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Bibliografie tematiche / Traffic engineering – Data processing

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Letteratura scientifica selezionata sul tema "Traffic engineering – Data processing"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 30 gennaio 2023

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Articoli di riviste sul tema "Traffic engineering – Data processing"

1

Knoop, Victor L., Serge P. Hoogendoorn, and Henk J. van Zuylen. "Processing Traffic Data Collected by Remote Sensing." Transportation Research Record: Journal of the Transportation Research Board 2129, no. 1 (January 2009): 55–61. http://dx.doi.org/10.3141/2129-07.

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Tarko, Andrzej P., and Nagui M. Rouphail. "Intelligent Traffic Data Processing for ITS Applications." Journal of Transportation Engineering 123, no. 4 (July 1997): 298–307. http://dx.doi.org/10.1061/(asce)0733-947x(1997)123:4(298).

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3

Mallikarjuna, C., A. Phanindra, and K. Ramachandra Rao. "Traffic Data Collection under Mixed Traffic Conditions Using Video Image Processing." Journal of Transportation Engineering 135, no. 4 (April 2009): 174–82. http://dx.doi.org/10.1061/(asce)0733-947x(2009)135:4(174).

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4

Sun, Yuan, Hao Xu, Jianqing Wu, Jianying Zheng, and Kurt M. Dietrich. "3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (June 8, 2018): 14–22. http://dx.doi.org/10.1177/0361198118775839.

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Abstract (sommario):

High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. T

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5

Zhao, Liangbin, Guoyou Shi, and Jiaxuan Yang. "Ship Trajectories Pre-processing Based on AIS Data." Journal of Navigation 71, no. 5 (April 22, 2018): 1210–30. http://dx.doi.org/10.1017/s0373463318000188.

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Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands

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Ivanov, Alexander, and Alexander Platov. "Environmental monitoring based on data processing of Internet of Things." E3S Web of Conferences 136 (2019): 01041. http://dx.doi.org/10.1051/e3sconf/201913601041.

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The concept of online monitoring of the urban environment is proposed. It is based on the online processing of hydrometeorological and traffic information received through the Internet of Things. The traditional approach of the Internet of things includes transfer and storage of huge arrays of measurements in digital form. This concept of online monitoring is primarily an analysis, evaluation of the results of processing information received from wireless networks. The concept was implemented at Nizhny Novgorod State University of Architecture and Civil Engineering in several services includin

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7

Zhao, Ming, Norman W. Garrick, and Luke E. K. Achenie. "Data Reconciliation–Based Traffic Count Analysis System." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (January 1998): 12–17. http://dx.doi.org/10.3141/1625-02.

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Traffic volume data, especially average annual daily traffic (AADT), are important in transportation engineering. They are required in managing and maintaining existing facilities and in planning and designing new facilities. Many state highway agencies use the ramp counting procedure described in FHWA’s Traffic Monitoring Guide to estimate AADTs for freeways. The procedure involves counting all entrance and exit ramps between two established mainline counters (anchor points) and then reconciling the count data to estimate mainline AADT. The reconciling of count data includes three steps. Firs

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Zhihuang Jiang. "Traffic Operation Data Analysis and Information Processing Based on Data Mining." Automatic Control and Computer Sciences 53, no. 3 (May 2019): 244–52. http://dx.doi.org/10.3103/s0146411619030040.

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Zhou, Xin. "Research on Front-End Fusion Processing Technology of Traffic Scenes." Journal of Architectural Research and Development 6, no. 2 (March 4, 2022): 1–7. http://dx.doi.org/10.26689/jard.v6i2.3707.

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Abstract (sommario):

With the intelligent development of road traffic control and management, higher requirements for the accuracy and effectiveness of traffic data have been put forward. The issue of how to collect and integrate data for traffic scenes has sought importance in this field as various treatment technologies have emerged. A lot of research work have been carried out from the theoretical aspect to engineering application.

