Relevant bibliographies by topics / GPS trajectory data

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'GPS trajectory data'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "GPS trajectory data"

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JUNG, Jaeeun, Taeho OH, and Inhi KIM. "Volume Delay Function Calibration Based on GPS Trajectory Data." Journal of Korean Society of Transportation 39, no. 4 (2021): 399–408. http://dx.doi.org/10.7470/jkst.2021.39.4.399.

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Kim, Taeyong, Bokuk Park, Jinkwan Park, and Hwan-Gue Cho. "An Efficient Clustering Algorithm for Massive GPS Trajectory Data." Journal of KIISE 43, no. 1 (2016): 40–46. http://dx.doi.org/10.5626/jok.2016.43.1.40.

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Corcoran, Padraig. "Topological Path Planning in GPS Trajectory Data." Sensors 16, no. 12 (2016): 2203. http://dx.doi.org/10.3390/s16122203.

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Dai, Zhaoxin, Weixiang Peng, and Chengcheng Zhang. "Data Cleansing Method for Sparse Trajectory Data: A Case Study of Shared Electric Bicycles in Tengzhou." Proceedings of the ICA 2 (July 10, 2019): 1–8. http://dx.doi.org/10.5194/ica-proc-2-22-2019.

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<p><strong>Abstract.</strong> Location based service (LBS) technologies provides a new perspective for the spatiotemporal dynamics analysis of urban systems. Previous studies have been performed by using data of mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, the analysis based on shared electric bicycles (e-bike) has yet to be studied in the literature. Data cleansing and the extraction of origin-destination (O-D) are prerequisites for the study of urban systems spatiotemporal patterns. In this study, based
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Sun, Shuming, Juan Chen, and Jian Sun. "Traffic congestion prediction based on GPS trajectory data." International Journal of Distributed Sensor Networks 15, no. 5 (2019): 155014771984744. http://dx.doi.org/10.1177/1550147719847440.

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Since speed sensors are not as widely used as GPS devices, the traffic congestion level is predicted based on processed GPS trajectory data in this article. Hidden Markov model is used to match GPS trajectory data to road network and the average speed of road sections can be estimated by adjacent GPS trajectory data. Four deep learning models including convolutional neural network, recurrent neural network, long short-term memory, and gated recurrent unit and three conventional machine learning models including autoregressive integrated moving average model, support vector regression, and ridg
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Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.

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Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there
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Li, Ji, Xin Pei, Xuejiao Wang, Danya Yao, Yi Zhang, and Yun Yue. "Transportation mode identification with GPS trajectory data and GIS information." Tsinghua Science and Technology 26, no. 4 (2021): 403–16. http://dx.doi.org/10.26599/tst.2020.9010014.

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Zhu, Lei, Jacob R. Holden, and Jeffrey D. Gonder. "Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (2017): 67–75. http://dx.doi.org/10.3141/2645-08.

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With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory.
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Li, Chengming, Zhaoxin Dai, Weixiang Peng, and Jianming Shen. "Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles." Sustainability 11, no. 5 (2019): 1429. http://dx.doi.org/10.3390/su11051429.

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Location-based service (LBS) technologies provide a new perspective for the analysis of the spatiotemporal dynamics of urban systems. Previous studies have been performed using data from mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, corresponding analyses based on shared electric bicycle (e-bike) have not yet been reported in the literature. Data cleaning and extraction of the origin-destination (O-D) are prerequisites for the study of the spatiotemporal patterns of urban systems. In this study, based on a dataset of a week
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Han, Shaowei, and Chris Rizos. "Road Slope Information from GPS-Derived Trajectory Data." Journal of Surveying Engineering 125, no. 2 (1999): 59–68. http://dx.doi.org/10.1061/(asce)0733-9453(1999)125:2(59).

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Dissertations / Theses on the topic "GPS trajectory data"

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Griffin, Terry W. "GPS CaPPture: a System for GPS Trajectory Collection, Processing, and Destination Prediction." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc115089/.

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In the United States, smartphone ownership surpassed 69.5 million in February 2011 with a large portion of those users (20%) downloading applications (apps) that enhance the usability of a device by adding additional functionality. a large percentage of apps are written specifically to utilize the geographical position of a mobile device. One of the prime factors in developing location prediction models is the use of historical data to train such a model. with larger sets of training data, prediction algorithms become more accurate; however, the use of historical data can quickly become a down
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Dabiri, Sina. "Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/86845.

