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Gotowe bibliografie tematyczne / Traffic event detection

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Gotowa bibliografia na temat „Traffic event detection”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Traffic event detection"

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Kim, Yuna, Sangho Song, Hyeonbyeong Lee, et al. "Regional Traffic Event Detection Using Data Crowdsourcing." Applied Sciences 13, no. 16 (2023): 9422. http://dx.doi.org/10.3390/app13169422.

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Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data

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Lee, Soomok, Sanghyun Lee, Jongmin Noh, Jinyoung Kim, and Harim Jeong. "Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives." Sensors 23, no. 19 (2023): 8129. http://dx.doi.org/10.3390/s23198129.

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Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve t

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Wang, Jia, Minh Ngueyn, and Weiqi Yan. "A Framework of Event-Driven Traffic Ticketing System." International Journal of Digital Crime and Forensics 9, no. 1 (2017): 39–50. http://dx.doi.org/10.4018/ijdcf.2017010103.

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This paper presents a new scheme to implement an event-driven traffic ticketing system. The system consists of two modules, namely, (1) event detection module, (2) database management module to execute information retrieval and deliver traffic tickets. In this paper, the notable contribution is an automatic detection of traffic ticketing events from video footages and immediate notification of such event when a car is passing the white stop line before a red traffic light. The results show the authors' work is efficient and could detect the events precisely.

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Navi, Ramesh, and Aruna M. G. "Traffic Event Detection using Computer Vision." IOSR Journal of Computer Engineering 16, no. 3 (2014): 25–32. http://dx.doi.org/10.9790/0661-16322532.

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Zhiwei Zhang, Zhiwei Zhang, Guiyuan Tang Zhiwei Zhang, Baoquan Ren Guiyuan Tang, Baoquan Ren Baoquan Ren, and Yulong Shen Baoquan Ren. "TV-ADS: A Smarter Attack Detection Scheme Based on Traffic Visualization of Wireless Network Event Cell." 網際網路技術學刊 25, no. 2 (2024): 301–11. http://dx.doi.org/10.53106/160792642024032502012.

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<p>To protect the increasing cyberspace assets, attack detection systems (ADSs) as well as intrusion detection systems (IDSs) have been equipped in various network environments. Recently, with the development of big data, machine learning, deep learning, neural networks and other artificial intelligence (AI) technologies, more and more ADSs/IDSs based on Artificial Intelligence are presented in academia and industry. Particularly, depending on the outstanding performance and efficiency in recognizing and classifying images, computer vision algorithms have been employed to detect maliciou

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K.V.G.N., Naidu*1 &. P. Sireesha2. "TWITTER ANALYSISFOR IDENTIFICATION OF REAL-TIME TRAFFIC." INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT 4, no. 5 (2017): 148–51. https://doi.org/10.5281/zenodo.573507.

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Social networks have been recently empployed as a source of information for event detection, with specific reference to road traffic activity congestion and accidents or earthquack reporting system. In our paper, we present a real-time detection of traffic from Twitter stream analysis. The system fetches tweets from Twitter as per a several search criteria; process tweets by applying text mining methods; lastly performs the classification of tweets. The aim is to assign suitable class label to every tweet, as related with an activity of traffic event or not. The traffic detection system or fra

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Xu, S., S. Li, R. Wen, and W. Huang. "TRAFFIC EVENT DETECTION USING TWITTER DATA BASED ON ASSOCIATION RULES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 543–47. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-543-2019.

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<p><strong>Abstract.</strong> Social media platforms allow millions of people worldwide to instantly share their thoughts online. Many people use social media to share traffic related experiences and events with online posts. A large amount of traffic related data can be obtained from these online posts &amp;ndash; especially geosocial media data, where posts are tagged with geolocation information such as coordinates or place names. By extracting traffic events from geosocial media data, drivers can adapt to changing traffic conditions, while traffic management departmen

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Priyanka, Shinde, and M. Dongre Manoj. "TRAFFIC CONGESTION DETECTION WITH COMPLEX EVENT PROCESSING IN VANET." JournalNX - a Multidisciplinary Peer Reviewed Journal RIT PG Con-18 (April 22, 2018): 270–74. https://doi.org/10.5281/zenodo.1413800.

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Magnitude of urban population and unplanned development of cities have led to road traffic congestion in major cities and added increasing pressure on overall road transport and road related infrastructure. Nowadays, intelligent transportation systems such as Vehicular Ad Hoc Networks (VANET) are used for distributed road traffic. VANET is a wireless network that gathers complex and randomly generated data related to distributed traffic along with other information such as weather con ditions on real time basis. In this paper, the Author has attempted to implement a system on Event Driven Arch

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Wu, Hao, Jiahao Yang, Ming-Dong Yuan, and Xin Li. "Heuristic Optimal Scheduling for Road Traffic Incident Detection Under Computational Constraints." Sensors 24, no. 22 (2024): 7221. http://dx.doi.org/10.3390/s24227221.

