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Статті в журналах з теми "TRAFFIC CONGESTION DETECTION"
Wiseman, Yair. "Computerized Traffic Congestion Detection System." International Journal of Transportation and Logistics Management 1, no. 1 (December 30, 2017): 1–8. http://dx.doi.org/10.21742/ijtlm.2017.1.1.01.
Повний текст джерелаIdura Ramli, Nurshahrily, and Mohd Izani Mohamed Rawi. "An overview of traffic congestion detection and classification techniques in VANET." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 437. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp437-444.
Повний текст джерелаXiang, Yingxiao, Wenjia Niu, Endong Tong, Yike Li, Bowei Jia, Yalun Wu, Jiqiang Liu, Liang Chang, and Gang Li. "Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods." Security and Communication Networks 2021 (October 31, 2021): 1–17. http://dx.doi.org/10.1155/2021/1632825.
Повний текст джерелаWang, 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, no. 8 (December 3, 2020): 1166–74. http://dx.doi.org/10.2174/2352096513999200628095848.
Повний текст джерелаKayarga, Tanuja, and H. M. Navyashree. "A Novel Framework to Control and Optimize the Traffic Congestion Issue in VANET." International Journal of Engineering & Technology 7, no. 2.31 (August 24, 2018): 245. http://dx.doi.org/10.14419/ijet.v7i3.31.18234.
Повний текст джерелаEl-Sersy, Heba, and Ayman El-Sayed. "Survey of Traffic Congestion Detection using VANET." Communications on Applied Electronics 1, no. 4 (March 26, 2015): 14–20. http://dx.doi.org/10.5120/cae-1520.
Повний текст джерелаCherkaoui, Badreddine, Abderrahim Beni-Hssane, Mohamed El Fissaoui, and 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.
Повний текст джерелаKalinic, Maja, and Jukka M. Krisp. "Fuzzy inference approach in traffic congestion detection." Annals of GIS 25, no. 4 (October 2, 2019): 329–36. http://dx.doi.org/10.1080/19475683.2019.1675760.
Повний текст джерелаBhanja, Urmila, Anita Mohanty, and Bhagyashree Das. "Embedded based real time traffic congestion detection." International Journal of Vehicle Information and Communication Systems 3, no. 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.094976.
Повний текст джерелаMohanty, Anita, Bhagyashree Das, and Urmila Bhanja. "Embedded based real time traffic congestion detection." International Journal of Vehicle Information and Communication Systems 3, no. 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.10016393.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаVägtrafikstockningar 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.
Повний текст джерелаAnbaroglu, 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/.
Повний текст джерелаKhatri, Chandra P. "Real-time road traffic information detection through social media." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53889.
Повний текст джерелаRui, 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.
Повний текст джерелаSwaro, 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.
Повний текст джерелаSvanberg, 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.
Повний текст джерелаI 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.
Повний текст джерелаLoureiro, Pedro Fernando Quintas. "Automatic traffic congestion detection using uncontrolled video sources." Dissertação, 2009. http://hdl.handle.net/10216/58176.
Повний текст джерелаANAS, MOHD. "TRAFFIC CONGESTION DETECTION USING DATA MINING IN VANET." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16357.
Повний текст джерелаКниги з теми "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.
Знайти повний текст джерелаHallenbeck, 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.
Знайти повний текст джерелаA busy day in Busytown. New York, N.Y: Simon Spotlight, 2010.
Знайти повний текст джерелаSantibañez Gruber, Rosa Maria, and Antonia Caro González, eds. DEUSTO Social Impact Briefings No. 4 (2019). University of Deusto, 2020. http://dx.doi.org/10.18543/dsib-4(2020).
Повний текст джерелаЧастини книг з теми "TRAFFIC CONGESTION DETECTION"
Noori, Mohammed Ahsan Raza, and Ritika Mehra. "Traffic Congestion Detection from Twitter Using word2vec." In ICT Analysis and Applications, 527–34. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8354-4_52.
Повний текст джерелаKumar, Tarun, and Dharmender Singh Kushwaha. "An Approach for Traffic Congestion Detection and Traffic Control System." In 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.
Повний текст джерелаChetouane, Ameni, Sabra Mabrouk, and Mohamed Mosbah. "Traffic Congestion Detection: Solutions, Open Issues and Challenges." In 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.
Повний текст джерелаKhalifa, Othman O., Azri A. Marzuki, Noreha Abdul Malik, and Mohammad H. Hassan Gani. "Traffic Congestion Detection for Smart and Control Transportation Management." In Lecture Notes in Electrical Engineering, 317–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2406-3_25.
