Relevant bibliographies by topics / TRAFFIC CONGESTION DETECTION

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'TRAFFIC CONGESTION DETECTION'

Author: Grafiati
Published: 11 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "TRAFFIC CONGESTION DETECTION"

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Jian, Cheng, Chenxi Lin, Xiaojian Hu, and Jian Lu. "Selective Scale-Aware Network for Traffic Density Estimation and Congestion Detection in ITS." Sensors 25, no. 3 (2025): 766. https://doi.org/10.3390/s25030766.

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Traffic congestion detection in surveillance video is crucial for road traffic condition monitoring and improving traffic operation efficiency. Currently, traffic congestion is often characterized through traffic density, which is obtained by detecting vehicles or using holistic mapping methods. However, these traditional methods are not effective in dealing with the vehicle scale variation in surveillance video. This prompts us to explore density-map-based traffic density detection methods. Considering the dynamic characteristics of traffic flow, relying solely on the spatial feature of traff
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S, Sneha, Sriranjini S, Himasai T, and Balaji M. "IoT Based Traffic Congestion Management and Accident Detection System." Journal of Electrical Engineering and Automation 6, no. 1 (2024): 63–71. http://dx.doi.org/10.36548/jeea.2024.1.005.

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This review proposes a traffic congestion management and accident detection system to reduce congestion at junctions and to provide emergency assistance during accidents. The proposed system employs advanced computer vision and image processing techniques like You Only Look Once (YOLO) to monitor and analyze real-time traffic conditions and accidents. The pivotal feature of this system lies in its adaptive decision-making capability, automatically adjusting traffic signal timings based on observed density patterns and updating and reporting about the congestions and accidents for which Interne
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Wiseman, Yair. "Computerized Traffic Congestion Detection System." International Journal of Transportation and Logistics Management 1, no. 1 (2017): 1–8. http://dx.doi.org/10.21742/ijtlm.2017.1.1.01.

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Sternford, Mavuchi, Magadza Tirivangani, and Chikoore Racheal. "Deep Learning for Traffic Congestion Detection: A Survey Paper." International Journal of Innovative Science and Research Technology (IJISRT) 8, no. 11 (2024): 5. https://doi.org/10.5281/zenodo.10877599.

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Traffic congestion is a major problem in urban areas, leading to increased travel time, economic losses, and environmental pollution. By analyzing traffic data from traffic cameras, we can detect and predict traffic congestion with high accuracy. In this survey, we explore the use of deep learning techniques for traffic congestion detection. Deep learning models, such as convolutional neural networks and recurrent neural networks, have shown promising results in traffic congestion detection. We also discuss the challenges and future directions of this field, including the need for high-quality
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Nurshahrily, Idura Ramli, and Izani Mohamed Rawi Mohd. "An overview of traffic congestion detection and classification techniques in VANET." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 437–44. https://doi.org/10.11591/ijeecs.v20.i1.pp437-444.

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Vehicular traffic congestion has been and still is a major problem for many countries and knowledge about the traffic condition is important in order to schedule, plan and avoid traffic congestion. With recent development in technology, various efforts and methods are proposed in mitigating traffic congestion. Vehicular Ad-hoc NETwork (VANET) is very much in the hype in addressing this issue due to its capabilities and adaptation to scalability, highly dynamic topology as well as cooperative communication. A popular focus is in detecting and classisying traffic congestion which presents variou
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Indra Bayu Pangestu, Maimunah Maimunah, and Mukhtar Hanafi. "Traffic Congestion Detection Using YOLOv8 Algorithm With CCTV Data." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 12, no. 2 (2024): 435–44. http://dx.doi.org/10.33558/piksel.v12i2.9953.

