Relevant bibliographies by topics / Spatio-temporal trajectories

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Academic literature on the topic 'Spatio-temporal trajectories'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Spatio-temporal trajectories"

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Gudmundsson, Joachim, Jyrki Katajainen, Damian Merrick, Cahya Ong, and Thomas Wolle. "Compressing spatio-temporal trajectories." Computational Geometry 42, no. 9 (2009): 825–41. http://dx.doi.org/10.1016/j.comgeo.2009.02.002.

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Madanayake, Adikarige Randil Sanjeewa, Kyungmi Lee, and Ickjai Lee. "Mining contacts from spatio-temporal trajectories." AI Open 5 (2024): 197–207. http://dx.doi.org/10.1016/j.aiopen.2024.10.002.

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Bao, Wei, Li Xin Ji, Shi Lin Gao, Xing Li, and Li Xiong Liu. "Video Copy Detection Based on Fusion of Spatio-Temporal Features." Applied Mechanics and Materials 347-350 (August 2013): 3653–61. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3653.

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A video copy detection method based on fusion of spatio-temporal features is proposed in this paper. Firstly, trajectories are built and lens boundaries are detected by SURF features analyzing, then normalized histogram is used to describe spatio-temporal behavior of trajectories, the bag of visual words is constructed by trajectories behavior clustering, word frequency vectors and SURF features with behavior labels are extracted to express spatio-temporal content of lens, finally, duplicates are detected efficiently based on grade-match. The experimental results show the performance of this m
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Zhou, Yan, Yunhan Zhang, Fangfang Zhang, Yeting Zhang, and Xiaodi Wang. "Trajectory Compression with Spatio-Temporal Semantic Constraints." ISPRS International Journal of Geo-Information 13, no. 6 (2024): 212. http://dx.doi.org/10.3390/ijgi13060212.

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Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature po
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Zhang, Ran, Xiaohui Chen, Lin Ye, Wentao Yu, Bing Zhang, and Junnan Liu. "Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix." Applied Sciences 14, no. 10 (2024): 4104. http://dx.doi.org/10.3390/app14104104.

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This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that descri
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Yang, Yuqi, Xiaoqing Zuo, Kang Zhao, and Yongfa Li. "Non-Uniform Spatial Partitions and Optimized Trajectory Segments for Storage and Indexing of Massive GPS Trajectory Data." ISPRS International Journal of Geo-Information 13, no. 6 (2024): 197. http://dx.doi.org/10.3390/ijgi13060197.

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The presence of abundant spatio-temporal information based on the location of mobile objects in publicly accessible GPS mobile devices makes it crucial to collect, analyze, and mine such information. Therefore, it is necessary to index a large volume of trajectory data to facilitate efficient trajectory retrieval and access. It is difficult for existing indexing methods that primarily rely on data-driven indexing structures (such as R-Tree) or space-driven indexing structures (such as Quadtree) to support efficient analysis and computation of data based on spatio-temporal range queries as a se
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Ni, Jinfeng, and Chinya V. Ravishankar. "Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations." IEEE Transactions on Knowledge and Data Engineering 19, no. 5 (2007): 663–78. http://dx.doi.org/10.1109/tkde.2007.1006.

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Sandu Popa, Iulian, Karine Zeitouni, Vincent Oria, and Ahmed Kharrat. "Spatio-temporal compression of trajectories in road networks." GeoInformatica 19, no. 1 (2014): 117–45. http://dx.doi.org/10.1007/s10707-014-0208-4.

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Zhang, Dongzhi, Kyungmi Lee, and Ickjai Lee. "Semantic periodic pattern mining from spatio-temporal trajectories." Information Sciences 502 (October 2019): 164–89. http://dx.doi.org/10.1016/j.ins.2019.06.035.

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Wang, Jiang, Cheng Zhu, Yun Zhou, and Weiming Zhang. "Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering." Journal of Navigation 70, no. 6 (2017): 1383–400. http://dx.doi.org/10.1017/s0373463317000406.

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Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to ge
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Dissertations / Theses on the topic "Spatio-temporal trajectories"

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Ishikawa, Yoshiharu. "SPATIO-TEMPORAL DATA MINING FROM MOVING OBJECT TRAJECTORIES." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2006. http://hdl.handle.net/2237/10446.

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Partsinevelos, Panayotis. "Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery." Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/PartsinevelosP2002.pdf.

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Jin, Meihan. "Un modèle spatio-temporel sémantique pour la modélisation de mobilités en milieu urbain." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0067/document.

