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Academic literature on the topic 'TRAFFIC CONGESTION DETECTION'

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Published: 11 September 2023

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Journal articles on the topic "TRAFFIC CONGESTION DETECTION"

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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.

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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 (October 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 presents various techniques and methodologies. This paper presents an overview of traffic congestion detection and classification methods of various related techniques in VANET, organized from the research perspective. Qualitative analysis is presented to classify these strategies in its system architecture, detection and classification methods, as well as its simulated mobility environment and simulation tools used. The analysis is useful in understanding all the techniques and methods applied in resolving this issue in the research domain. </span>
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Xiang, 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.

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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 empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.
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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 (December 3, 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 the data in the congestion areas can be transferred to noncongestion areas as soon as possible. Results: The simulation results indicate that the proposed scheme can reduce the number of loss packets, improve the throughput, stabilize the average transmission rate of source node and reduce the end-to-end delay. Conclusion: : So the proposed scheme can enhance the overall performance of the network. Keywords: wireless sensor network; congestion control; congestion detection; congestion mitigation; traffic assignment; traffic reassignment.
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Kayarga, 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.

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In the recent times due to the increase of vehicular nodes in a vehicular communication network, there is a need of developing efficient systems in order to optimize the vehicular traffic congestion issues in urban areas. The current research trends shows that most of the conventional studies focused on developing fuzzy inference systems based vehicular traffic congestion model which has gained lots of attention on detecting and minimizing the congestion levels.We have proposed a new approach towards detection and controlling of traffic congestion in VANET. The proposed system utilizes the communication channels very efficiently and irrespective of any kind of overload. This proposed system aims to introduce a novel framework for identifying traffic jam on Vehicular Ad-hoc Networks. In order to detect and minimize the level of congestion our approach will use a fuzzy logic based approach to notify the drivers about available routes during the traffic congestion. An experimental prototype will be set up to enable the graphical simulation.
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El-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.

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Cherkaoui, 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.

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Kalinic, 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.

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Bhanja, 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.

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Mohanty, 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.