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Chronopoulos, Anthony Theodore, and Gang Wang. "Traffic Flow Simulation through Parallel Processing." Transportation Research Record: Journal of the Transportation Research Board 1566, no. 1 (January 1996): 31–38. http://dx.doi.org/10.1177/0361198196156600104.

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Numerical methods for solving traffic flow continuum models have been studied and efficiently implemented in traffic simulation codes in the past. Explicit and implicit methods have been used in traffic simulation codes in the past. Implicit methods allow a much larger time step size than explicit methods to achieve the same accuracy. However, at each time step a nonlinear system must be solved. The Newton method, coupled with a linear iterative method (Orthomin), is used. The efficient implementation of explicit and implicit numerical methods for solving the high-order flow conservation traff

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Tesi sul tema "Traffic engineering – Data processing"

1

賴翰笙 and Hon-seng Lai. "An effective methodology for visual traffic surveillance." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B30456708.

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Lam, Fung, and 林峰. "Internet inter-domain traffic engineering and optimizatioon." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31224581.

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3

Wolf, Jean Louise. "Using GPS data loggers to replace travel diaries in the collection of travel data." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/20203.

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Mawji, Afzal. "Achieving Scalable, Exhaustive Network Data Processing by Exploiting Parallelism." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/779.

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Telecommunications companies (telcos) and Internet Service Providers (ISPs) monitor the traffic passing through their networks for the purposes of network evaluation and planning for future growth. Most monitoring techniques currently use a form of packet sampling. However, exhaustive monitoring is a preferable solution because it ensures accurate traffic characterization and also allows encoding operations, such as compression and encryption, to be performed. To overcome the very high computational cost of exhaustive monitoring and encoding of data, this thesis suggests exploiting p

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Wang, Hong Feng. "IGP traffic engineering : a comparison of computational optimization algorithms." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/20877.

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Thesis (MSc)--Stellenbosch University, 2008.<br>ENGLISH ABSTRACT: Traffic Engineering (TE) is intended to be used in next generation IP networks to optimize the usage of network resources by effecting QoS agreements between the traffic offered to the network and the available network resources. TE is currently performed by the IP community using three methods including (1) IGP TE using connectionless routing optimization (2) MPLS TE using connection-oriented routing optimization and (3) Hybrid TE combining IGP TE with MPLS TE. MPLS has won the battle of the core of the Internet and is ma

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Hwang, Kuo-Ping. "Applying heuristic traffic assignment in natural disaster evacuation: a decision support system." Diss., Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/54455.

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The goal of this research is to develop a heuristic traffic assignment method to simulate the traffic flow of a transportation network at a real-time speed. The existing assignment methods are reviewed and a heuristic path-recording assignment method is proposed. Using the new heuristic assignment method, trips are loaded onto the network in a probabilistic approach for the first iteration; paths are recorded, and path impedance is computed as the basis for further assignment iteration. The real-time traffic assignment model developed with the new assignment method is called HEUPRAE. The diffe

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Trinh, Viet. "Using voicexml to provide real-time traffic information." Honors in the Major Thesis, University of Central Florida, 2002. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/307.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.<br>Bachelors<br>Engineering<br>Computer Engineering

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Smith, Katie S. "A profile of HOV lane vehicle characteristics on I-85 prior to HOV-to-HOT conversion." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42923.

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The conversion of high-occupancy vehicle (HOV) lanes to high-occupancy toll (HOT) lanes is currently being implemented in metro Atlanta on a demonstration basis and is under consideration for more widespread adoption throughout the metro region. Further conversion of HOV lanes to HOT lanes is a major policy decision that depends on knowledge of the likely impacts, including the equity of the new HOT lane. Rather than estimating these impacts using modeling or surveys, this study collects revealed preference data in the form of observed vehicle license plate data and vehicle occupancy data fr

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Kumar, Saurabh. "Real-Time Road Traffic Events Detection and Geo-Parsing." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842958.

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<p> In the 21<sup>st</sup> century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Anoth

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Glick, Travis Bradley. "Utilizing High-Resolution Archived Transit Data to Study Before-and-After Travel-Speed and Travel-Time Conditions." PDXScholar, 2017. https://pdxscholar.library.pdx.edu/open_access_etds/4065.