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Identification of travelers' transportation modes is a fundamental step for various problems that arise in the domain of transportation such as travel demand analysis, transport planning, and traffic management. This thesis aims to identify travelers' transportation modes purely based on their GPS trajectories. First, a segmentation process is developed to partition a user's trip into GPS segments with only one transportation mode. A majority of studies have proposed mode inference models based on hand-crafted features, which might be vulnerable to traffic and environmental conditions. Further
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Chen, Chao. "Understanding social and community dynamics from taxi GPS data." Phd thesis, Institut National des Télécommunications, 2014. http://tel.archives-ouvertes.fr/tel-01048662.

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Taxis equipped with GPS sensors are an important sensory device for examining people's movements and activities. They are not constrained to a pre-defined schedule/route. Big taxi GPS data recording the spatio-temporal traces left by taxis provides rich and detailed glimpse into the motivations, behaviours, and resulting dynamics of a city's mobile population through the road network. In this dissertation, we aim to uncover the "hidden facets" regarding social and community dynamics encoded in the taxi GPS data to better understand how urban population behaves and the resulting dynamics in the
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Asgari, Fereshteh. "Inferring user multimodal trajectories from cellular network metadata in metropolitan areas." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0005/document.

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Dans cette thèse, nous avons étudier une méthode de classification et d'évaluation des modalités de transport utilisées par les porteurs de mobile durant leurs trajets quotidiens. Les informations de mobilité sont collectées par un opérateur au travers des logs du réseau téléphonique mobile qui fournissent des informations sur les stations de base qui ont été utilisées par un mobile durant son trajet. Les signaux (appels/SMS/3G/4G) émis par les téléphones sont une source d'information pertinente pour l'analyse de la mobilité humaine, mais au-delà de ça, ces données représentent surtout un moye
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Leal, Bruno de Carvalho. "Da modelagem Conceitual à Representação Lógica de Trajetórias em SGBDOR e Sistemas de DW." reponame:Repositório Institucional da UFC, 2011. http://www.repositorio.ufc.br/handle/riufc/17455.

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LEAL, Bruno de Carvalho. Da modelagem Conceitual à Representação Lógica de Trajetórias em SGBDOR e Sistemas de DW. 2011. 120 f. : Dissertação (mestrado) - Universidade Federal do Ceará, Centro de Ciências, Departamento de Computação, Fortaleza-CE, 2011.<br>Submitted by guaracy araujo (guaraa3355@gmail.com) on 2016-06-03T18:06:29Z No. of bitstreams: 1 2011_dis_bcleal.pdf: 2151043 bytes, checksum: 6cb423b35ccbf999cc937ddda41507be (MD5)<br>Approved for entry into archive by guaracy araujo (guaraa3355@gmail.com) on 2016-06-03T18:07:43Z (GMT) No. of bitstreams: 1 2011_dis_bcleal.pdf: 2151043 bytes,
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Koňárek, Petr. "Dolování neobvyklého chování v datech trajektorií." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363796.

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The goal of this work is to provide an overview of approaches for mining anomalous behavior in trajectory data. Next part is proposes a mining task for outliner detection in trajectories and selects appropriate methods for this task. Selected methods are implemented as application for outliner trajectories detection.
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Schwarz, Ivan. "Rozpoznávání aktivit z trajektorií pohybujících se objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236165.

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The aim of this thesis is a development of a system for trajectory-based periodic pattern recognition and following GPS trajectory classification. This system is designed according to a performed analysis of techniques of data mining in moving object data and furthermore, on recent research on a subject of a trajectory-based activity recognition. This system is implemented in C++ programming language and experiments addresing its      effectiveness are performed.
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Santolini, Nicola. "Utilizzo di dati social per la deanonimizzazione di tracce GPS." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18088/.

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Negli ultimi anni la diffusione massiva e pervasiva dei dispositivi di localizzazione ha portato a un aumento esponenziale della produzione di dati di traiettoria. Un numero crescente di applicazioni, in maniera più o meno diretta, raccoglie e memorizza dati relativi al posizionamento degli utenti che le utilizzano. Questi dati racchiudono un potere informativo enorme, che da un lato ha stimolato un forte sviluppo delle tecniche di analisi (si parla di trajectory data mining) ma dall'altro ha portato a una maggiore esposizione della privacy dei soggetti che producono i dati stessi. Attraverso
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Gamez, serna Citlalli. "Towards visual urban scene understanding for autonomous vehicle path tracking using GPS positioning data." Thesis, Bourgogne Franche-Comté, 2019. http://www.theses.fr/2019UBFCA004/document.

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Cette thèse de doctorat s’intéresse au suivi de trajectoire basé sur la perception visuelle et la localisation en milieu urbain. L'approche proposée comprend deux systèmes. Le premier concerne la perception de l'environnement. Cette tâche est effectuée en utilisant des techniques d'apprentissage profond pour extraire automatiquement les caractéristiques visuelles 2D et utiliser ces derniers pour apprendre à distinguer les différents objets dans les scénarios de conduite. Trois techniques d'apprentissage approfondi sont adoptées : la segmentation sémantique pour assigner chaque pixel d’une imag
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.

