Literatura académica sobre el tema "TRAFFIC CONGESTION DETECTION"
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Artículos de revistas sobre el tema "TRAFFIC CONGESTION DETECTION"
Wiseman, Yair. "Computerized Traffic Congestion Detection System". International Journal of Transportation and Logistics Management 1, n.º 1 (30 de diciembre de 2017): 1–8. http://dx.doi.org/10.21742/ijtlm.2017.1.1.01.
Texto completoIdura Ramli, Nurshahrily y Mohd Izani Mohamed Rawi. "An overview of traffic congestion detection and classification techniques in VANET". Indonesian Journal of Electrical Engineering and Computer Science 20, n.º 1 (1 de octubre de 2020): 437. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp437-444.
Texto completoXiang, Yingxiao, Wenjia Niu, Endong Tong, Yike Li, Bowei Jia, Yalun Wu, Jiqiang Liu, Liang Chang y Gang Li. "Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods". Security and Communication Networks 2021 (31 de octubre de 2021): 1–17. http://dx.doi.org/10.1155/2021/1632825.
Texto completoWang, Chao. "An Effective Congestion Control Algorithm based on Traffic Assignment and Reassignment in Wireless Sensor Network". Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, n.º 8 (3 de diciembre de 2020): 1166–74. http://dx.doi.org/10.2174/2352096513999200628095848.
Texto completoKayarga, Tanuja y H. M. Navyashree. "A Novel Framework to Control and Optimize the Traffic Congestion Issue in VANET". International Journal of Engineering & Technology 7, n.º 2.31 (24 de agosto de 2018): 245. http://dx.doi.org/10.14419/ijet.v7i3.31.18234.
Texto completoEl-Sersy, Heba y Ayman El-Sayed. "Survey of Traffic Congestion Detection using VANET". Communications on Applied Electronics 1, n.º 4 (26 de marzo de 2015): 14–20. http://dx.doi.org/10.5120/cae-1520.
Texto completoCherkaoui, Badreddine, Abderrahim Beni-Hssane, Mohamed El Fissaoui y Mohammed Erritali. "Road traffic congestion detection in VANET networks". Procedia Computer Science 151 (2019): 1158–63. http://dx.doi.org/10.1016/j.procs.2019.04.165.
Texto completoKalinic, Maja y Jukka M. Krisp. "Fuzzy inference approach in traffic congestion detection". Annals of GIS 25, n.º 4 (2 de octubre de 2019): 329–36. http://dx.doi.org/10.1080/19475683.2019.1675760.
Texto completoBhanja, Urmila, Anita Mohanty y Bhagyashree Das. "Embedded based real time traffic congestion detection". International Journal of Vehicle Information and Communication Systems 3, n.º 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.094976.
Texto completoMohanty, Anita, Bhagyashree Das y Urmila Bhanja. "Embedded based real time traffic congestion detection". International Journal of Vehicle Information and Communication Systems 3, n.º 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.10016393.
Texto completoTesis sobre el tema "TRAFFIC CONGESTION DETECTION"
Thorri, Sigurdsson Thorsteinn. "Road traffic congestion detection and tracking with Spark Streaming analytics". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254874.
Texto completoVägtrafikstockningar orsakar flera problem. Till exempel utgör långsam trafik i överbelastade områden en säkerhetsrisk för fordon som närmar sig den överbelastade regionen och ökade pendeltider leder till ökade transportkostnader och ökad förorening.Arbetet i denna avhandling syftar till att upptäcka och spåra trafikstockningar i realtid. Detektering av vägtrafiken i realtid är viktigt för att möjliggöra mekanismer för att t.ex. förbättra trafiksäkerheten genom att skicka avancerade varningar till förare som närmar sig en överbelastad region och för att mildra trängsel genom att kontrollera adaptiva hastighetsgränser. Dessutom kan spårningen av trängselutveckling i tid och rum vara en värdefull inverkan på utvecklingen av vägnätet. Trafikavkännare i Stockholms vägnät representeras som en riktad vägd graf och problemet med överbelastningsdetektering är formulerat som ett problem med behandling av flödesgrafer. Den anslutna komponentalgoritmen och befintliga grafbehandlingsalgoritmer som ursprungligen användes för communitydetektering i sociala nätgravar är anpassade för uppgiften att detektera vägtäthet. Resultaten indikerar att en överbelastningsdetekteringsmetod baserad på den strömmande anslutna komponentalgoritmen och den inkrementella Dengraph communitydetekteringsalgoritmen kan upptäcka överbelastning med noggrannhet i bästa fall upp till 94% för anslutna komponenter och upp till 88% för Dengraph. En metod baserad på hierarkisk klustring kan detektera överbelastning men saknar detaljer som shockwaves, och Louvain modularitetsalgoritmen för communitydetektering misslyckas med att detektera överbelastade områden i trafiksensorns graf.Slutligen utvärderas prestandan hos de implementerade strömmalgoritmerna med hänsyn till systemets realtidskrav, deras genomströmning och minnesfotavtryck.
