Relevant bibliographies by topics / Traffic congestion

Academic literature on the topic 'Traffic congestion'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Traffic congestion"

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Zhang, Liangliang, Yuanhua Jia, Zhonghai Niu, and Cheng Liao. "Widespread Traffic Congestion Prediction for Urban Road Network Based on Synergetic Theory." Journal of Systems Science and Information 2, no. 4 (August 25, 2014): 366–71. http://dx.doi.org/10.1515/jssi-2014-0366.

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AbstractThe traffic congestion often occurs in urban road network. When one of the sections becomes congested, it will lead to a series of congestions in other sections. The traffic congestion spreads rapidly until part of road network becomes congestion ultimately. In this case, the paper investigates the mechanism of the traffic congestion in urban road network and points out that subsystems of the traffic congestion always perform completive and cooperative functions in the process of traffic congestion. The process behaves in a manner of self-organized criticality, which can be forecasted. The paper also establishes synergetic predictive models based on self-organized criticality of the synergetic theory. Finally, the paper takes Beijing road network as an example to forecast the widespread traffic congestion. The result shows that the established models are accuracy, and the traffic congestion is featured of self-organized criticality.
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Nguyen, Minh Quyen, Thi Thanh Xuan Pham, and Thi Thuy Hoa Phan. "Traffic Congestion." European Journal of Engineering Research and Science 4, no. 9 (September 19, 2019): 112–16. http://dx.doi.org/10.24018/ejers.2019.4.9.1524.

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Congestion is essentially a relative phenomenon that is linked to the difference between the roadway system performance that users expect and how the system actually performs. The approach to traffic congestion (TC) from the different perspectives of countries around the world helps Vietnamese experts to better understand and come up with a consensus on how to understand the problem. this topic. Since then, in the management of traffic safety and control, to curb traffic congestion, traffic police and other departments have the basis to perform the work, properly assess the level of traffic safety to implement the task; doing well the reporting, statistics, building databases on TC; create integration in the context of international cooperation with other countries. Solving the problem of traffic congestion in large cities in Vietnam is a problem that cannot be solved overnight. This problem requires the synchronous participation of many ministries, branches and people in traffic. All measures need to get feedback from the people, thoroughly resolve the critic before implementation. This article briefly presents the situation of traffic congestion in Hanoi and Ho Chi Minh City. The team also focused on analyzing the causes of traffic congestion and proposed solutions to solve this urban problem.
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Nguyen, Minh Quyen, Thi Thanh Xuan Pham, and Thi Thuy Hoa Phan. "Traffic Congestion." European Journal of Engineering and Technology Research 4, no. 9 (September 19, 2019): 112–16. http://dx.doi.org/10.24018/ejeng.2019.4.9.1524.

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Congestion is essentially a relative phenomenon that is linked to the difference between the roadway system performance that users expect and how the system actually performs. The approach to traffic congestion (TC) from the different perspectives of countries around the world helps Vietnamese experts to better understand and come up with a consensus on how to understand the problem. this topic. Since then, in the management of traffic safety and control, to curb traffic congestion, traffic police and other departments have the basis to perform the work, properly assess the level of traffic safety to implement the task; doing well the reporting, statistics, building databases on TC; create integration in the context of international cooperation with other countries. Solving the problem of traffic congestion in large cities in Vietnam is a problem that cannot be solved overnight. This problem requires the synchronous participation of many ministries, branches and people in traffic. All measures need to get feedback from the people, thoroughly resolve the critic before implementation. This article briefly presents the situation of traffic congestion in Hanoi and Ho Chi Minh City. The team also focused on analyzing the causes of traffic congestion and proposed solutions to solve this urban problem.
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Mandayam, Chinmoy V., and Balaji Prabhakar. "Traffic congestion." ACM SIGMETRICS Performance Evaluation Review 42, no. 1 (June 20, 2014): 553–54. http://dx.doi.org/10.1145/2637364.2592014.

