Relevant bibliographies by topics / Traffic data generation

Contents

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

Academic literature on the topic 'Traffic data generation'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Traffic data generation"

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Asif, Muhammad, Shafi Khan, Rashid Ahmad, and Dhananjay Singh. "Game-Theoretic Solutions for Data Offloading in Next Generation Networks." Symmetry 10, no. 8 (2018): 299. http://dx.doi.org/10.3390/sym10080299.

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In recent years, global mobile data traffic has seen an unprecedented increase. This is due to worldwide usage of smart devices, availability of fast internet connections, and the popularity of social media. The Mobile Network Operators (MNOs) are, therefore, facing problems in handling this huge traffic flow. Each type of traffic, including real-time video, audio, and text has its own Quality of Services (QoS) requirements which, if not met, may cause a sufficient loss of profit. Offloading of these traffics can be made more efficient so that values of QoS parameters are enhanced. In this wor
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Chen, Zhijun, Jingming Zhang, Yishi Zhang, and Zihao Huang. "Traffic Accident Data Generation Based on Improved Generative Adversarial Networks." Sensors 21, no. 17 (2021): 5767. http://dx.doi.org/10.3390/s21175767.

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For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the genera
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Knies, Christian, and Frank Diermeyer. "Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways." Applied Sciences 10, no. 22 (2020): 8154. http://dx.doi.org/10.3390/app10228154.

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Future automated vehicles will have to meet the challenge of anticipating the intentions of other road users in order to plan their own behavior without compromising safety and efficiency of the surrounding road traffic. Therefore, the research area of cooperative driving deals with maneuver-planning algorithms that enable vehicles to behave cooperatively in interactive traffic scenarios. To prove the functionality of these algorithms, single test scenarios are used in the current body of literature. The use of a single, exemplary scenario bears the risk that the presented approach only works
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Ma, Xiaoyi, Xiaowei Hu, Thomas Weber, and Dieter Schramm. "Evaluation of Accuracy of Traffic Flow Generation in SUMO." Applied Sciences 11, no. 6 (2021): 2584. http://dx.doi.org/10.3390/app11062584.

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A traffic simulation of the Jianghan Zone in Wuhan, China was carried out. In order to simulate genuine traffic flow without traditional hard-to-implement data collection methods, geographic population distribution data were gathered from the public information and traffic flow was generated by ActivityGen in SUMO (Simulation of Urban Mobility). For the sake of discovering the accuracy of the simulated traffic, real-time road condition and traffic prediction based on previous data on same time of each road in this area was compared. The results show that traffic flow generated from geographic
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Grebenshchikova, A., and Elagin V. "Data Traffic Based on Slicing in a 5G Smart Uplink System." Telecom IT 8, no. 2 (2020): 44–54. http://dx.doi.org/10.31854/2307-1303-2020-8-2-44-54.

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The paper considers the data traffic based on slicing in a 5g mobile network uplink system. Slicing is a promising technology for the fifth generation of networks that provides optimal quality of QOS services for each specific user or group of users. Data traffic that is processed by cellular networks increases every year. Therefore, we should consider all set of traffic from VoIP to M2M devices. For example, smart devices in the healthcare system transmit big data that is sensitive to latency, but also a video stream that requires minimal latency in certain cases. The paper focuses on the suc
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Silva, Pedro Henrique, and Nilton Alves. "IPERF tool: generation and evaluation of TCP and UDP data traffic." Notas técnicas 4, no. 2 (2014): 1–13. http://dx.doi.org/10.7437/nt2236-7640/2014.02.003.

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Prem Jacob, T., A. Pravin, Manikandan Ramachandran, and Fadi Al-Turjman. "Differential spectrum access for next generation data traffic in massive-IoT." Microprocessors and Microsystems 82 (April 2021): 103951. http://dx.doi.org/10.1016/j.micpro.2021.103951.

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Koutsakis, Polychronis. "Scheduling and Traffic Policing for Variably Demanding Video Traffic Integrated with Voice and Data Traffic in 4th Generation Wireless Networks." Wireless Personal Communications 36, no. 1 (2006): 69–77. http://dx.doi.org/10.1007/s11277-006-8030-4.

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Shakhuro, V. I., and A. S. Konushin. "IMAGE SYNTHESIS WITH NEURAL NETWORKS FOR TRAFFIC SIGN CLASSIFICATION." Computer Optics 42, no. 1 (2018): 105–12. http://dx.doi.org/10.18287/2412-6179-2018-42-1-105-112.

