Relevant bibliographies by topics / Trajectory Datasets

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Academic literature on the topic 'Trajectory Datasets'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Trajectory Datasets"

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Zhu, Hong, Yunkai Yu, and Meiyi Xie. "Releasing Differential Private Trajectory Datasets Without Revealing Trajectory Correlations." Security and Communication Networks 2022 (June 29, 2022): 1–19. http://dx.doi.org/10.1155/2022/2590648.

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With the prevailing use of smartphones and location-based services, vast amounts of trajectory data are collected and used for many applications. When trajectory data are sent to a third-party research institute for analytical applications, the privacy of users would be severely disclosed. For example, the relationship between users will be revealed from the correlation between trajectories. In this paper, we propose a method for releasing trajectory datasets without revealing correlation between trajectories, called RDPT. In RDPT, we first quantify the trajectory correlation and convert the p
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Park, Daehee, Jaewoo Jeong, and Kuk-Jin Yoon. "Improving Transferability for Cross-Domain Trajectory Prediction via Neural Stochastic Differential Equation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 10145–54. http://dx.doi.org/10.1609/aaai.v38i9.28879.

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Multi-agent trajectory prediction is crucial for various practical applications, spurring the construction of many large-scale trajectory datasets, including vehicles and pedestrians. However, discrepancies exist among datasets due to external factors and data acquisition strategies. External factors include geographical differences and driving styles, while data acquisition strategies include data acquisition rate, history/prediction length, and detector/tracker error. Consequently, the proficient performance of models trained on large-scale datasets has limited transferability on other small
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BARBIE, Thibault, Takaki NISHIO, and Takeshi NISHIDA. "Trajectory Prediction without Requiring Online Datasets." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 2A2—E16. http://dx.doi.org/10.1299/jsmermd.2018.2a2-e16.

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Santiago-Chaparro, Kelvin R., and David A. Noyce. "Expanding the Capabilities of Radar-Based Vehicle Detection Systems: Noise Characterization and Removal Procedures." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (2019): 150–60. http://dx.doi.org/10.1177/0361198119852607.

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The capabilities of radar-based vehicle detection (RVD) systems used at signalized intersections for stop bar and advanced detection are arguably underutilized. Underutilization happens because RVD systems can monitor the position and speed (i.e., trajectory) of multiple vehicles at the same time but these trajectories are only used to emulate the behavior of legacy detection systems such as inductive loop detectors. When full vehicle trajectories tracked by an RVD system are collected, detailed traffic operations and safety performance measures can be calculated for signalized intersections.
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Silber, Israel, Jennifer M. Comstock, Michael R. Kieburtz, and Lynn M. Russell. "ARMTRAJ: a set of multipurpose trajectory datasets augmenting the Atmospheric Radiation Measurement (ARM) user facility measurements." Earth System Science Data 17, no. 1 (2025): 29–42. https://doi.org/10.5194/essd-17-29-2025.

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Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric, thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol–cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observa
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Lim, Hong Seo, and Peng Qiu. "Quantifying the clusterness and trajectoriness of single-cell RNA-seq data." PLOS Computational Biology 20, no. 2 (2024): e1011866. http://dx.doi.org/10.1371/journal.pcbi.1011866.

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Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the “clusterness” and “trajectoriness” of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introdu
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Xiong, Wen, Xiaoxuan Wang, and Hao Li. "Efficient Large-Scale GPS Trajectory Compression on Spark: A Pipeline-Based Approach." Electronics 12, no. 17 (2023): 3569. http://dx.doi.org/10.3390/electronics12173569.

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Every day, hundreds of thousands of vehicles, including buses, taxis, and ride-hailing cars, continuously generate GPS positioning records. Simultaneously, the traffic big data platform of urban transportation systems has already collected a large amount of GPS trajectory datasets. These incremental and historical GPS datasets require more and more storage space, placing unprecedented cost pressure on the big data platform. Therefore, it is imperative to efficiently compress these large-scale GPS trajectory datasets, saving storage cost and subsequent computing cost. However, a set of classica
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Zong, Ruixue, Ying Wang, Juan Ding, and Weiwen Deng. "Statistical Risk and Performance Analyses on Naturalistic Driving Trajectory Datasets for Traffic Modeling." World Electric Vehicle Journal 15, no. 3 (2024): 77. http://dx.doi.org/10.3390/wevj15030077.

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The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive analyses on data diversity and balance in machine learning-oriented research. This study presents a comprehensive assessment of existing highway scenario datasets in the context of traffic modeling in autonomous driving simulation tests. In order to clarify the level of traffic risk, we desi
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Lan, Doi Thi, and Seokhoon Yoon. "Trajectory Clustering-Based Anomaly Detection in Indoor Human Movement." Sensors 23, no. 6 (2023): 3318. http://dx.doi.org/10.3390/s23063318.