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Chen, Yongjun, Ming Huang, Kaixuan Song, and Tengfei Wang. "Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy c-Means Clustering." Journal of Advanced Transportation 2023 (May 20, 2023): 1–12. http://dx.doi.org/10.1155/2023/7175863.

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Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute a hybrid modelling approach for predicting individual modal components and integrating the results of individual components. Considering a map between ship traffic flow parameters and congestion, fuzzy c-means clustering is used to predict the level of ship traffic congestion. To verify the effectiveness of the proposed method, ship traffic flow data of the Yangtze River estuary were selected for evaluation. Results from the proposed method for predicting ship traffic flow parameters are consistent with measurements. Specifically, the prediction accuracy of the ship traffic congestion reaches 76.04%, which is reasonable and practical for predicting ship traffic congestion.
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Adeleke, A. K., and R. M. Gebashe. "DEVELOPING SPATIAL DECISION SUPPORT SYSTEM TO ASSESS TRAFFIC CONGESTION IN THE CITY OF JOHANNESBURG." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (May 18, 2022): 145–51. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-145-2022.

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Abstract. African cities are rapidly experiencing an increase in population, thereby making it difficult to attain self-sustainability. Traffic congestion is a major contributing factor to this issue. Johannesburg's inner-city fits this profile, with an increasing decline in economic and social activities, and quality of life due to traffic congestion. Furthermore, the lack of a road transport infrastructure geodatabase and traffic data in these cities makes it more difficult for stakeholders to make an informed decision on how to effectively manage roads prone to traffic congestion or due for infrastructure upgrade. This paper focuses on developing a geodatabase using factors that cause traffic congestion such as bus stops, traffic lights, speed humps, t-joints, cross joints, street parking, and others. These factors were investigated on some selected roads within the Johannesburg inner-city by enumerating the number of such factors existing on each road with the aid of high-resolution aerial imagery. The developed geodatabase becomes a tool that can support the decision-making process in solving traffic congestion by querying the geodatabase to select roads that are prone to traffic congestions depending on the number of factors occurring along a road.
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Aoyagi, Saizo, Yiping Le, Tetsuo Shimizu, and Kazuki Takahashi. "Mobile Application to Provide Traffic Congestion Estimates and Tourism Spots to Promote Additional Stopovers." Future Internet 12, no. 5 (April 29, 2020): 83. http://dx.doi.org/10.3390/fi12050083.

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In Japan, traffic congestions often occur on the expressways connecting tourism areas with the Tokyo metropolitan area. This congestion can be mitigated if tourists delayed their departure of homeward trips to avoid peak traffic hours. A potential method to promote staggered departure times is providing the estimates of near-future traffic congestion. This study hypothesized and experimentally confirmed that some tourists would delay their departure to avoid traffic based on near-future traffic estimates. The experiment was conducted in the Yatsugatake area using a mobile application that provided this information to tourists. The results suggest that approximately 40% of self-driving tourists will perform an additional stopover if the returning route is congested and near-future traffic congestion estimate is provided.
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Muslih, Muneer, Ahmed Abduljabbar, and Hasan Joni. "Review of traffic demand management strategies." IOP Conference Series: Earth and Environmental Science 1232, no. 1 (September 1, 2023): 012055. http://dx.doi.org/10.1088/1755-1315/1232/1/012055.

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Abstract This review article seeks to determine the most effective strategies to reduce traffic congestion which have been implanted in different countries and cities around the world. In this review, previous research work was gathered focusing on approaches for mitigating traffic congestion issues. The paper discovered that there are two ways to control congestion which are through supply-side actions, and demand-side actions, the paper also discovered that traffic modelling and simulation software packages are crucial to decision-makers because of its capability to evaluate and simulate the effects of any suggested improvements or strategies to reduce traffic congestions for a certain street network and for a whole city.
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Boarnet, Marlon G., Eugene Jae Kim, and Emily Parkany. "Measuring Traffic Congestion." Transportation Research Record: Journal of the Transportation Research Board 1634, no. 1 (January 1998): 93–99. http://dx.doi.org/10.3141/1634-12.