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In this work, we research the applicability of generative adversarial neural networks for generating training samples for a traffic sign classification task. We consider generative neural networks trained using the Wasserstein metric. As a baseline method for comparison, we take image generation based on traffic sign icons. Experimental evaluation of the classifiers based on convolutional neural networks is conducted on real data, two types of synthetic data, and a combination of real and synthetic data. The experiments show that modern generative neural networks are capable of generating real
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Grekhov, Andrii, Vasyl Kondratiuk, and Svitlana Ilnytska. "Data Traffic Modeling in RPAS/UAV Networks with Different Architectures." Modelling 2, no. 2 (2021): 210–23. http://dx.doi.org/10.3390/modelling2020011.

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Deploying of Fifth Generation and Beyond Fifth Generation (5G/B5G) wireless networks will require wider coverage, flexible connectivity, low latency, support for a large number of user devices, and more bandwidth. This article explores the paradigm that Remotely Piloted Air Systems (RPASs) or Unmanned Aerial Vehicles (UAVs) are integrated as a communication platform with cellular networks using radio access. It is important to know the possibilities and ways of such integration for effective interaction with RPASs. This paper studies the issues of ensuring the required Quality of Service (QoS)
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Dissertations / Theses on the topic "Traffic data generation"

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Puri, Anuj. "Statistical profile generation of real-time UAV-based traffic data." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002674.

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Johnston, Robert Thomas. "A traffic generation algorithm for SDH digital cross-connects." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15723.

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MURASE, Hiroshi, Yoshito MEKADA, Ichiro IDE, Tomokazu TAKAHASHI, and Hiroyuki ISHIDA. "Generation of Training Data by Degradation Models for Traffic Sign Symbol Recognition." Institute of Electronics, Information and Communication Engineers, 2007. http://hdl.handle.net/2237/14958.

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Gössel, Frank. "Informationsentropische, spektrale und statistische Untersuchungen fahrzeuggenerierter Verkehrsdaten unter besonderer Berücksichtigung der Auswertung und Dimensionierung von FCD-Systemen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2005. http://nbn-resolving.de/urn:nbn:de:swb:14-1116572388287-50504.

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Untersuchungsgegenstand der vorliegenden Arbeit ist die Schnittstelle zwischen Verkehrsprozess und Informationsprozess in Systemen für die fahrzeuggenerierte Verkehrsdatengewinnung. Dabei konzentrieren sich die Untersuchungen auf die originäre Größe Geschwindigkeit. Das wesentliche Ziel der theoretischen und praktischen Untersuchungen bildet die qualifizierte Bestimmung makroskopischer Kenngrößen des Verkehrsflusses aus mikroskopischen Einzelfahrzeugdaten. Einen Schwerpunkt der Arbeit bildet die Analyse von mikroskopischen Einzelfahrzeugdaten mit Hilfe von informationsentropischen und spektral
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Gössel, Frank. "Informationsentropische, spektrale und statistische Untersuchungen fahrzeuggenerierter Verkehrsdaten unter besonderer Berücksichtigung der Auswertung und Dimensionierung von FCD-Systemen." Doctoral thesis, Technische Universität Dresden, 2004. https://tud.qucosa.de/id/qucosa%3A24494.

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Untersuchungsgegenstand der vorliegenden Arbeit ist die Schnittstelle zwischen Verkehrsprozess und Informationsprozess in Systemen für die fahrzeuggenerierte Verkehrsdatengewinnung. Dabei konzentrieren sich die Untersuchungen auf die originäre Größe Geschwindigkeit. Das wesentliche Ziel der theoretischen und praktischen Untersuchungen bildet die qualifizierte Bestimmung makroskopischer Kenngrößen des Verkehrsflusses aus mikroskopischen Einzelfahrzeugdaten. Einen Schwerpunkt der Arbeit bildet die Analyse von mikroskopischen Einzelfahrzeugdaten mit Hilfe von informationsentropischen und spektral
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Jin, Goangsung. "Using Commodity Flow Data for Predicting Truck Freight Flow on State Truck Routes." BYU ScholarsArchive, 2011. https://scholarsarchive.byu.edu/etd/2867.

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The increase in truck traffic on highways has brought many problems and challenges to transportation planning and traffic operation, including traffic congestion, transportation system deficiency (insufficient truck parking, etc.), safety, infrastructure deterioration, environmental impacts (air quality and noise), economic development, and so forth. Along with the increase in truck traffic, the need for developing a statewide truck freight demand model has grown so that a state can estimate truck traffic at any point on its highways. The most significant hurdle to including freight transporta
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Krishnaswamy, Vijay. "Heuristic network generator : an expert systems approach for selection of alternative routes during incident conditions /." Thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-05022009-040559/.