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La croissance rapide et la complexité de nombreuses villes contemporaines offrent de nombreux défis de recherche pour les scientifiques à la recherche d'une meilleure compréhension des mobilités qui se produisent dans l'espace et dans le temps. A l’heure où de très grandes séries de données de trajectoires en milieu urbain sont disponibles grâce à profusion de nombreux capteurs de positionnement et de services de nombreuses et nouvelles opportunités de recherche et d’application nous sont offertes. Cependant, une bonne intégration de ces données de mobilité nécessite encore l'élaboration de ca
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Palma, Andrey Luis Tietbohl. "A clustering-based approach for discovering interesting places in trajectories." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/17024.

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Por causa da grande quantidade de dados de trajetórias producidos por dispositivos móveis, existe um aumento crescente das necessidades de mecanismos para extrair conhecimento a partir desses dados. A maioria dos trabalhos existentes focam nas propriedades geometricas das trajetorias, mas recentemente surgiu o conceito de trajetórias semânticas, nas quais a informação da geografia por baixo da trajetória é integrada aos pontos da trajetória. Nesse novo conceito, trajetórias são observadas como um conjunto de stops e moves, onde stops são as partes mais importantes da trajetória. Os stops e mov
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Wu, Jing. "A qualitative spatio-temporal modelling and reasoning approach for the representation of moving entities." Thesis, Brest, 2015. http://www.theses.fr/2015BRES0036/document.

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La recherche développée dans cette thèse introduit une approche qualitative pour représenter et raisonner à partir d'entités spatiales dans un espace géographique à deux dimensions. Les patrons de mouvements entre entités dynamiques sont catégorisés à partir d'un modèle qualitatif de relations topologiques entre une ligne orientée et une région, et de relations d'orientation entre deux lignes orientées, respectivement. Les mouvements qualitatifs sont dérivés à partir de relations spatio-temporelles qui caractérisent des entités dynamiques conceptualisées comme des points ou des régions dans un
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Vercelloni, Julie. "Quantifying the state of populations and effects of disturbances at large spatio-temporal scales: The case of coral populations in the great barrier reef." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/87812/1/Julie_Vercelloni_Thesis.pdf.

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This project was a step forward in applying statistical methods and models to provide new insights for more informed decision-making at large spatial scales. The model has been designed to address complicated effects of ecological processes that govern the state of populations and uncertainties inherent in large spatio-temporal datasets. Specifically, the thesis contributes to better understanding and management of the Great Barrier Reef.
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Reux, Sara. "Les figures de la discontinuité dans le développement résidentiel périurbain : application à la région Limousin." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0019/document.

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Alors que la continuité du bâti ne suffit plus pour appréhender l’espace urbain d’aujourd’hui,la discontinuité du tissu urbain est devenue une clé de compréhension de la ville contemporaine et de sonprocessus de formation. Elle suscite l'intérêt des chercheurs, d'autant plus que le déploiement des systèmesd'information géographique offre de nouvelles perspectives de mesure des formes urbaines. Mais, si lestravaux en écologie du paysage ou en géographie permettent de mesurer l'émergence de ces formesdiscontinues, il nous semble important de nous intéresser aux fondements économiques de l'urbani
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Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738/document.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont propo
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Almuhisen, Feda. "Leveraging formal concept analysis and pattern mining for moving object trajectory analysis." Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738.

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Cette thèse présente un cadre de travail d'analyse de trajectoires contenant une phase de prétraitement et un processus d’extraction de trajectoires d’objets mobiles. Le cadre offre des fonctions visuelles reflétant le comportement d'évolution des motifs de trajectoires. L'originalité de l’approche est d’allier extraction de motifs fréquents, extraction de motifs émergents et analyse formelle de concepts pour analyser les trajectoires. A partir des données de trajectoires, les méthodes proposées détectent et caractérisent les comportements d'évolution des motifs. Trois contributions sont propo
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Strat, Sabin Tiberius. "Analyse et interprétation de scènes visuelles par approches collaboratives." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00959081.

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Les dernières années, la taille des collections vidéo a connu une forte augmentation. La recherche et la navigation efficaces dans des telles collections demande une indexation avec des termes pertinents, ce qui nous amène au sujet de cette thèse, l'indexation sémantique des vidéos. Dans ce contexte, le modèle Sac de Mots (BoW), utilisant souvent des caractéristiques SIFT ou SURF, donne de bons résultats sur les images statiques. Notre première contribution est d'améliorer les résultats des descripteurs SIFT/SURF BoW sur les vidéos en pré-traitant les vidéos avec un modèle de rétine humaine, c
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Book chapters on the topic "Spatio-temporal trajectories"

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Frentzos, Elias, Yannis Theodoridis, and Apostolos N. Papadopoulos. "Spatio-Temporal Trajectories." In Encyclopedia of Database Systems. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-7993-3_364-2.