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The intelligent monitoring of road surveillance videos is a crucial tool for detecting and predicting traffic anomalies, swiftly identifying road safety risks, rapidly addressing potential hazards, and preventing accidents or secondary incidents. With the vast number of surveillance cameras in operation, conducting traditional real-time video analysis across all cameras at once requires substantial computational resources. Alternatively, methods that employ periodic camera patrol analysis frequently overlook a significant number of anomalous traffic events, thereby hindering the effectiveness

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A Mohamed, M., N. Jamil, A. F Abidin, M. M Din, W. N S W Nik, and A. R Mamat. "Entity-based Parameterization for Distinguishing Distributed Denial of Service from Flash Events." International Journal of Engineering & Technology 7, no. 2.14 (2018): 5. http://dx.doi.org/10.14419/ijet.v7i2.14.11142.

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In a perfect condition, there are only normal network traffic and sometimes flash event traffics due to some eye-catching or heart-breaking events. Nevertheless, both events carry legitimate requests and contents to the server. Flash event traffic can be massive and damaging to the availability of the server. However, it can easily be remedied by hardware solutions such as adding extra processing power and memory devices and software solution such as load balancing. In contrast, a collection of illegal traffic requests produced during distributed denial of service (DDoS) attack tries to cause

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Rozprawy doktorskie na temat "Traffic event detection"

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Hyink, Jeffrey F. "Mobile sensor networks a discrete event simulation of WMD threat detection in urban traffic schemes." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion.exe/07Mar%5FHyink.pdf.

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Buyukozcu, Demirhan. "Discretized Categorization Of High Level Traffic Activites In Tunnels Using Attribute Grammars." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615127/index.pdf.

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This work focuses on a cognitive science inspired solution to an event detection problem in a video domain. The thesis raises the question whether video sequences that are taken in highway tunnels can be used to create meaningful data in terms of symbolic representation, and whether these symbolic representations can be used as sequences to be parsed by attribute grammars into abnormal and normal events. The main motivation of the research was to develop a novel algorithm that parses sequences of primitive events created by the image processing algorithms. The domain of the research is video d

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Wulff, Tobias. "Evaluation of and Mitigation against Malicious Traffic in SIP-based VoIP Applications in a Broadband Internet Environment." Thesis, University of Canterbury. Computer Science and Software Engineering, 2010. http://hdl.handle.net/10092/5120.

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Voice Over IP (VoIP) telephony is becoming widespread, and is often integrated into computer networks. Because of his, it is likely that malicious software will threaten VoIP systems the same way traditional computer systems have been attacked by viruses, worms, and other automated agents. While most users have become familiar with email spam and viruses in email attachments, spam and malicious traffic over telephony currently is a relatively unknown threat. VoIP networks are a challenge to secure against such malware as much of the network intelligence is focused on the edge devices and acces

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SILVA, Adson Diego Dionisio da. "Arcabouço para análise de eventos em vídeos." Universidade Federal de Campina Grande, 2015. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/592.

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Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-07T15:29:04Z No. of bitstreams: 1 ADSON DIEGO DIONISIO DA SILVA - DISSERTAÇÃO PPGCC 2015..pdf: 2453030 bytes, checksum: 863c817f9714377b827d4d6fa0770c51 (MD5)<br>Made available in DSpace on 2018-05-07T15:29:04Z (GMT). No. of bitstreams: 1 ADSON DIEGO DIONISIO DA SILVA - DISSERTAÇÃO PPGCC 2015..pdf: 2453030 bytes, checksum: 863c817f9714377b827d4d6fa0770c51 (MD5) Previous issue date: 2015-08-31<br>O reconhecimento automático de eventos de interesse em vídeos envolvendo conjuntos de ações ou de interações entre objetos.

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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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Bhatia, Sajal. "Detecting distributed Denial-of-Service attacks and Flash Events." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/62031/1/Sajal_Bhatia_Thesis.pdf.