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Travel times, operating speeds, and service reliability influence costs and service attractiveness. This paper outlines an approach to quantify how these metrics change after a modification of roadway design or transit routes using archived transit data. The Tri-County Metropolitan Transportation District of Oregon (TriMet), Portland's public transportation provider, archives automatic vehicle location (AVL) data for all buses as part of their bus dispatch system (BDS). This research combines three types of AVL data (stop event, stop disturbance, and high-resolution) to create a detailed accou

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Libri sul tema "Traffic engineering – Data processing"

1

Computer image processing in traffic engineering. Taunton, Somerset, England: Research Studies Press, 1991.

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Young, W. Microcomputers in traffic engineering. Taunton, Somerset, England: Research Studies Press, 1989.

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Abawajy, Jemal H. Network and traffic engineering in emerging distributed computing applications. Hershey, PA: Information Science Reference, 2013.

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4

1951-, Bonsall P. W., and Young W. 1949-, eds. Understanding traffic systems: Data, analysis, and presentation. Aldershot: Ashgate, 2000.

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1951-, Bonsall P. W., and Young W. 1949-, eds. Understanding traffic systems: Data, analysis, and presentation. Aldershot: Avebury Technical, 1996.

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6

Taylor, M. A. P. Traffic analysis: New technology & new solutions. North Melbourne, Victoria, Australia: Hargreen Pub. Co., 1988.

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Blay, D. R. Evaluation of the FREQ7PE freeway traffic simulation program. Downsview, Ont: Research and Development Branch, Ontario Ministry of Transportation, 1988.

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8

C, Bazzan Ana L., and Klügl Franziska, eds. Handbook of research on multi-agent systems for traffic and transportation engineering. Hershey, PA: Information Science Reference, 2009.

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9

Heureka '90 (1990 Karlsruhe, Germany). Optimierung in Verkehr und Transport: Vortragsveranstaltung mit Vorstellung von Hard- und Software, Karlsruhe 5./6. April 1990 : Tagungsbericht. [Köln]: Die Forschungsgesellschaft, 1990.

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Gao su gong lu jiao tong yun xing zhuang tai fen xi fang fa ji ying yong. Nanjing Shi: Dong nan da xue chu ban she, 2012.

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Capitoli di libri sul tema "Traffic engineering – Data processing"

1

Sanghavi, Jeet D., Alay M. Shah, Saurabh S. Rane, and V. Venkataramanan. "Smart Traffic Density Management System Using Image Processing." In Lecture Notes on Data Engineering and Communications Technologies, 301–12. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8339-6_33.

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Willmott, S. C. "Design of A Flight and Radar Data Processing System for the Support of Air Traffic Control." In Software Engineering for Large Software Systems, 122–40. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0771-3_5.

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3

Tarefder, Rafiqul, and James Brogan. "A Review of Statewide Traffic Data Collection, Processing, Projection and Quality Control." In Traffic Management, 1–18. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307822.ch1.

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4

Molnár, P., and T. R. Collins. "Traffic Data Collection Using Image Processing Technology." In Traffic and Granular Flow ’99, 357–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59751-0_35.

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García-Dorado, José Luis, Felipe Mata, Javier Ramos, Pedro M. Santiago del Río, Victor Moreno, and Javier Aracil. "High-Performance Network Traffic Processing Systems Using Commodity Hardware." In Data Traffic Monitoring and Analysis, 3–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36784-7_1.

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Furutani, Nobuaki, Jun Kitazono, Seiichi Ozawa, Tao Ban, Junji Nakazato, and Jumpei Shimamura. "Adaptive DDoS-Event Detection from Big Darknet Traffic Data." In Neural Information Processing, 376–83. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_45.

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Trinh, Thong Duc, Dinh Khanh Nguyen Diep, and Vinh Dinh Nguyen. "Robust Median-Ternary Pattern for Traffic Light Detection." In Artificial Intelligence in Data and Big Data Processing, 485–94. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_38.