RezaeiDivkolaei, Pouya. "DETECTION, CLASSIFICATION, AND LOCATION IDENTIFICATION OF TRAFFIC CONGESTION FROM TWITTER STREAM ANALYSIS". OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2257.
Texto completoAnbaroglu, B. "Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks". Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1408826/.
Texto completoKhatri, Chandra P. "Real-time road traffic information detection through social media". Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53889.
Texto completoRui, Zhu. "Moving Object Trajectory Based Intelligent Traffic Information Hub". Thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134944.
Texto completoSwaro, James E. "A Heuristic-Based Approach to Real-Time TCP State and Retransmission Analysis". Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1448030769.
Texto completoSvanberg, John. "Anomaly detection for non-recurring traffic congestions using Long short-term memory networks (LSTMs)". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234465.
Texto completoI den här masteruppsatsen implementerar vi en tvåstegsalgoritm för avvikelsedetektering för icke återkommande trafikstockningar. Data är insamlad från kollektivtrafikbussarna i Stockholm. Vi undersöker användningen av maskininlärning för att modellerna tidsseriedata med hjälp av LSTM-nätverk och evaluerar sedan dessa resultat med en grundmodell. Avvikelsedetekteringsalgoritmen inkluderar både kollektiv och kontextuell uttrycksfullhet, vilket innebär att kollektiva förseningar kan hittas och att även temporaliteten hos datan beaktas. Resultaten visar att prestandan hos avvikelsedetekteringen förbättras av mindre prediktionsfel genererade av LSTM-nätverket i jämförelse med grundmodellen. En regel för avvikelser baserad på snittet av två andra regler reducerar märkbart antalet falska positiva medan den höll kvar antalet sanna positiva på en tillräckligt hög nivå. Prestandan hos avvikelsedetekteringsalgoritmen har setts bero av vilken vägsträcka den tillämpas på, där några vägsträckor är svårare medan andra är lättare för avvikelsedetekteringen. Den bästa varianten av algoritmen hittade 84.3 % av alla avvikelser och 96.0 % av all avvikelsefri data blev markerad som normal data.
Loureiro, Pedro Fernando Quintas. "Automatic traffic congestion detection using uncontrolled video sources". Master's thesis, 2009. http://hdl.handle.net/10216/58176.
Texto completoLoureiro, Pedro Fernando Quintas. "Automatic traffic congestion detection using uncontrolled video sources". Dissertação, 2009. http://hdl.handle.net/10216/58176.
Texto completoANAS, MOHD. "TRAFFIC CONGESTION DETECTION USING DATA MINING IN VANET". Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16357.
Texto completoLibros sobre el tema "TRAFFIC CONGESTION DETECTION"
Paselk, Theodore Alan. Automated vehicle delay estimation and motorist information at the U.S./Canadian Border: Final technical report, Research Project GC 8719, Task 41, Automated Motorist Information Detection System. [Olympia, Wash.?]: Washington State Dept. of Transportation, Washington State Transportation Commission in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1992.
Buscar texto completoHallenbeck, Mark E. Use of automatic vehicle identification techniques for measuring traffic performance and performing incident detection: Final report. [Olympia, Wash.?]: TransNow, Transportation Northwest, University Transportation Centers Program, Federal Region Ten, Washington State Dept. of Transportation, Transit, Research, and Intermodal Planning Division, in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1992.
Buscar texto completoA busy day in Busytown. New York, N.Y: Simon Spotlight, 2010.
Buscar texto completoSantibañez Gruber, Rosa Maria y Antonia Caro González, eds. DEUSTO Social Impact Briefings No. 4 (2019). University of Deusto, 2020. http://dx.doi.org/10.18543/dsib-4(2020).
Texto completoCapítulos de libros sobre el tema "TRAFFIC CONGESTION DETECTION"
Noori, Mohammed Ahsan Raza y Ritika Mehra. "Traffic Congestion Detection from Twitter Using word2vec". En ICT Analysis and Applications, 527–34. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8354-4_52.
Texto completoKumar, Tarun y Dharmender Singh Kushwaha. "An Approach for Traffic Congestion Detection and Traffic Control System". En Information and Communication Technology for Competitive Strategies, 99–108. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0586-3_10.