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This thesis investigates and develops techniques for accurately detecting Internet-based Distributed Denial-of-Service (DDoS) Attacks where an adversary harnesses the power of thousands of compromised machines to disrupt the normal operations of a Web-service provider, resulting in significant down-time and financial losses. This thesis also develops methods to differentiate these attacks from similar-looking benign surges in web-traffic known as Flash Events (FEs). This thesis also addresses an intrinsic challenge in research associated with DDoS attacks, namely, the extreme scarcity of publi

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Chang, Fu-Lin, and 張馥麟. "Object Tracking, Shadow Removal and Collision Event Detection for Traffic Surveillance System." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/79858593116661730622.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>94<br>In　recent　years,　public　safety　becomes　more　and　more　important　issue　in　our　daily　life.　In　order　to　reduce　the　damage　from　traffic　incident,　video-based　surveillance　system　has　a　wide　range　of　applications　for　traffic　monitoring.　In　this　thesis,　an　automatic　traffic　surveillance　system　is　presented　to　track　moving　objects　and　detect　the　collision　event.　First,　initial　background　model　is　built　to　extract　the　foreground　regions　by　background　subtraction　and　background　model　must　be　updated　by　the　result　of　extracted　foreground　region　due　to　illumination　chang

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Chen, Ying-ju, and 陳盈如. "Using different change blindness method to investigate the effect of field dependence/ field independence drivers on traffic event detection." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/27520508037130938837.

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碩士<br>雲林科技大學<br>工業工程與管理研究所碩士班<br>96<br>A change blindness method was conducted to investigate the effect of different age groups (younger vs. older), and different cognitive style (field dependence vs. field independence) on traffic event detection in different intersection environments (simple vs. complex). 45 participants broken down by different age and cognitive style voluntarily participated in this study (15 younger groups with field dependence vs. 15 younger groups with field independence vs. 15 older groups with field dependence). Traffic events were divided into four types i.e., the t

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Hou, Chien-he, and 侯建和. "Design and Implementation of the Image Vehicle Detector with Traffic Events Detection in the City Traffic Control Protocol 3.1." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/89952864542575418986.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>103<br>The image vehicle detector not only can providing basic traffic information, but also can attach other image event detection functions. Therefore, it is a main direction of vehicle detector in recent years. However, it is not easy to use image to calculate the traffic volume and traffic event at the same time because of the vehicle is fleeting and the image processing is time-consuming. Accordingly, the purpose of this study was to explore and to design a real-time vehicle detection algorithm and several real-time image events detection method. Another aim w

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Książki na temat "Traffic event detection"

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Current: A Novel. Thorndike Press, 2019.

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Johnston, Tim. The Current: A Novel. Algonquin Books, 2019.

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The Current: A Novel. Highbridge Audio and Blackstone Publishing, 2021.

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The Current: A Novel. Algonquin Books, 2019.

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Części książek na temat "Traffic event detection"

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Pawlowski, Oliver, Jürgen Dunkel, Ralf Bruns, and Sascha Ossowski. "Applying Event Stream Processing on Traffic Problem Detection." In Progress in Artificial Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04686-5_3.

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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. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26561-2_45.

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Souto, Gustavo, and Thomas Liebig. "On Event Detection from Spatial Time Series for Urban Traffic Applications." In Solving Large Scale Learning Tasks. Challenges and Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41706-6_11.

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Mentasti, Simone, Abednego Wamuhindo Kambale, and Matteo Matteucci. "Event-Based Object Detection and Tracking - A Traffic Monitoring Use Case -." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13324-4_9.

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Alomari, Ebtesam, Rashid Mehmood, and Iyad Katib. "Sentiment Analysis of Arabic Tweets for Road Traffic Congestion and Event Detection." In Smart Infrastructure and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13705-2_2.

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Paramonov, Alexander, and Andrey Koucheryavy. "M2M Traffic Models and Flow Types in Case of Mass Event Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10353-2_25.

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Chakravarthi, Bharatesh, M. Manoj Kumar, and B. N. Pavan Kumar. "Event-Based Sensing for Improved Traffic Detection and Tracking in Intelligent Transport Systems Toward Sustainable Mobility." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9610-0_8.

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Apostolovski, Nikolaj, Naum Trajanovski, Marko Chavdar, Tomislav Kartalov, Branislav Gerazov, and Zoran Ivanovski. "Deep Learning Based Multimodal Information Fusion for Near-Miss Event Detection in Intelligent Traffic Monitoring Systems." In Complex Systems: Spanning Control and Computational Cybernetics: Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00978-5_15.

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Yu, Chao, Chongyang Zhang, Guang Tian, and Longfei Liang. "Vehicle Trajectory Description for Traffic Events Detection." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34595-1_32.

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Bhowmick, Kiran, and Meera Narvekar. "Trajectory Outlier Detection for Traffic Events: A Survey." In Intelligent Computing and Information and Communication. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_5.

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Streszczenia konferencji na temat "Traffic event detection"

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Shukla, Minakshee, Renu Rameshan, and Shikha Gupta. "Anomalous Event Detection in Traffic Audio." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013152100003905.

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Bouraffa, Tayssir, Elias Kjellberg Carlson, Erik Wessman, Ali Nourir, Pierre Lamart, and Christian Berger. "Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves." In 2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2024. https://doi.org/10.1109/itsc58415.2024.10919665.