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Human movement anomalies in indoor spaces commonly involve urgent situations, such as security threats, accidents, and fires. This paper proposes a two-phase framework for detecting indoor human trajectory anomalies based on density-based spatial clustering of applications with noise (DBSCAN). The first phase of the framework groups datasets into clusters. In the second phase, the abnormality of a new trajectory is checked. A new metric called the longest common sub-sequence using indoor walking distance and semantic label (LCSS_IS) is proposed to calculate the similarity between trajectories,
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Kim, Jong Wook, and Beakcheol Jang. "Effectively computing transition patterns with privacy-preserved trajectory datasets." PLOS ONE 17, no. 12 (2022): e0278744. http://dx.doi.org/10.1371/journal.pone.0278744.

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Recent advances in positioning techniques, along with the widespread use of mobile devices, make it easier to monitor and collect user trajectory information during their daily activities. An ever-growing abundance of data about trajectories of individual users paves the way for various applications that utilize user mobility information. One of the most common analysis tasks in these new applications is to extract the sequential transition patterns between two consecutive timestamps from a collection of trajectories. Such patterns have been widely exploited in diverse applications to predict
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Dissertations / Theses on the topic "Trajectory Datasets"

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Nekooeimehr, Iman. "Oversampling Methods for Imbalanced Dataset Classification and their Application to Gynecological Disorder Diagnosis." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6335.

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In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to some of the classes (majority classes), while only a few instances are from the other classes (minority classes). Conventional classifiers will strongly favor the majority class and ignore the minority instances. The imbalance problem can occur in both binary data classification and also in ordinal regression. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. Extensive research has been performed for address
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Books on the topic "Trajectory Datasets"

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Parnell, Tamsin. Constructing Brexit Britain. Bloomsbury Publishing Plc, 2024. http://dx.doi.org/10.5040/9781350436978.

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Combining corpus linguistics, critical discourse analysis, and a discourse analysis of narratives, this book considers one aspect of the Brexit process: the language that journalists, politicians and individuals used to write and talk about what it means to be British and European around the time of Brexit. It reveals a trajectory towards a discourse of national division in Brexit Britain in three datasets: pro-Brexit newspaper articles, UK Government documents, and interviews with individual citizens. Demonstrating the important role that (supra-)national identity discourses played in discuss
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Book chapters on the topic "Trajectory Datasets"

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Forsch, Axel, Stefan Funke, Jan-Henrik Haunert, and Sabine Storandt. "Efficient Mining of Volunteered Trajectory Datasets." In Volunteered Geographic Information. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_3.

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AbstractWith the ubiquity of mobile devices that are capable of tracking positions (be it via GPS or Wi-Fi/mobile network localization), there is a continuous stream of location data being generated every second. These location measurements are typically not considered individually but rather as sequences, each of which reflects the movement of one person or vehicle, which we call trajectory. This chapter presents new algorithmic approaches to process and visualize trajectories both in the network-constrained and the unconstrained case.
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Lee, Seongju, Junseok Lee, Yeonguk Yu, Taeri Kim, and Kyoobin Lee. "MART: MultiscAle Relational Transformer Networks for Multi-agent Trajectory Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72848-8_6.

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AbstractMulti-agent trajectory prediction is crucial to autonomous driving and understanding the surrounding environment. Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets in recent years. However, the hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a MultiscAle Relational Transformer (MART) network for multi-agent trajectory prediction. MART is a hypergr
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Bériault, Silvain, Fahd Al Subaie, Kelvin Mok, Abbas F. Sadikot, and G. Bruce Pike. "Automatic Trajectory Planning of DBS Neurosurgery from Multi-modal MRI Datasets." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23623-5_33.

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Bhat, Madhav Narayan, Paul Cesaretti, Mayank Goswami, and Prashant Pandey. "Distance and Time Sensitive Filters for Similarity Search in Trajectory Datasets." In Symposium on Algorithmic Principles of Computer Systems (APOCS). Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977578.ch4.

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Wang, Ye, Kyungmi Lee, and Ickjai Lee. "Multivariate Higher Order Information for Emergency Management Based on Tourism Trajectory Datasets." In Trends in Applied Knowledge-Based Systems and Data Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42007-3_62.

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Schestakov, Stefan, Paul Heinemeyer, and Elena Demidova. "Road Network Representation Learning with Vehicle Trajectories." In Advances in Knowledge Discovery and Data Mining. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_5.

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AbstractSpatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model
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Chen, Yanbo, Huilong Yu, and Junqiang Xi. "STS-GAN: Spatial-Temporal Attention Guided Social GAN for Vehicle Trajectory Prediction." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_24.

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AbstractAccurately predicting the trajectories of other vehicles is crucial for autonomous driving to ensure driving safety and efficiency. Recently, deep learning techniques have been extensively employed for trajectory prediction, resulting in significant advancements in predictive accuracy. However, existing studies often fail to explicitly distinguish the impact of historical inputs at different time steps and the influence of surrounding vehicles at distinct locations. Moreover, deep learning-based approaches generally lack model interpretation. To overcome the issues, we propose the Spat
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Lovreglio, Ruggiero, Charitha Dias, Xiang Song, and Lucia Ballerini. "Towards Microscopic Calibration of Pedestrian Simulation Models Using Open Trajectory Datasets: The Case Study of the Edinburgh Informatics Forum." In Traffic and Granular Flow '17. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11440-4_25.