Повний текст джерелаCheng, Jieren, Boyi Liu, and Xiangyan Tang. "A Traffic-Congestion Detection Method for Bad Weather Based on Traffic Video." In Communications in Computer and Information Science, 506–18. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0356-1_54.
Повний текст джерелаShaikh, Faisal Karim, Mohsin Shah, Bushra Shaikh, and Roshan Ahmed Shaikh. "Implementation and Evaluation of Vehicle-to-Vehicle Traffic Congestion Detection." In 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.
Повний текст джерелаSong, Yang, Zhuzhu Wang, Junwei Zhang, Zhuo Ma, and Jianfeng Ma. "A Decentralized Weighted Vote Traffic Congestion Detection Framework for ITS." In Communications in Computer and Information Science, 249–62. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9129-7_18.
Повний текст джерелаZhuo, Yedi, Ping Wang, Jiaojiao Sun, and Yinli Jin. "Traffic Congestion Detection Based on the Image Classification with CNN." In Lecture Notes in Electrical Engineering, 4569–80. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_378.
Повний текст джерелаOumaima, El Joubari, Ben Othman Jalel, and Vèque Véronique. "A Stochastic Traffic Model for Congestion Detection in Multi-lane Highways." In Ad Hoc Networks, 87–99. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67369-7_7.
Повний текст джерелаAlomari, Ebtesam, Rashid Mehmood, and Iyad Katib. "Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection." In Smart Infrastructure and Applications, 37–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13705-2_2.
Повний текст джерелаТези доповідей конференцій з теми "TRAFFIC CONGESTION DETECTION"
Palmer, J. P. "Automatic incident detection and improved traffic control in urban areas." In IEE Colloquium on Urban Congestion Management. IEE, 1995. http://dx.doi.org/10.1049/ic:19951296.
Повний текст джерелаNidhal, Ahmed, Umi Kalthum Ngah, and Widad Ismail. "Real time traffic congestion detection system." In 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2014. http://dx.doi.org/10.1109/icias.2014.6869538.
Повний текст джерелаG., Raji C., Shamna Shirin K, Murshidha, Fathimathul Fasila V. P, and Shiljiya Shirin K. T. "Emergency Vehicles Detection during Traffic Congestion." In 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2022. http://dx.doi.org/10.1109/icoei53556.2022.9776942.
Повний текст джерелаAnjum, Nimra, Nasreen Badruddin, and Micheal Drieberg. "Simulation of traffic congestion detection using VANETs." In 2014 5th International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2014. http://dx.doi.org/10.1109/icias.2014.6869475.
Повний текст джерелаDimri, Anuj, Harsimran Singh, Naveen Aggarwal, Bhaskaran Raman, Diyva Bansal, and K. K. Ramakrishnan. "RoadSphygmo: Using barometer for traffic congestion detection." In 2016 8th International Conference on Communication Systems and Networks (COMSNETS). IEEE, 2016. http://dx.doi.org/10.1109/comsnets.2016.7439942.
Повний текст джерелаLiu, Tingrang, and Min Zhao. "The 3D McMaster Algorithm for Traffic Congestion Detection." In 2020 Chinese Control And Decision Conference (CCDC). IEEE, 2020. http://dx.doi.org/10.1109/ccdc49329.2020.9164882.
Повний текст джерелаKhalil, Mudassir, Jianping Li, Abida Sharif, and Jalaluddin Khan. "Traffic congestion detection by use of satellites view." In 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.
Повний текст джерелаSommer, Matthias, and Jörg Hähner. "Learning Classifier Systems for Road Traffic Congestion Detection." In 3rd International Conference on Vehicle Technology and Intelligent Transport Systems. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006214101420150.
Повний текст джерелаManjoro, Wellington Simbarashe, Mradul Dhakar, and Brijesh Kumar Chaurasia. "Traffic congestion detection using data mining in VANET." In 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE, 2016. http://dx.doi.org/10.1109/sceecs.2016.7509347.
Повний текст джерелаRao, Aditya, Akshay Phadnis, Atul Patil, Tejal Rajput, and Pravin Futane. "Dynamic Traffic System Based on Real Time Detection of Traffic Congestion." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697838.
Повний текст джерелаЗвіти організацій з теми "TRAFFIC CONGESTION DETECTION"
System Monitoring of Auto Traffic: Queue Detection and Congestion Impact Assessment. Tampa, FL: University of South Florida, April 2022. http://dx.doi.org/10.5038/cutr-nicr-rr-2022-1-3.
Повний текст джерела