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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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Figueiras, Paulo, Hugo Antunes, Guilherme Guerreiro, Ruben Costa, and Ricardo Jardim-Gonçalves. "Visualisation and Detection of Road Traffic Events Using Complex Event Processing." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87909.

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In the recent decades, we have witnessed an increase in the number of vehicles using the road infrastructure, resulting in an increased overload of the road network. To mitigate such problems, caused by the increasing number of vehicles and increasing the efficiency and safety of transport systems has been integrated applications of advanced technology, denominated Intelligent Transport Systems (ITS). However, one problem still unsolved in current road networks is the automatic identification of road events such as accidents or traffic jams, being inhibitor to efficient road management. In ord

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Salas, A., P. Georgakis, C. Nwagboso, A. Ammari, and I. Petalas. "Traffic event detection framework using social media." In 2017 IEEE International Conference on Smart Grid and Smart Cities (ICSGSC). IEEE, 2017. http://dx.doi.org/10.1109/icsgsc.2017.8038595.

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Pereira, Alexandra S., Thais R. M. B. Silva, Fabricio A. Silva, and Antonio A. F. Loureiro. "Traffic Event Detection Using Online Social Networks." In 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2017. http://dx.doi.org/10.1109/dcoss.2017.36.

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Meysel, Frederik. "Map-based change detection (MBCD) in urban traffic scenes." In 2011 Joint Urban Remote Sensing Event (JURSE). IEEE, 2011. http://dx.doi.org/10.1109/jurse.2011.5764704.

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Lima, Janio, Rebecca Salles, Luciana Escobar, et al. "Towards a cloud-based framework for online and integrated event detection." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbbd_estendido.2022.21865.

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Time series events detection relates to the study of techniques for detecting points in a series with special meaning which differs from the expected behavior of the dataset. In scenarios such as digital twins and IoT devices, there is natural generation and traffic of data in the cloud. Event detection is critical for timely decision-making. Since many methods for detecting events target different types selecting a suitable method makes the task more difficult. In this context, this article proposes a cloud-based framework called Harbinger Nimbus. The implementation was evaluated on the Micro

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Chen, Qi, and Wei Wang. "Multi-modal Neural Network for Traffic Event Detection." In 2019 IEEE 2nd International Conference on Electronics and Communication Engineering (ICECE). IEEE, 2019. http://dx.doi.org/10.1109/icece48499.2019.9058508.

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Nejjari, Fadoua, Laila Benhlima, and Slimane Bah. "Event traffic detection using heterogenous wireless sensors network." In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE, 2016. http://dx.doi.org/10.1109/aiccsa.2016.7945825.

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Raporty organizacyjne na temat "Traffic event detection"

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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Xueqian Shi. Guidelines for Evaluating Safety Using Traffic Encounters: Proactive Crash Estimation on Roadways with Conventional and Autonomous Vehicle Scenarios. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317587.

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With the expected arrival of autonomous vehicles, and the ever-increasing levels of automation in today’s human driven vehicles, road safety is changing at a rapid pace. This project aimed to address the need for an efficient and rapid method of safety evaluation and countermeasure identification via traffic encounters, specifically traffic conflicts that are considered useful surrogates of crashes. Recent research-delivered methods for estimating crash frequencies based on these events were observed in the field. In this project we developed a method for observing traffic encounters with two

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Saldivar-Carranza, Enrique D., Howell Li, Jijo K. Mathew, et al. Next Generation Traffic Signal Performance Measures: Leveraging Connected Vehicle Data. Purdue University Press, 2023. http://dx.doi.org/10.5703/1288284317625.

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High-resolution connected vehicle (CV) trajectory and event data has recently become commercially available. With over 500 billion vehicle position records generated each month in the United States, these data sets provide unique opportunities to build on and expand previous advances on traffic signal performance measures and safety evaluation. This report is a synthesis of research focused on the development of CV-based performance measures. A discussion is provided on data requirements, such as acquisition, storage, and access. Subsequently, techniques to reference vehicle trajectories to re

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Pourhomayoun, Mohammad. Artificial Intelligence for Pedestrian and Bicyclist Safety: Using AI to Detect Near-Miss Collisions. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2350.

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Near-Miss Collisions are events that, with a slight change in position or timing, could have resulted in a collision, which could have caused severe injury or property damage. Understanding near-miss collisions can help identify risks and potentially improve road safety. In this project, we developed an effective end-to-end system based on advanced artificial intelligence (AI) models and computer vision algorithms to detect and report near-miss collisions as an important indicator to identify and measure safety risks, especially in specific circumstances such as a right turn on a red light. Th

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