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Svensk, Gustav. "TDNet : A Generative Model for Taxi Demand Prediction." Thesis, Linköpings universitet, Programvara och system, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-158514.

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Supplying the right amount of taxis in the right place at the right time is very important for taxi companies. In this paper, the machine learning model Taxi Demand Net (TDNet) is presented which predicts short-term taxi demand in different zones of a city. It is based on WaveNet which is a causal dilated convolutional neural net for time-series generation. TDNet uses historical demand from the last years and transforms features such as time of day, day of week and day of month into 26-hour taxi demand forecasts for all zones in a city. It has been applied to one city in northern Europe and on
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Scarlato, Michele. "Sicurezza di rete, analisi del traffico e monitoraggio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3223/.

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Il lavoro è stato suddiviso in tre macro-aree. Una prima riguardante un'analisi teorica di come funzionano le intrusioni, di quali software vengono utilizzati per compierle, e di come proteggersi (usando i dispositivi che in termine generico si possono riconoscere come i firewall). Una seconda macro-area che analizza un'intrusione avvenuta dall'esterno verso dei server sensibili di una rete LAN. Questa analisi viene condotta sui file catturati dalle due interfacce di rete configurate in modalità promiscua su una sonda presente nella LAN. Le interfacce sono due per potersi interfacciar
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AlGhadhban, Amer M. "Paving the Way for Next Generation Wireless Data Center Networks." Diss., 2019. http://hdl.handle.net/10754/652891.

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Data Centers (DCs) have become an intrinsic element of emerging technologies such as big data, artificial intelligence, cloud services; all of which entails interconnected and sophisticated computing and storage resources. Recent studies of conventional data center networks (DCNs) revealed two key challenges: a biased distribution of inter-rack traffic and unidentified flow classes: delay sensitive mice flows (MFs) and throughput-hungry elephant flows (EFs). Unfortunately, existing DCN topologies support only uniform distribution of capacities, provide limited bandwidth flexibilities and lacks
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Books on the topic "Traffic data generation"

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Adiguzel, Rahim Ilker. Prototype report generation facility in the Data Traffic Management System. US Army Corps of Engineers, Construction Engineering Research Laboratory, 1987.

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Yevgeni, Koucheryavy, Moltchanov Dmitri, and SpringerLink (Online service), eds. Smart Spaces and Next Generation Wired/Wireless Networking: 9th International Conference, NEW2AN 2009 and Second Conference on Smart Spaces, ruSMART 2009, St. Petersburg, Russia, September 15-18, 2009. Proceedings. Springer Berlin Heidelberg, 2009.

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Verloo, Nanke, and Luca Bertolini, eds. Seeing the City. Amsterdam University Press, 2020. http://dx.doi.org/10.5117/9789463728942.

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The city is a complex object. Some researchers look at its shape, others at its people, animals, ecology, policy, infrastructures, buildings, history, art, or technical networks. Some researchers analyse processes of in- or exclusion, gentrification, or social mobility; others biological evolution, traffic flows, or spatial development. Many combine these topics or add still more topics beyond this list. Some projects cross the boundaries of research and practice and engage in action research, while others pursue knowledge for the sake of curiosity. This volume embraces this variety of perspec
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United States. Congress. House. Committee on Government Reform. Subcommittee on Criminal Justice, Drug Policy, and Human Resources. Methamphetamine and date rape drugs: A new generation of killers : hearing before the Subcommittee on Criminal Justice, Drug Policy, and Human Resources of the Committee on Government Reform, House of Representatives, One Hundred Sixth Congress, second session, September 18, 2000. U.S. G.P.O., 2001.

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Mei, Robert D. van der., Huebner-Szabo de Bucs Frank, and Society of Photo-optical Instrumentation Engineers., eds. Performance and control of next-generation communications networks: 9-10 September, 2003, Orlando, Florida, USA. SPIE, 2003.

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United States. National Aeronautics and Space Administration., ed. MMPP Traffic Generator for the testing of the SCAR 2 Fast Packet Switch. National Aeronautics and Space Administration, 1995.

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Book chapters on the topic "Traffic data generation"

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Monil, Shah, Bhalerao Ketan, Patel Dhruvkumar, Patel Narendra, and Swadas Prashant. "Automatic Traffic Fine Generation Using Deep Learning." In Lecture Notes on Data Engineering and Communications Technologies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4474-3_53.

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Adetiloye, Taiwo, and Anjali Awasthi. "Multimodal Big Data Fusion for Traffic Congestion Prediction." In Multimodal Analytics for Next-Generation Big Data Technologies and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-97598-6_13.