Texto completoChetouane, Ameni, Sabra Mabrouk y Mohamed Mosbah. "Traffic Congestion Detection: Solutions, Open Issues and Challenges". En Communications in Computer and Information Science, 3–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65810-6_1.
Texto completoKhalifa, Othman O., Azri A. Marzuki, Noreha Abdul Malik y Mohammad H. Hassan Gani. "Traffic Congestion Detection for Smart and Control Transportation Management". En Lecture Notes in Electrical Engineering, 317–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2406-3_25.
Texto completoCheng, Jieren, Boyi Liu y Xiangyan Tang. "A Traffic-Congestion Detection Method for Bad Weather Based on Traffic Video". En Communications in Computer and Information Science, 506–18. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0356-1_54.
Texto completoShaikh, Faisal Karim, Mohsin Shah, Bushra Shaikh y Roshan Ahmed Shaikh. "Implementation and Evaluation of Vehicle-to-Vehicle Traffic Congestion Detection". En Communications in Computer and Information Science, 227–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10987-9_21.
Texto completoSong, Yang, Zhuzhu Wang, Junwei Zhang, Zhuo Ma y Jianfeng Ma. "A Decentralized Weighted Vote Traffic Congestion Detection Framework for ITS". En Communications in Computer and Information Science, 249–62. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9129-7_18.
Texto completoZhuo, Yedi, Ping Wang, Jiaojiao Sun y Yinli Jin. "Traffic Congestion Detection Based on the Image Classification with CNN". En Lecture Notes in Electrical Engineering, 4569–80. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_378.
Texto completoOumaima, El Joubari, Ben Othman Jalel y Vèque Véronique. "A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways". En Ad Hoc Networks, 87–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67369-7_7.
Texto completoAlomari, Ebtesam, Rashid Mehmood y Iyad Katib. "Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection". En Smart Infrastructure and Applications, 37–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13705-2_2.
Texto completoActas de conferencias sobre el tema "TRAFFIC CONGESTION DETECTION"
Palmer, J. P. "Automatic incident detection and improved traffic control in urban areas". En IEE Colloquium on Urban Congestion Management. IEE, 1995. http://dx.doi.org/10.1049/ic:19951296.
Texto completoNidhal, Ahmed, Umi Kalthum Ngah y Widad Ismail. "Real time traffic congestion detection system". En 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2014. http://dx.doi.org/10.1109/icias.2014.6869538.
Texto completoG., Raji C., Shamna Shirin K, Murshidha, Fathimathul Fasila V. P y Shiljiya Shirin K. T. "Emergency Vehicles Detection during Traffic Congestion". En 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2022. http://dx.doi.org/10.1109/icoei53556.2022.9776942.
Texto completoAnjum, Nimra, Nasreen Badruddin y Micheal Drieberg. "Simulation of traffic congestion detection using VANETs". En 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2014. http://dx.doi.org/10.1109/icias.2014.6869475.
Texto completoDimri, Anuj, Harsimran Singh, Naveen Aggarwal, Bhaskaran Raman, Diyva Bansal y K. K. Ramakrishnan. "RoadSphygmo: Using barometer for traffic congestion detection". En 2016 8th International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2016. http://dx.doi.org/10.1109/comsnets.2016.7439942.
Texto completoLiu, Tingrang y Min Zhao. "The 3D McMaster Algorithm for Traffic Congestion Detection". En 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164882.
Texto completoKhalil, Mudassir, Jianping Li, Abida Sharif y Jalaluddin Khan. "Traffic congestion detection by use of satellites view". En 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2017. http://dx.doi.org/10.1109/iccwamtip.2017.8301495.
Texto completoSommer, Matthias y Jörg Hähner. "Learning Classifier Systems for Road Traffic Congestion Detection". En 3rd International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006214101420150.
Texto completoManjoro, Wellington Simbarashe, Mradul Dhakar y Brijesh Kumar Chaurasia. "Traffic congestion detection using data mining in VANET". En 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2016. http://dx.doi.org/10.1109/sceecs.2016.7509347.
Texto completoRao, Aditya, Akshay Phadnis, Atul Patil, Tejal Rajput y Pravin Futane. "Dynamic Traffic System Based on Real Time Detection of Traffic Congestion". En 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697838.
Texto completoInformes sobre el tema "TRAFFIC CONGESTION DETECTION"
System Monitoring of Auto Traffic: Queue Detection and Congestion Impact Assessment. Tampa, FL: University of South Florida, abril de 2022. http://dx.doi.org/10.5038/cutr-nicr-rr-2022-1-3.
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