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A traffic congestion index was developed using data for California highways from 1976 through 1994. The technique yields a congestion measure that has several advantages. The index developed here can be applied to counties, urbanized areas, highway segments, or other portions of geographic areas or highway networks. The index allows cross-sectional and time-series comparisons that have only rarely been possible. What is most important is that the congestion index developed here is based on data that are readily available. The index is compared to others, based on Highway Performance Monitoring System data, and similarities and differences are illustrated. Important issues for future research and data collection efforts that can contribute to more refined congestion measurement are also discussed.
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Henry, J. J., and J. L. Farges. "Traffic Congestion Control." IFAC Proceedings Volumes 23, no. 2 (September 1990): 177–82. http://dx.doi.org/10.1016/s1474-6670(17)52668-3.

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Dissertations / Theses on the topic "Traffic congestion"

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Bojs, Eric. "Quantifying Traffic Congestion in Nairobi." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275684.

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This thesis aims to give insight into a novel approach for quantifying car traffic in developing cities. This is necessary to improve efficiency in resource allocation for improvements in infrastructure. The project took form of a case study of neighborhoods in the city of Nairobi, Kenya. The approach consists of a method which relies on topics from the field of Topological Data Analysis, together with the use of large data sources from taxi services in the city. With this, both qualitative and quantitative insight can be given about the traffic. The method was proven useful for understanding how traffic spreads, and to differentiate between levels of congestion: quantifying it. However, it failed to detect the effect of previous improvements of infrastructure.
Målet med rapporten är att ge insikt i en innovativ ansats för att kvantifiera biltrafik i utvecklingsstäder. Detta kommer som en nödvändighet för att kunna förbättra resursfördelning i utvecklandet av infrastruktur. Projektet utspelade sig som en fallstudie där stadsdelar i Nairobi, Kenya studerades. Ansatsen innefattar en metod som bygger på tekniker från topologisk dataanalys (eng. \textit{Topological Data Analysis}), tillsammans med stora datakällor från taxitjänster i staden. Detta hoppas ge både kvalitativ och kvantitativ information om trafiken i staden. Metoden visade sig vara användbar för att förstå hur trafik sprider sig och att differentiera mellan nivåer av trafik, alltså att kvantifiera den. Tyvärr så misslyckades metoden visa sig användbar för att mäta förbättringar i infrastruktur.
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Lerz, Stefan. "Congestion theory and railway traffic /." Capelle a/d IJssel : Labyrint, 1996. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=007375963&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Wong, Wan-ho. "Bus to bus interchange : solution for traffic congestion in Hong Kong Island /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk:8888/cgi-bin/hkuto%5Ftoc%5Fpdf?B23425787.

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Ramakrishna, Sajja D. "An approach to predict traffic congestion." Thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-09192009-040304/.

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Welzl, Michael. "Network congestion control : managing Internet traffic /." Chichester [u.a.] : Wiley, 2005. http://www.loc.gov/catdir/toc/ecip0513/2005015429.html.

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Lee, Tak-kwong. "A study of the traffic congestion problem in Hong Kong : a case study of Wanchai district /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19906511.

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Huisken, Giovanni. "Inter-urban short-term traffic congestion prediction." Enschede : University of Twente [Host], 2006. http://doc.utwente.nl/57639.

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Drum, David K. "Counteracting traffic congestion using intelligent driver feedback." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10180875.

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Traffic congestion is a daily occurrence in urban highway networks worldwide. It is not possible, however, for society to build its way out of congestion; rather, smarter roads and vehicles are needed. While the development of a smarter transportation system is underway, full implementation is years or decades from now. Yet, some of the sensing technology needed for smarter vehicles is already widely deployed in the form of smart phones. This thesis develops a novel method for recognizing traffic congestion using an artificially intelligent heuristic that could be implemented in a smart phone application or embedded system. Its goal is to provide intelligent feedback to a driver or autonomous vehicle control system to counteract stop-and-go traffic, a defining feature of urban highway congestion. Evaluation of the method indicates that a specific condition during stop-and-go traffic can be recognized accurately. A driver or control system acting upon feedback provided by the artificially intelligent system can improve traffic flow on the roadway by 1% to 3.5% over the course of the test duration.