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Community development and growth according to data from the Central Java Statistics Agency regarding the number of vehicles in Central Java Province in 2021 is 20 320 743. The increasing growth of society has caused vehicle density which is a serious problem in urban areas. This study developed a congestion detection system using the YOLOv8 algorithm to analyze traffic density from CCTV footage. Automated detection of traffic congestion is a critical challenge in urban transport management. YOLOv8, a fast and accurate object detection algorithm, is used to identify vehicles and count their num
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Idura 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 (2020): 437. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp437-444.

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<span>Vehicular traffic congestion has been and still is a major problem for many countries and knowledge about the traffic condition is important in order to schedule, plan and avoid traffic congestion. With recent development in technology, various efforts and methods are proposed in mitigating traffic congestion. Vehicular Ad-hoc NETwork (VANET) is very much in the hype in addressing this issue due to its capabilities and adaptation to scalability, highly dynamic topology as well as cooperative communication. A popular focus is in detecting and classisying traffic congestion which pre
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Xiang, Yingxiao, Wenjia Niu, Endong Tong, et al. "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.

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The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empiric
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Wang, 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 (2020): 1166–74. http://dx.doi.org/10.2174/2352096513999200628095848.

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Background: It is important to improve the quality of service by using congestion detection technology to find the potential congestion as early as possible in wireless sensor network. Methods: So an improved congestion control scheme based on traffic assignment and reassignment algorithm is proposed for congestion avoidance, detection and mitigation. The congestion area of the network is detected by predicting and setting threshold. When the congestion occurs, sensor nodes can be recovery quickly from congestion by adopting reasonable method of traffic reassignment. And the method can ensure
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Jiang, Shan, Yuming Feng, Wei Zhang, Xiaofeng Liao, Xiangguang Dai, and Babatunde Oluwaseun Onasanya. "A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection." Sensors 24, no. 13 (2024): 4272. http://dx.doi.org/10.3390/s24134272.

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With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection
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Dissertations / Theses on the topic "TRAFFIC CONGESTION DETECTION"

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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.

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Road traffic congestion causes several problems. For instance, slow moving traffic in congested regions poses a safety hazard to vehicles approaching the congested region and increased commuting times lead to higher transportation costs and increased pollution.The work carried out in this thesis aims to detect and track road traffic congestion in real time. Real-time road congestion detection is important to allow for mechanisms to e.g. improve traffic safety by sending advanced warnings to drivers approaching a congested region and to mitigate congestion by controlling adaptive speed limits.
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RezaeiDivkolaei, Pouya. "DETECTION, CLASSIFICATION, AND LOCATION IDENTIFICATION OF TRAFFIC CONGESTION FROM TWITTER STREAM ANALYSIS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2257.

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Social media today is an important source of information about various events happening around the world. Among various social networking platforms, microtext based ones such as Twitter are of special interest as they are also a rich source of real-time events. In this thesis, our goal is to study the effectiveness of using Twitter as a social sensor for obtaining real-time information on road traffic conditions. Specifically, we focus on: i) identifying tweets that contain traffic event related information, ii) classify such tweets into six main groups of accident, fire, road construction, po
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Anbaroglu, 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/.

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Non-Recurrent Congestion events (NRCs) frustrate commuters, companies and traffic operators because they cause unexpected delays. Most existing studies consider NRCs to be an outcome of incidents on motorways. The differences between motorways and urban road networks, and the fact that incidents are not the only cause of NRCs, limit the usefulness of existing automatic incident detection methods for identifying NRCs on an urban road network. This thesis contributes to the literature by developing an NRC detection methodology to support the accurate detection of NRCs on large urban road network
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Khatri, Chandra P. "Real-time road traffic information detection through social media." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53889.

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In current study, a mechanism to extract traffic related information such as congestion and incidents from textual data from the internet is proposed. The current source of data is Twitter, however, the same mechanism can be extended to any kind of text available on the internet. As the data being considered is extremely large in size automated models are developed to stream, download, and mine the data in real-time. Furthermore, if any tweet has traffic related information then the models should be able to infer and extract this data. To pursue this task, Artificial Intelligence, Machine Lear
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Rui, 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.

