporal topological relationships between moving object trajectories, the conceptual and computational framework for moving objects along a road network has not received much attention. This paper aims to draw an improved model based on Region Connection Calculus (RCC) theory to represent the spatiotemporal topological relationships between moving object trajectories along road networks. This paper first uses a dimension reduction method based on a linear-reference transformation to model the moving object trajectories segments, and then defines new time–connection and space–connection relations between two trajectory segments. On this basis, the paper proposes an extension to the RCC-based spatiotemporal binary relationship set so that the combined semantics of the spatiotemporal predicates can be described completely. A case study was carried out using Floating Car Data in Guangzhou city. The computational results show that in a real application, the occurrence frequencies of the RCC-based binary relationships are distributed nonuniformly and the semantics of some binary relationships with the highest occurrence are coarse. Therefore, the partition of the spatiotemporal connection relations and the finer aspects of the spatiotemporal relationship model may require further research work. 相似文献
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获取现势性的交通道路数据是数字城市和智慧城市建设的基础，基于传统测绘的道路网更新方法存在一定局限性，而基于众源数据及行车轨迹数据更新道路网近年来则倍受关注。首先提出了一种新的道路变化增量更新方法，该方法先对历史道路网建立面拓扑结构，生成由道路网组成的最小闭合面域（道路网眼）；然后以道路网眼为基本控制单元，综合利用轨迹点上下文距离信息和隐马尔可夫模型（hidden Markov model，HMM），提取失配轨迹点和失配轨迹段；最后采用缓冲区分析和最大密度法对失配轨迹提取骨架线，创建新增道路，增量更新历史道路网。实验结果表明，以道路网眼为控制单元，利用轨迹点上下文距离分析和HMM捕获失配轨迹点，可提高失配轨迹点的提取效率，改善道路网更新效果。该方法可用于大规模路网的增量式更新。 相似文献