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Berns, Karsten, Alexander Köpper, and Bernd Schürmann. "Sensor Data Processing." In Lecture Notes in Electrical Engineering, 227–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65157-2_8.

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Godse, Jay. "Reverse-Engineering Complex Solutions." In Ruby Data Processing, 89–96. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3474-7_4.

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Awange, Joseph L. "Data Processing and Adjustment." In Environmental Science and Engineering, 91–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-88256-5_6.

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Atti di convegni sul tema "Traffic engineering – Data processing"

1

Lu, B., L. Yang, and A. Qin. "Mobile Traffic Data Collection/Processing and Its Applications." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)627.

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2

Kriegel, Hans-Peter, Peer Kroger, Peter Kunath, Matthias Renz, and Tim Schmidt. "Efficient Query Processing in Large Traffic Networks." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497586.

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Jiang, Jia, Hong Li, and Rui Xiao. "Error Processing on the Real-Time Traffic Data." In 2010 International Conference on Intelligent System Design and Engineering Application (ISDEA). IEEE, 2010. http://dx.doi.org/10.1109/isdea.2010.127.

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4

Nejad, Emad Soltani, and Mohammad Reza Majma. "RMTE: Robust modular traffic engineering in software-defined data center networks." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090216.

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Liu, Nan, Peng Peng, Zuzhi Shen, Haihang Han, and Mingrong Deng. "Optimization of Traffic Information Processing Based on Data Mining from GPS Historical Data." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)584.

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Blackman, Samuel S., M. T. Busch, S. W. Cantrell, and C. J. Musial. "Applications of signal and data processing technology to vehicular traffic monitoring." In Optical Engineering and Photonics in Aerospace Sensing, edited by Oliver E. Drummond. SPIE, 1993. http://dx.doi.org/10.1117/12.157791.

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Li, Jianping, and Han Chen. "Research on Abnormal Data Analysis and Data Processing Method Based on Network Traffic." In 2022 IEEE International Conference on Computation, Big-Data and Engineering (ICCBE). IEEE, 2022. http://dx.doi.org/10.1109/iccbe56101.2022.9888172.

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Yadav, Mukesh, Kasturi Ghadge, Nitya Shetty, Deepa Chanchlani, Priyanka Narwani, and Richard Joseph. "Smart Road Traffic Handler: Cause Identification and Resolution Using Image Processing." In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2019. http://dx.doi.org/10.1109/confluence.2019.8776938.

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Zhang, Ming, Shisheng Wang, Yu jing Cai, and Xiubin Xuan. "Intelligent processing system for equipment monitoring data of urban rail transit based on IOT perception." In 5th International Conference on Traffic Engineering and Transportation System (ICTETS 2021), edited by Yongkang Xing. SPIE, 2021. http://dx.doi.org/10.1117/12.2619774.

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Figueiras, Paulo, Ricardo Silva, André Ramos, Guilherme Guerreiro, Ruben Costa, and Ricardo Jardim-Goncalves. "Big Data Processing and Storage Framework for ITS: A Case Study on Dynamic Tolling." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-68069.

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Urban and national road networks in many countries are severely congested, resulting in increased travel times, unexpected delays, greater travel costs, worsening air pollution and noise levels, and a greater number of traffic accidents. Expanding traffic network capacities by building more roads is both extremely costly and harmful to the environment. By far the best way to accommodate growing travel demand is to make more efficient use of existing networks. Portugal has a good but underused toll highway network that runs near to an urban/national road network that is free to use but congeste

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Rapporti di organizzazioni sul tema "Traffic engineering – Data processing"

1

Saad, T., R. Gandhi, X. Liu, V. Beeram, and I. Bryskin. Common YANG Data Types for Traffic Engineering. RFC Editor, June 2020. http://dx.doi.org/10.17487/rfc8776.