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Congestion is a major problem in most metropolitan areas and given the increasingrate of urbanization it is likely to be an even more serious problem in the rapidlyexpanding mega cities. One possible method to combat congestion is to provide in-telligent traffic management systems that can in a timely manner inform drivers aboutcurrent or predicted traffic congestions that are relevant to them on their journeys. Thedetection of traffic congestion and the determination of whom to send in advance no-tifications about the detected congestions is the objective of the present research. Byadopting a gri
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Swaro, 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.

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Svanberg, 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.

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In this master thesis, we implement a two-step anomaly detection mechanism for non-recurrent traffic congestions with data collected from public transport buses in Stockholm. We investigate the use of machine learning to model time series data with LSTMs and evaluate the results with a baseline prediction model. The anomaly detection algorithm embodies both collective and contextual expressivity, meaning it is capable of findingcollections of delayed buses and also takes the temporality of the data into account. Results show that the anomaly detection performance benefits from the lower predic
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Loureiro, Pedro Fernando Quintas. "Automatic traffic congestion detection using uncontrolled video sources." Master's thesis, 2009. http://hdl.handle.net/10216/58176.

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Loureiro, Pedro Fernando Quintas. "Automatic traffic congestion detection using uncontrolled video sources." Dissertação, 2009. http://hdl.handle.net/10216/58176.

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ANAS, MOHD. "TRAFFIC CONGESTION DETECTION USING DATA MINING IN VANET." Thesis, 2018. http://dspace.dtu.ac.in:8080/jspui/handle/repository/16357.

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Information Technology, in the past few years has progressed to a level where it is impossible for an aspect of life to not be touched by it. It is being used to the advantage of humanity in solving difficult engineering problems and improving the quality of life. Vehicular traffic is one such area where modern technology has advanced to a phase where the ideas of interconnecting the vehicles on the road are being experimented with in different countries. These networks of interconnected vehicles are commonly known as Vehicular Ad-hoc Networks or VANETs for short. VANETs provide a platform for
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Books on the topic "TRAFFIC CONGESTION DETECTION"

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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. Washington State Dept. of Transportation, Washington State Transportation Commission in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration, 1992.

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Hallenbeck, Mark E. Use of automatic vehicle identification techniques for measuring traffic performance and performing incident detection: Final report. 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.

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Santibañez 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).

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This fourth edition of the DSIB presents the main results of the research carried out under four broad-based projects jointly developed by researchers and actors involved in topics of great social relevance such as responsible gambling, Cooperative-Intelligent transport Systems, gender dimension of alcohol addiction and support and care for victims of trafficking for sexual exploitation. This issue comprises the following four briefings: 1. What would sports betting advertising be like if it were handled more responsibly? will analyse the structure of sports betting advertising, in an attempt
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A busy day in Busytown. Simon Spotlight, 2010.

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Book chapters on the topic "TRAFFIC CONGESTION DETECTION"

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Noori, Mohammed Ahsan Raza, and Ritika Mehra. "Traffic Congestion Detection from Twitter Using word2vec." In ICT Analysis and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8354-4_52.

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Kumar, Tarun, and Dharmender Singh Kushwaha. "An Approach for Traffic Congestion Detection and Traffic Control System." In Information and Communication Technology for Competitive Strategies. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0586-3_10.

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Chetouane, Ameni, Sabra Mabrouk, and Mohamed Mosbah. "Traffic Congestion Detection: Solutions, Open Issues and Challenges." In Communications in Computer and Information Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65810-6_1.

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Khalifa, 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. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2406-3_25.

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Cheng, 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. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0356-1_54.

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Bhattacharjee, Sanjoy, Debdatta Chatterjee, Dipankar Misra, Kaustav Sharma, and Papri Ghosh. "Smart Traffic Management: Automated Rerouting and Congestion Detection with Sensor Technology." In Springer Tracts on Transportation and Traffic. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87627-1_13.