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Arya, Vijay. "Congestion inference and traffic engineering in networks." Nice, 2005. http://www.theses.fr/2005NICE4079.

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Cette thèse présente des mécanismes pour obtenir de meilleures estimations de la congestion sur Internet. Elle présente également un mécanisme ayant des applications sur le trafic multipoint dans des réseaux overlay. Tout d’abord, nous présentons une méthode de différenciation des pertes qui permet aux protocoles de transport non fiables d’estimer avec précision la congestion de bout-en-bout, en distinguant les pertes liées à la congestion des pertes liées aux erreurs de transmission sur les liaisons sans fil. Ensuite, nous présentons deux contributions relatives à un outil de tomographie de réseau. Nous proposons d’une part un algorithme statistique qui vérifie les mesures binaires effectuées en multipoint par MINC pour estimer les pertes. Cet algorithme permet de garantir une estimation fiable des taux de pertes sur les différents liens. D’autre part, nous proposons une nouvelle version de l’estimateur de perte MINC. Notre estimateur utilise les informations multipoints cumulées fournies en retour pour évaluer les taux de perte sur tous les liens. Il peut être utilisé dans des situations où la bande passante pour transmettre les rapports de réception est faible. Enfin, nous présentons des techniques efficaces pour encoder les arbres de transmission multipoint au sein des paquets de données. Ces techniques d’encodage peuvent être utilisées pour implanter un routage multipoint explicite et sans état dans des réseaux overlay et ont donc des applications dan le domaine d’ingénierie de trafic multipoint
This thesis presents methods which help to improve the quality of congestion inference on both en-to-end paths and internal network links in the Internet and a method which help to perform multicast traffic engineering in Overlay Networks. First, we propose an explicit loss differentiation scheme which allows unreliable transport protocols to accurately infer congestion on end-to-end paths by correctly differentiating congestion losses from wireless losses. Second, we present two contributions related to Multicast-based Inference of Network Characteristics (MINC). MINC is a method of performing network tomography which infers loss rates, i. E. , congestion on internal network links from end-to-end multicast measurements. We propose a statistical verification algorithm which can verify the integrity of binary multicast measurements used by MINC to perform loss inference. This algorithm helps to ensure a trustworthy inference of link loss rates. Next, we propose an extended MINC loss estimator which can infer loss rates of network links using aggregate multicast feedbacks. This estimator can be used to perform loss inference in situations where the bandwidth to report multicast feedbacks is low. Third, we present efficient ways of encoding multicast trees within data packets. These encodings can be used to perform stateless and explicit multicast routing in overlay networks and thus achieve goals of multicast traffic engineering
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Raman, Mala. "Supply management measures for alleviating urban traffic congestion." Master's thesis, This resource online, 1994. http://scholar.lib.vt.edu/theses/available/etd-03302010-020519/.

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Books on the topic "Traffic congestion"

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Clark, Charles S. Traffic Congestion. 2455 Teller Road, Thousand Oaks California 91320 United States: CQ Press, 1994. http://dx.doi.org/10.4135/cqresrre19940506.

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Hosansky, David. Traffic Congestion. 2455 Teller Road, Thousand Oaks California 91320 United States: CQ Press, 1999. http://dx.doi.org/10.4135/cqresrre19990827.

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Hosansky, David. Traffic Congestion. 2455 Teller Road, Thousand Oaks California 91320 United States: CQ Press, 2018. http://dx.doi.org/10.4135/cqresrre20180126.

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Victoria. Office of the Auditor-General. Managing traffic congestion. Melbourne, Vic: Victorian Government Printer, 2013.