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Frentzos, Elias, Yannis Theodoridis, and Apostolos N. Papadopoulos. "Spatio-Temporal Trajectories." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_364.

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Gudmundsson, Joachim, Jyrki Katajainen, Damian Merrick, Cahya Ong, and Thomas Wolle. "Compressing Spatio-temporal Trajectories." In Algorithms and Computation. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-77120-3_66.

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Padoy, Nicolas, and Gregory D. Hager. "Spatio-Temporal Registration of Multiple Trajectories." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23623-5_19.

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Zhang, Dongzhi, Kyungmi Lee, and Ickjai Lee. "Mining Medical Periodic Patterns from Spatio-Temporal Trajectories." In Health Information Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01078-2_11.

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Wang, Chenhao, Lisi Chen, Shanshan Feng, and Shuo Shang. "Flexible Contact Correlation Learning on Spatio-Temporal Trajectories." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5552-3_10.

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Zhang, Pengdong, Min Deng, and Nico Van de Weghe. "Clustering Spatio-temporal Trajectories Based on Kernel Density Estimation." In Computational Science and Its Applications – ICCSA 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09144-0_21.

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Hellbach, Sven, Julian P. Eggert, Edgar Körner, and Horst-Michael Gross. "Basis Decomposition of Motion Trajectories Using Spatio-temporal NMF." In Artificial Neural Networks – ICANN 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04277-5_81.

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Hwang, Jung-Rae, Hye-Young Kang, and Ki-Joune Li. "Spatio-temporal Similarity Analysis Between Trajectories on Road Networks." In Perspectives in Conceptual Modeling. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11568346_30.

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Gryllakis, Fragkiskos, Nikos Pelekis, Christos Doulkeridis, Stylianos Sideridis, and Yannis Theodoridis. "Searching for Spatio-Temporal-Keyword Patterns in Semantic Trajectories." In Advances in Intelligent Data Analysis XVI. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68765-0_10.

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Conference papers on the topic "Spatio-temporal trajectories"

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Hong, Yan, and Hao Yue. "Design of traffic signal timing schemes for intersections based on vehicle spatio-temporal trajectories." In Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), edited by Xiantao Xiao and Jia Yao. SPIE, 2024. https://doi.org/10.1117/12.3054605.

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Meskovic, E., D. Osmanovic, Z. Galic, and M. Baranovic. "Generating spatio-temporal streaming trajectories." In 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEE, 2014. http://dx.doi.org/10.1109/mipro.2014.6859738.

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Xing, Songhua, Xuan Liu, Qing He, and Arun Hampapur. "Mining Trajectories for Spatio-temporal Analytics." In 2012 IEEE 12th International Conference on Data Mining Workshops. IEEE, 2012. http://dx.doi.org/10.1109/icdmw.2012.25.

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Patel, Dhaval, Chidansh Bhatt, Wynne Hsu, Mong Li Lee, and Mohan Kankanhalli. "Analyzing Abnormal Events from Spatio-temporal Trajectories." In 2009 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2009. http://dx.doi.org/10.1109/icdmw.2009.45.

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Cai, Yuhan, and Raymond Ng. "Indexing spatio-temporal trajectories with Chebyshev polynomials." In the 2004 ACM SIGMOD international conference. ACM Press, 2004. http://dx.doi.org/10.1145/1007568.1007636.

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Karapiperis, Dimitrios, Aris Gkoulalas-Divanis, and Vassilios S. Verykios. "Linkage of Spatio-Temporal Data and Trajectories." In 2019 IEEE International Smart Cities Conference (ISC2). IEEE, 2019. http://dx.doi.org/10.1109/isc246665.2019.9071724.

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Galasso, Fabio, Masahiro Iwasaki, Kunio Nobori, and Roberto Cipolla. "Spatio-temporal clustering of probabilistic region trajectories." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126438.

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Frihida, Ali, Donia Zheni, Christophe Claramunt, and Henda Ben Ghezala. "Modeling Trajectories: A Spatio-Temporal Data Type Approach." In 2009 20th International Workshop on Database and Expert Systems Application. DEXA 2009. IEEE, 2009. http://dx.doi.org/10.1109/dexa.2009.70.

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Badretdinov, R., E. Takhavova, and M. Shleimovich. "Characteristic Trajectories Detection in Spatio-Temporal Data Streams." In 2019 International Science and Technology Conference "EastConf". IEEE, 2019. http://dx.doi.org/10.1109/eastconf.2019.8725376.

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Brkic, K., S. Segvic, Z. Kalafatic, I. Sikiric, and A. Pinz. "Generative modeling of spatio-temporal traffic sign trajectories." In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2010. http://dx.doi.org/10.1109/cvprw.2010.5543888.

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