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Shaikh, 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. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10987-9_21.

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Song, 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. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9129-7_18.

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Ahmed, Mustapha Abubakar, Azizul Rahman Mohd Shariff, and Saadatu Abubakar. "Long Term Traffic Congestion Detection Method Based on Speed-Threshold." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77003-6_3.

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Zhuo, Yedi, Ping Wang, Jiaojiao Sun, and Yinli Jin. "Traffic Congestion Detection Based on the Image Classification with CNN." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_378.

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Conference papers on the topic "TRAFFIC CONGESTION DETECTION"

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Purkrábková, Zuzana, and Pavel Hrubeš. "Validity of Speed-Based Congestion Detection in Traffic Data." In 2025 Smart City Symposium Prague (SCSP). IEEE, 2025. https://doi.org/10.1109/scsp65598.2025.11037718.

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Swaned, Mohammed, Sajid Javid, Shreyali Humaney, Anuj Sachan, Nisha Singh Chauhan, and Neetesh Kumar. "Enhancing Traffic Management Through Advanced Vehicle Detection for Congestion Prevention." In 2024 IEEE 21st International Conference on Mobile Ad-Hoc and Smart Systems (MASS). IEEE, 2024. http://dx.doi.org/10.1109/mass62177.2024.00099.

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D, Malathi, Feisal Alaswad, Batoul Aljaddouh, Leela Ranganayagi, and Sangeetha R. "AI-Powered Traffic Management: Improving Congestion Detection and Signal Regulation." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894186.

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Barik, Pradip Kumar, Aayushi Varmora, Ankita Dimri, Annshu Prajapati, and Brijesh Kavar. "Real-time Collision Detection and Traffic Congestion Control for Vehicular Networks." In 2025 International Conference on Sustainable Energy Technologies and Computational Intelligence (SETCOM). IEEE, 2025. https://doi.org/10.1109/setcom64758.2025.10932547.

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Bouabid, Marwen, Olfa Daikhi, and Mohamed Farah. "Real-Time Traffic Congestion Detection in Bogota Using Tweets: A Hybrid Approach Combining DistilBERT and BiLSTM." In 2024 IEEE/ACS 21st International Conference on Computer Systems and Applications (AICCSA). IEEE, 2024. https://doi.org/10.1109/aiccsa63423.2024.10912598.

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Sridevi, S., Alex David S, Pujari Mahi Pranathi, Kunamneni Sravya, Prabhu Shankar B, and Sakthi Karthi Durai B. "AI-Driven Traffic Monitoring System for Real-Time Congestion Detection and Route Optimization in 6G-Enabled Smart Cities." In 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025. https://doi.org/10.1109/icict64420.2025.11004705.

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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.

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Nidhal, 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.

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G., 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.

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M S, Sneha Maria, Sreyas Raj P, Vivek Viswanathan, Saniya Sajan, and Soosan George T. "Vehicle Actuated Traffic Signal using AI." In Second International Conference in Civil Engineering for a Sustainable Planet. AIJR Publisher, 2025. https://doi.org/10.21467/proceedings.179.41.

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Traffic congestion in urban areas leads to capacity issues, intersection delays, increased congestion, fuel consumption, and air pollution. Advanced traffic management systems, including adaptive signals and intelligent transportation systems, offer solutions to mitigate congestion and improve road network efficiency. Utilizing live camera imagery and AI for real-time traffic density assessment, alongside adaptive signal control algorithms, reduces congestion and optimizes traffic flow, aligning with the trend of technology-driven transportation systems for environmental benefits. YOLO (You On
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Reports on the topic "TRAFFIC CONGESTION DETECTION"

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Ayala Somayajula, Revanth. Real time traffic congestion detection using images. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1188.

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System Monitoring of Auto Traffic: Queue Detection and Congestion Impact Assessment. University of South Florida, 2022. http://dx.doi.org/10.5038/cutr-nicr-rr-2022-1-3.

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