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Lee, Woo Ho, Chae Sang Lim, and Bong Nam Noh. "Generation of Similar Traffic Using GAN for Resolving Data Imbalance." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9341-9_1.

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Chakraborty, Suchetana, Sandip Chakraborty, Sukumar Nandi, and Sushanta Karmakar. "Sensory Data Gathering for Road Traffic Monitoring: Energy Efficiency, Reliability, and Fault Tolerance." In Modeling and Processing for Next-Generation Big-Data Technologies. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09177-8_4.

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Mazumder, Sahisnu, Tuhin Sharma, Rahul Mitra, Nandita Sengupta, and Jaya Sil. "Generation of Sufficient Cut Points to Discretize Network Traffic Data Sets." In Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35380-2_62.

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Martyna, Jerzy. "Data Traffic Scheduling Algorithm for Multiuser MIMO-OFDM System with the Throughput Maximization." In Smart Spaces and Next Generation Wired/Wireless Networking. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14891-0_38.

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Pratik Chaudhari, Rajesh Yawle, and Pratiksha Chaudhari. "Traffic Violation Detection and Penalty Generation System at a Street Intersection." In Proceedings of the International Conference on Data Engineering and Communication Technology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1675-2_79.

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Jugurnauth, R. A., and J. Ramashire. "Forecasting Model for Voice and Internet Data Traffic During Peak Time Using Hidden Markov Model." In Smart and Sustainable Engineering for Next Generation Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18240-3_14.

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Bekmezci, İlker, and Fatih Alagöz. "Delay Sensitive and Energy Efficient Distributed Slot Assignment Algorithm for Sensor Networks Under Convergecast Data Traffic." In Next Generation Teletraffic and Wired/Wireless Advanced Networking. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759355_33.

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Lee, WooHo, BongNam Noh, YeonSu Kim, and KiMoon Jeong. "Generation of Network Traffic Using WGAN-GP and a DFT Filter for Resolving Data Imbalance." In Internet and Distributed Computing Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34914-1_29.

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Conference papers on the topic "Traffic data generation"

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Kuang, Jilong, Daniel G. Waddington, and Changhui Lin. "Techniques for fast and scalable time series traffic generation." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363747.

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Abraham, S., and N. Weller. "Next generation traffic event detection." In ICC '17: Second International Conference on Internet of Things, Data and Cloud Computing. ACM, 2017. http://dx.doi.org/10.1145/3018896.3056796.

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Tran, Khoa, and Fred Popowich. "Automatic Tweet Generation From Traffic Incident Data." In Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG 2016). Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-3512.

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Sapre, Varun, Subramaniam Kalambur, Dinkar Sitaram, and Rohit Bastian. "Synthetic Generation of Traffic Data for Urban Mobility." In 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2018. http://dx.doi.org/10.1109/icacci.2018.8554633.

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Kanaya, Nobuyuki, Yu Tsuda, Yuuki Takano, and Daisuke Inoue. "Byakko: Automatic Whitelist Generation based on Occurrence Distribution of Features of Network Traffic." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005711.

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Oaks, Shurong Liu, Zhou, Paglione, and Hughes. "Methodology for the generation of air traffic scenarios based on recorded traffic data." In 22nd Digital Avionics Systems Conference. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/dasc.2003.1245861.

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Caytiles, Ronnie, Seungyong Shin, Minji Yang, and Byungjoo Park. "A Design of Distributed Data Traffic Algorithm based on Hierarchical Wireless/Mobile Networks." In Future Generation Communication and Networking 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.117.35.

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Ayoubi, Sara, Samir Sebbah, Khaled Shaban, and Chadi Assi. "Traffic engineering in cloud data centers: A column generation approach." In NOMS 2014 - 2014 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2014. http://dx.doi.org/10.1109/noms.2014.6838351.

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Han, Yoonseon, Sin-seok Seo, Chankyou Hwang, Jae-Hyoung Yoo, and James Won-Ki Hong. "Flow-level traffic matrix generation for various data center networks." In NOMS 2014 - 2014 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2014. http://dx.doi.org/10.1109/noms.2014.6838394.

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Amara, Yacine, Abdenour Amamra, Yasmine Daheur, and Lamia Saichi. "A GIS Data Realistic Road Generation Approach for Traffic Simulation." In 2019 Federated Conference on Computer Science and Information Systems. IEEE, 2019. http://dx.doi.org/10.15439/2019f223.

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Reports on the topic "Traffic data generation"

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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c
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