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Fujii, Keisuke. "Predictive Analysis and Play Evaluation with Machine Learning." In SpringerBriefs in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1445-5_3.

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Abstract This chapter examines the important role of machine learning in sports predictive analysis and play evaluation. It covers a spectrum of techniques, from traditional result analysis to advanced machine learning approaches, addressing key areas such as game result, event, and trajectory prediction, as well as action and space evaluation in team sports. The chapter introduces various datasets and methodologies, highlighting the evolution from rule-based systems to deep learning models. It explores how these techniques are applied to classify plays, cluster similar behaviors, extract mean
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Sakoglu, Unal, Lohit Bhupati, Nazanin Beheshti, Nikolaos Tsekos, and Lennart Johnsson. "An Adaptive Space-Filling Curve Trajectory for Ordering 3D Datasets to 1D: Application to Brain Magnetic Resonance Imaging Data for Classification." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50420-5_48.

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Conference papers on the topic "Trajectory Datasets"

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Yamashita, Michiharu, Thanh Tran, and Dongwon Lee. "OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825519.

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Wang, Debao, Xingong Cheng, Xianlong Lv, Conghao Li, Chuang Xu, and Junwei Liu. "A Trajectory Unified Framework for Deep Neural Network Training in Imbalanced Datasets." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010692.

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Han, Yi, Ruichun Zhou, Xiaotong Zhou, et al. "A Roundabout Video Dataset for Vehicle Trajectory Prediction." In 2025 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2025. https://doi.org/10.1109/smartcomp65954.2025.00039.

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Zhou, Xiaotong, Zhenhui Yuan, Yi Han, Tianhua Xu, and Jaiwei Wang. "Large Language Model Based Roundabout Dataset Augmentation for Trajectory Prediction." In 2025 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2025. https://doi.org/10.1109/smartcomp65954.2025.00101.

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Naduvil-Vadukootu, Sajitha, Berkay Aydin, Michael A. Schuh, and Rafal A. Angryk. "Cultivating Evolving Region Trajectory Datasets." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.42.

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Lucchese, Claudio, Michail Vlachos, Deepak Rajan, and Philip S. Yu. "Rights Protection of Trajectory Datasets." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497552.

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Zachár, Gergely. "Visualization of large-scale trajectory datasets." In CPS-IoT Week '23: Cyber-Physical Systems and Internet of Things Week 2023. ACM, 2023. http://dx.doi.org/10.1145/3576914.3587710.

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Amiri, Hossein, Richard Yang, and Andreas Züfle. "GeoLife+: Large-Scale Simulated Trajectory Datasets Calibrated to the GeoLife Dataset." In SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems. ACM, 2024. http://dx.doi.org/10.1145/3681770.3698573.

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Hassan, Md Yeakub, Ullash Saha, Noman Mohammed, Stephane Durocher, and Avery Miller. "Efficient Privacy-Preserving Approaches for Trajectory Datasets." In 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE, 2020. http://dx.doi.org/10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00107.

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Lin, Chen-Yi, Yuan-Chen Wang, Wan-Tian Fu, Yun-Sheng Chen, Kuan-Chen Chien, and Bing-Yi Lin. "Efficiently Preserving Privacy on Large Trajectory Datasets." In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). IEEE, 2018. http://dx.doi.org/10.1109/dsc.2018.00058.

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Reports on the topic "Trajectory Datasets"

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Clark, Andrew E., Angela Greulich, and Hippolyte d’Albis. The age U-shape in Europe: the protective role of partnership. Verlag der Österreichischen Akademie der Wissenschaften, 2021. http://dx.doi.org/10.1553/populationyearbook2021.res3.1.

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In this study, we ask whether the U-shaped relationship between life satisfactionand age is flatter for individuals who are partnered. An analysis of cross-sectionalEU-SILC data indicates that the decline in life satisfaction from the teens to thefifties is almost four times larger for non-partnered than for partnered individuals,whose life satisfaction essentially follows a slight downward trajectory with age.However, the same analysis applied to three panel datasets (BHPS, SOEP andHILDA) reveals a U-shape for both groups, albeit somewhat flatter for the partneredthan for the non-partnered in
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Bado, Arsène Brice, and Brandon Kendhammer. Women, CBAGs, and the Politics of Security Supply & Demand in Côte d’Ivoire. RESOLVE Network, 2022. http://dx.doi.org/10.37805/cbags2022.1.

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This study explores the drivers of participation and the roles women play within their communities in participating both formally and informally in community-based security groups. It seeks to understand how women are involved in community-based security groups by investigating and illustrating, among other things, their motivations and roles, the context, and the dynamics that underpin their participation in both the supply side and demand side of security provision. Based on extensive field research and an original dataset of interviews with a wide range of informal security actors, this res
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