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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, i
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Books on the topic "GPS trajectory data"

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1.

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Using the GPS to Collect Trajectory Data for Ejection Seat Design, Validation, and Testing. Storming Media, 2002.

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Fox, Raymond. The Use of Self. Oxford University Press, 2011. http://dx.doi.org/10.1093/oso/9780190616144.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic
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Raff, Lionel, Ranga Komanduri, Martin Hagan, and Satish Bukkapatnam. Neural Networks in Chemical Reaction Dynamics. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199765652.001.0001.

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This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic
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Book chapters on the topic "GPS trajectory data"

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "Trajectory Data Compression." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_2.

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "Trajectory Data Protection." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_3.

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "Trajectory Data Map-matching." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_1.

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Yin, Peifeng, Mao Ye, Wang-Chien Lee, and Zhenhui Li. "Mining GPS Data for Trajectory Recommendation." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06605-9_5.

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "GPS Environment Friendliness Estimation with Trajectory Data." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_8.

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Li, Lun, Xiaohang Chen, Qizhi Liu, and Zhifeng Bao. "A Data-Driven Approach for GPS Trajectory Data Cleaning." In Database Systems for Advanced Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59410-7_1.

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "TripPlanner: Personalized Trip Planning Leveraging Heterogeneous Trajectory Data." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_10.

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "Real-Time Imputing Trip Purpose Leveraging Heterogeneous Trajectory Data." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_7.

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "B-Planner: Planning Night Bus Routes Using Taxi Trajectory Data." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_9.

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Chen, Chao, Daqing Zhang, Yasha Wang, and Hongyu Huang. "Open Issues and Conclusions." In Enabling Smart Urban Services with GPS Trajectory Data. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0178-1_14.

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Conference papers on the topic "GPS trajectory data"

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Muckell, Jonathan, Jeong-Hyon Hwang, Catherine T. Lawson, and S. S. Ravi. "Algorithms for compressing GPS trajectory data." In the 18th SIGSPATIAL International Conference. ACM Press, 2010. http://dx.doi.org/10.1145/1869790.1869847.

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Savage, Norma Saiph, Shoji Nishimura, Norma Elva Chavez, and Xifeng Yan. "Frequent trajectory mining on GPS data." In the 3rd International Workshop. ACM Press, 2010. http://dx.doi.org/10.1145/1899662.1899665.

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Xiaoliang Geng, H. Arimura, and T. Uno. "Pattern Mining from Trajectory GPS Data." In 2012 IIAI International Conference on Advanced Applied Informatics (IIAIAAI 2012). IEEE, 2012. http://dx.doi.org/10.1109/iiai-aai.2012.21.

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Lin, Kunhui, Zhentuan Xu, Ming Qiu, Xiaoli Wang, and Tianxiong Han. "Noise filtering, trajectory compression and trajectory segmentation on GPS data." In 2016 11th International Conference on Computer Science & Education (ICCSE). IEEE, 2016. http://dx.doi.org/10.1109/iccse.2016.7581629.

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Birnbaum, Jeremy, Hsiang-Cheng Meng, Jeong-Hyon Hwang, and Catherine Lawson. "Similarity-Based Compression of GPS Trajectory Data." In 2013 4th International Conference on Computing for Geospatial Research & Application (COM.Geo). IEEE, 2013. http://dx.doi.org/10.1109/comgeo.2013.15.

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Ismail, Anas, and Antoine Vigneron. "A New Trajectory Similarity Measure for GPS Data." In SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2015. http://dx.doi.org/10.1145/2833165.2833173.

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Barucija, Emir, Amra Mujcinovic, Berina Muhovic, Emir Zunic, and Dzenana Donko. "Data-driven approach for anomaly detection of real GPS trajectory data." In 2019 XXVII International Conference on Information, Communication and Automation Technologies (ICAT). IEEE, 2019. http://dx.doi.org/10.1109/icat47117.2019.8938938.

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Miao, Xupeng, Qiange Wang, and Tiancheng Zhang. "Road network generation from low frequency GPS trajectory data." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7532123.

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Patil, Vikram, Priyanka Singh, Shivam Parikh, and Pradeep K. Atrey. "GeoSClean: Secure Cleaning of GPS Trajectory Data Using Anomaly Detection." In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2018. http://dx.doi.org/10.1109/mipr.2018.00037.

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Fu, Chuanyun, Yue Zhou, Chuan Xu, and Haipeng Cui. "Spatial Analysis of Taxi Speeding Event Using GPS Trajectory Data *." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8916870.

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