Academic literature on the topic 'Traffic engineering – Data processing'
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Journal articles on the topic "Traffic engineering – Data processing"
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.
Full textTarko, 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).
Full textMallikarjuna, 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).
Full textSun, 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.
Full textZhao, 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.
Full textIvanov, 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.
Full textZhao, 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.
Full textZhihuang 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.
Full textZhou, 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.
Full textChronopoulos, 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.
Full textDissertations / Theses on the topic "Traffic engineering – Data processing"
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.
Full textLam, 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.
Full textWolf, 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.
Full textMawji, Afzal. "Achieving Scalable, Exhaustive Network Data Processing by Exploiting Parallelism." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/779.
Full textWang, Hong Feng. "IGP traffic engineering : a comparison of computational optimization algorithms." Thesis, Stellenbosch : Stellenbosch University, 2008. http://hdl.handle.net/10019.1/20877.
Full textENGLISH 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 making its way into metro, access and even some private networks. However, emerging provider practices are revealing the relevance of using IGP TE in hybrid TE models where IGP TE is combined with MPLS TE to optimize IP routing. This is done by either optimizing IGP routing while setting a few number of MPLS tunnels in the network or optimizing the management of MPLS tunnels to allow growth for the IGP traffic or optimizing both IGP and MPLS routing in a hybrid IGP+MPLS setting. The focus of this thesis is on IGP TE using heuristic algorithms borrowed from the computational intelligence research field. We present four classes of algorithms for Maximum Link Utilization (MLU) minimization. These include Genetic Algorithm (GA), Gene Expression Programming (GEP), Ant Colony Optimization (ACO), and Simulated Annealing (SA). We use these algorithms to compute a set of optimal link weights to achieve IGP TE in different settings where a set of test networks representing Europe, USA, Africa and China are used. Using NS simulation, we compare the performance of these algorithms on the test networks with various traffic profiles.
AFRIKAANSE OPSOMMING: Verkeersingenieurswese (VI) is aangedui vir gebruik in volgende generasie IP netwerke vir die gebruiksoptimering van netwerkbronne deur die daarstelling van kwaliteit van diens ooreenkomste tussen die verkeersaanbod vir die netwerk en die beskikbare netwerkbronne. VI word huidiglik algemeen bewerkstellig deur drie metodes, insluitend (1) IGP VI gebruikmakend van verbindingslose roete-optimering, (2) MPLS VI gebruikmakend van verbindingsvaste roete-optimering en (3) hibriede VI wat IGP VI en MPLS VI kombineer. MPLS is die mees algemene, en word ook aangewend in metro, toegang en selfs sommige privaatnetwerke. Nuwe verskaffer-praktyke toon egter die relevansie van die gebruik van IGP VI in hibriede VI modelle, waar IGP VI gekombineer word met MPLS VI om IP roetering te optimeer. Dit word gedoen deur `of optimering van IGP roetering terwyl ’n paar MPLS tonnels in die netwerk gestel word, `of optimering van die bestuur van MPLS tonnels om toe te laat vir groei in die IGP verkeer `of die optimering van beide IGP en MPLS roetering in ’n hibriede IGP en MPLS situasie. Die fokus van hierdie tesis is op IGP VI gebruikmakend van heuristieke algoritmes wat ontleen word vanuit die berekeningsintelligensie navorsingsveld. Ons beskou vier klasse van algoritmes vir Maksimum Verbindingsgebruik (MVG) minimering. Dit sluit in genetiese algoritmes, geen-uitdrukkingsprogrammering, mierkoloniemaksimering and gesimuleerde temperoptimering. Ons gebruik hierdie algoritmes om ’n versameling optimale verbindingsgewigte te bereken om IGP VI te bereik in verskillende situasies, waar ’n versameling toetsnetwerke gebruik is wat Europa, VSA, Afrika en China verteenwoordig. Gebruikmakende van NS simulasie, vergelyk ons die werkverrigting van hierdie algoritmes op die toetsnetwerke, met verskillende verkeersprofiele.
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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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.
Full textBachelors
Engineering
Computer Engineering
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.
Full textKumar, Saurabh. "Real-Time Road Traffic Events Detection and Geo-Parsing." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10842958.
Full textIn the 21st 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. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis.
Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents.
Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis.
Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture.
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.
Full textBooks on the topic "Traffic engineering – Data processing"
Computer image processing in traffic engineering. Taunton, Somerset, England: Research Studies Press, 1991.
Find full textYoung, W. Microcomputers in traffic engineering. Taunton, Somerset, England: Research Studies Press, 1989.
Find full textAbawajy, Jemal H. Network and traffic engineering in emerging distributed computing applications. Hershey, PA: Information Science Reference, 2013.
Find full text1951-, Bonsall P. W., and Young W. 1949-, eds. Understanding traffic systems: Data, analysis, and presentation. Aldershot: Ashgate, 2000.
Find full text1951-, Bonsall P. W., and Young W. 1949-, eds. Understanding traffic systems: Data, analysis, and presentation. Aldershot: Avebury Technical, 1996.
Find full textTaylor, M. A. P. Traffic analysis: New technology & new solutions. North Melbourne, Victoria, Australia: Hargreen Pub. Co., 1988.
Find full textBlay, D. R. Evaluation of the FREQ7PE freeway traffic simulation program. Downsview, Ont: Research and Development Branch, Ontario Ministry of Transportation, 1988.
Find full textC, 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.
Find full textHeureka '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.
Find full textGao 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.
Find full textBook chapters on the topic "Traffic engineering – Data processing"
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.
Full textWillmott, 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.
Full textTarefder, 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.
Full textMolnár, 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.
Full textGarcía-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.
Full textFurutani, 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.
Full textTrinh, 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.
Full textBerns, 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.
Full textGodse, 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.
Full textAwange, 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.
Full textConference papers on the topic "Traffic engineering – Data processing"
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.
Full textKriegel, 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.
Full textJiang, 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.
Full textNejad, 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.
Full textLiu, 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.
Full textBlackman, 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.
Full textLi, 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.
Full textYadav, 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.
Full textZhang, 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.
Full textFigueiras, 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.
Full textReports on the topic "Traffic engineering – Data processing"
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.
Full textLiu, 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.
Full textGao, 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.
Full textChien, 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.
Full textRinghand, 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.
Full textBäumler, 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.
Full textHall, 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.
Full textTarko, 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.
Full textLi, 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.
Full textKress, 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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