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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. In addition, the tracking of the evolution of congestion in time and space can be a valuable input to the development of the road network. Traffic sensors in Stockholm’s road network are represented as a directed weighted graph and the congestion detection problem is formulated as a streaming graph processing problem. The connected components algorithm and existing graph processing algorithms originally used for community detection in social network graphs are adapted for the task of road congestion detection. The results indicate that a congestion detection method based on the streaming connected components algorithm and the incremental Dengraph community detection algorithm can detect congestion with accuracy at best up to 94% for connected components and up to 88% for Dengraph. A method based on hierarchical clustering is able to detect congestion while missing details such as shockwaves, and the Louvain modularity algorithm for community detection fails to detect congested regions in the traffic sensor graph.Finally, the performance of the implemented streaming algorithms is evaluated with respect to the real-time requirements of the system, their throughput and memory footprint.
Vägtrafikstockningar 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.
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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, police activities, weather and others, iii) extract fine-grained location information about the traffic incident by analyzing tweet text. Our experimental results show that using Twitter as a social sensor for obtaining rich information about traffic events is indeed a promising approach. We show that we can correctly detect traffic related tweets with an accuracy of 81%. Moreover, the accuracy of correctly classifying traffic related tweets into one of the six categories is 97%. Lastly, our experimental results show that using only geo-tags of tweets is not sufficient for fine-grained localization of traffic incidents due to two reasons: i) a vast majority of traffic related tweets do not contain geo-tags, and ii) the location mentioned in the tweet text and the geo-tag of a tweet do not always agree. Such observations prove that fine-grained localization of traffic incidents from tweet must also include analysis of the tweet text using Natural Language Processing techniques.
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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 networks. To achieve this, substantially high Link Journey Time estimates (LJTs) on adjacent links that occur at the same time are clustered. Substantially high LJTs are defined in two different ways: (i) those LJTs that are greater than a threshold, (ii) those LJTs that belong to a statistically significant Space-Time Region (STR). These two different ways of defining the term ‘substantially high LJT’ lead to different NRC detection methods. To evaluate these methods, two novel criteria are proposed. The first criterion, high-confidence episodes, assesses to what extent substantially high LJTs that last for a minimum duration are detected. The second criterion, the Localisation Index, assesses to what extent detected NRCs could be related to incidents. The proposed NRC detection methodology is tested for London’s urban road network, which consists of 424 links. Different levels of travel demand are analysed in order to establish a complete understanding of the developed methodology. Optimum parameter settings of the two proposed NRC detection methods are determined by sensitivity analysis. Related to the first method, LJTs that are at least 40% higher than their expected values are found to maintain the best balance between the proposed evaluation criteria for detecting NRCs. Related to the second method, it is found that constructing STRs by considering temporal adjacencies rather than spatial adjacencies improves the performance of the method. These findings are applied in real life situations to demonstrate the advantages and limitations of the proposed NRC detection methods. Traffic operation centres could readily start using the proposed NRC detection methodology. In this way, traffic operators could be able to quantify the impact of incidents and develop effective NRC reduction strategies.
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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 Learning, and Natural Language Processing techniques are used. These models are designed in such a way that they are able to detect the traffic congestion and traffic incidents from the Twitter stream at any location. Currently, the data is collected only for United States. The data is collected for 85 days (50 complete and 35 partial) randomly sampled over the span of five months (September, 2014 to February, 2015) and a total of 120,000 geo-tagged traffic related tweets are extracted, while six million geo-tagged non-traffic related tweets are retrieved. The classification models for detection of traffic congestion and incidents are trained on this dataset. Furthermore, this data is also used for various kinds of spatial and temporal analysis. A mechanism to calculate level of traffic congestion, safety, and traffic perception for cities in U.S. is proposed. Traffic congestion and safety rankings for the various urban areas are obtained and then they are statistically validated with existing widely adopted rankings. Traffic perception depicts the attitude and perception of people towards the traffic. It is also seen that traffic related data when visualized spatially and temporally provides the same pattern as the actual traffic flows for various urban areas. When visualized at the city level, it is clearly visible that the flow of tweets is similar to flow of vehicles and that the traffic related tweets are representative of traffic within the cities. With all the findings in current study, it is shown that significant amount of traffic related information can be extracted from Twitter and other sources on internet. Furthermore, Twitter and these data sources are freely available and are not bound by spatial and temporal limitations. That is, wherever there is a user there is a potential for data.
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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 grid based discretization of space, the proposed system extracts and main-tains traffic flow statistics and mobility statistics from the grid based recent trajectoriesof moving objects, and captures periodical spatio-temporal changes in the traffic flowsand movements by managing statistics for relevant temporal domain projections, i.e.,hour-of-day and day-of-week. Then, the proposed system identifies a directional con-gestion as a cell and its immediate neighbor, where the speed and flow of the objectsthat have moved from the neighbor to the cell significantly deviates from the histori-cal speed and flow statistics. Subsequently, based on one of two notification criteria,namely, Mobility Statistic Criterion (MSC) and Linear Movement Criterion (LMC),the system decides which objects are likely to be affected by the identified conges-tions and sends out notifications to the corresponding objects such that the numberof false negative (missed) and false positive (unnecessary) notifications is minimized.The thesis discusses the design and DBMS-based implementation of the proposedsystem. Empirical evaluations on realistically simulated trajectory data assess the ac-curacy of the methods and test the scalability of the system for varying input sizes andparameter settings. The accuracy assessment results show that the MSC based systemachieves an optimal performance with a true positive notification rate of 0.67 and afalse positive notification rate of 0.05 when min prob equals to 0.35, which is superiorto the performance of the LMC based system. The execution time of- and the spaceused by the system scales linearly with the input size (number of concurrently movingvehicles) and the methods mutually dependent parameters (grid resolution r and RTlength l) that jointly define a spatio-temporal resolution. Within the area of a large  city (40km by 40km), assuming a 60km/h average vehicle speed, the system, runningon a commodity personal computer, can manage the described congestion detectionand three-minute-ahead notification tasks within real-time requirements for 2000 and20000 concurrently moving vehicles for spatio-temporal resolutions (r=100m, l=19)and (r=2km, l=3), respectively.
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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 prediction errors produced by the LSTM network. The intersection rule significantly decreases the number of false positives while maintaining the true positive rate at a sufficient level. The performance of the anomaly detection algorithm has been found to depend on the road segment it is applied to, some segments have been identified to be particularly hard whereas other have been identified to be easier than others. The performance of the best performing setup of the anomaly detection mechanism had a true positive rate of 84.3 % and a true negative rate of 96.0 %.
I 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.
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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 the implementation of road safety procedures and access to various features of internet like multimedia, emails, etc. VANETs make use of the on-board communication abilities of a vehicle and pre-installed roadside Units to achieve this goal. One of the most interesting areas of research is the analysis of road traffic. This includes vehicle path tracking, path prediction, intelligent vehicles, congestion detection and many more. Most of the research that has been done to detect traffic congestion used vehicular adhoc network (VANET) but of late data mining approach has been applied. Though most of the proposed work has successfully detected traffic congestion, it is complex to come up with an effective mechanism that incorporates detection, control and prediction of recurrent and nonrecurrent traffic congestions all in one system.
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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. [Olympia, Wash.?]: 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. [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.

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A busy day in Busytown. New York, N.Y: Simon Spotlight, 2010.

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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 to understand whether such advertising could become a public health issue. This briefing examines different works that have led to scientific publications and presents their main conclusions as well as the major recommendations for gambling companies and regulators. 2. How can artificial intelligence reduce road traffic accidents and prevent congestion? This briefing seeks to present the benefits of the TIMON system for optimising traffic management and urban transport network operations in cities, directly supporting transport managers in their decision-making processes for transport operations. 3. Gender inequalities in matters of drug addiction: how does alcoholism really affect women? aims to study the phenomenon of drug dependence from a gender perspective. This involves identifying what kind of socio-cultural and psychological representations are involved in women, according to their gender role, so that they develop a series of risk factors for them, both for the beginning of consumption and in its continuity. In addition, the research team proposes guidelines for a specialized care for women in this area, in order to increase the effectiveness of required interventions. 4. Key points for supporting and accompanying victims and survivors of human trafficking for sexual exploitation is intended as a working document for specialists involved in the prevention and detection of cases and in support and care for victims. It seeks to fill the current gaps and meet the needs of women victims of trafficking providing a better response to their situations.
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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, 527–34. Singapore: 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, 99–108. Singapore: 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, 3–22. Cham: 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, 317–27. Singapore: 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, 506–18. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0356-1_54.

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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, 227–38. Cham: 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, 249–62. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9129-7_18.

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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, 4569–80. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8155-7_378.

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Oumaima, 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.

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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, 37–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13705-2_2.

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Conference papers on the topic "TRAFFIC CONGESTION DETECTION"

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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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Anjum, 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.

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Dimri, 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.

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Liu, 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.

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Khalil, 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.

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Sommer, 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.

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Manjoro, 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.

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Rao, 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.

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Reports on the topic "TRAFFIC CONGESTION DETECTION"

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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.

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