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Liu, X., I. Bryskin, V. Beeram, T. Saad, H. Shah, and O. Gonzalez de Dios. YANG Data Model for Traffic Engineering (TE) Topologies. RFC Editor, August 2020. http://dx.doi.org/10.17487/rfc8795.

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Gao, J., G. Xie, and R. Papneja. Label Switched Path (LSP) Data Path Delay Metrics in Generalized MPLS and MPLS Traffic Engineering (MPLS-TE) Networks. Edited by W. Sun and G. Zhang. RFC Editor, November 2012. http://dx.doi.org/10.17487/rfc6777.

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Chien, Stanley, Lauren Christopher, Yaobin Chen, Mei Qiu, and Wei Lin. Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT's Traffic Management System. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317400.

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The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and

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Ringhand, Madlen, Maximilian Bäumler, Christian Siebke, Marcus Mai, and Felix Elrod. Report on validation of the stochastic traffic simulation (Part A). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.242.

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This document is intended to give an overview of the human subject study in a driving simulator that was conducted by the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD) to provide the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD with the necessary input for the validation of a stochastic traffic simulation, especially for the parameterization, consolidation, and validation of driver behaviour models. VPSY planned, conducted, and analysed a driving simulator study. The main purpose of the s

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Bäumler, Maximilian, Madlen Ringhand, Christian Siebke, Marcus Mai, Felix Elrod, and Günther Prokop. Report on validation of the stochastic traffic simulation (Part B). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.243.

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This document is intended to give an overview of the validation of the human subject study, conducted in the driving simulator of the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD), as well of the validation of the stochastic traffic simulation developed in the AutoDrive project by the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD. Furthermore, the evaluation process of a C-AEB (Cooperative-Automatic Emergency Brake) system is demonstrated. The main purpose was to compare the driving behavi

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Hall, Candice, and Robert Jensen. Utilizing data from the NOAA National Data Buoy Center. Engineer Research and Development Center (U.S.), March 2021. http://dx.doi.org/10.21079/11681/40059.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) guides users through the quality control (QC) and processing steps that are necessary when using archived U.S. National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) wave and meteorological data. This CHETN summarizes methodologies to geographically clean and QC NDBC measurement data for use by the U.S. Army Corps of Engineers (USACE) user community.

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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Cristhian Lizarazo. TScan–Stationary LiDAR for Traffic and Safety Applications: Vehicle Interpretation and Tracking. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317402.

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To improve traffic performance and safety, the ability to measure traffic accurately and effectively, including motorists and other vulnerable road users, at road intersections is needed. A past study conducted by the Center for Road Safety has demonstrated that it is feasible to detect and track various types of road users using a LiDAR-based system called TScan. This project aimed to progress towards a real-world implementation of TScan by building two trailer-based prototypes with full end-user documentation. The previously developed detection and tracking algorithms have been modified and

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Li, Howell, Jijo K. Mathew, Woosung Kim, and Darcy M. Bullock. Using Crowdsourced Vehicle Braking Data to Identify Roadway Hazards. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317272.

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Modern vehicles know more about the road conditions than transportation agencies. Enhanced vehicle data that provides information on “close calls” such as hard braking events or road conditions during winter such as wheel slips and traction control will be critical for improving safety and traffic operations. This research applied conflict analyses techniques to process approximately 1.5 million hard braking events that occurred in the state of Indiana over a period of one week in August 2019. The study looked at work zones, signalized intersections, interchanges and entry/exit ramps. Qualitat

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Kress, Marin, David Young, Katherine Chambers, and Brandan Scully. AIS data case study : quantifying connectivity for six Great Lakes port areas from 2015 through 2018. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40720.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) presents results from a preliminary examination of commercial vessel traffic connectivity between six major port areas on the Great Lakes using Automatic Identification System (AIS) data collected from 2015 to 2018. The six port areas included in this study are Calumet Harbor, IL and IN; Cleveland, OH; Detroit, MI; Duluth-Superior, MN and WI; Indiana Harbor, IN; and Two Harbors, MN. These six locations represent an important subset of the more than 100 federally authorized navigation projects in the Great Lakes maintained by the US

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