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Institution of Civil Engineers (Great Britain). Infrastructure Policy Group., ed. Congestion. London: Telford, 1989.

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Organisation for Economic Co-operation and Development., European Conference of Ministers of Transport., and Transport Research Centre, eds. Managing urban traffic congestion. Paris: OECD, 2007.

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Tilmann, Rave, and Schöb Ronnie, eds. Alleviating urban traffic congestion. Cambridge, Mass: MIT Press, 2005.

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Nihan, N. L. Freeway congestion prediction. [Olympia, Wash.]: Washington State Dept. of Transportation, 1995.

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Lindsey, Robin. Congestion modelling. Edmonton, Alta: Dept. of Economics, University of Alberta, 1999.

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Mallett, William. Surface transportation congestion. New York: Nova Science Publishers, 2008.

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Book chapters on the topic "Traffic congestion"

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Mazurkeviciute, Aide. "Traffic Congestion." In Encyclopedia of Law and Economics, 1–12. New York, NY: Springer New York, 2023. http://dx.doi.org/10.1007/978-1-4614-7883-6_801-1.

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Falcocchio, John C., and Herbert S. Levinson. "Measuring Traffic Congestion." In Road Traffic Congestion: A Concise Guide, 93–110. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15165-6_8.

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Mallard, Graham, and Stephen Glaister. "Tackling Traffic Congestion." In Transport Economics, 249–76. London: Macmillan Education UK, 2010. http://dx.doi.org/10.1007/978-1-137-06823-1_16.

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Kerner, B. S., H. Rehborn, and M. Aleksic. "Forecasting of Traffic Congestion." In Traffic and Granular Flow ’99, 339–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59751-0_32.

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Salter, R. J. "Traffic congestion and restraint." In Highway Traffic Analysis and Design, 259–69. London: Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13423-6_27.

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Salter, R. J. "Traffic congestion and restraint." In Highway Traffic Analysis and Design, 245–62. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-20014-6_27.

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Falcocchio, John C., and Herbert S. Levinson. "Managing Nonrecurring Congestion." In Road Traffic Congestion: A Concise Guide, 197–211. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15165-6_15.

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Delahaye, Daniel, and Stéphane Puechmorel. "Airspace Congestion Metrics." In Modeling and Optimization of Air Traffic, 243–89. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118743805.ch7.

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Pérez Herrero, María, Julien Brunel, and Gregoire Marlot. "Rail Externalities: Assessing the Social Cost of Rail Congestion." In Traffic Management, 331–44. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307822.ch23.

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Kerner, Boris S. "Spatiotemporal Features of Traffic Congestion." In Complex Dynamics of Traffic Management, 387–500. New York, NY: Springer US, 2019. http://dx.doi.org/10.1007/978-1-4939-8763-4_560.

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Conference papers on the topic "Traffic congestion"

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Ghali, M. O. "Managing traffic congestion by using traffic control." In IEE Colloquium on Urban Congestion Management. IEE, 1995. http://dx.doi.org/10.1049/ic:19951300.

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Mandayam, Chinmoy V., and Balaji Prabhakar. "Traffic congestion." In The 2014 ACM international conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2591971.2592014.

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Bell, M. G. H. "Traffic restraint and urban decay." In IEE Colloquium on Urban Congestion Management. IEE, 1995. http://dx.doi.org/10.1049/ic:19951294.

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Ramos, Gabriel O., Ana L. C. Bazzan, and Bruno C. Da Silva. "Regret Minimisation and System-Efficiency in Route Choice." In XXXII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/ctd.2019.6332.

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Traffic congestions present a major challenge in large cities. Consid- ering the distributed, self-interested nature oftraffic we tackle congestions using multiagent reinforcement learning (MARL). In this thesis, we advance the state- of-the-art by delivering the first MARL convergence guarantees in congestion- like problems. We introduce an algorithm through which drivers can learn opti- mal routes by locally estimating the regret associated with their decisions, which we prove to converge to an equilibrium. In order to mitigate the effects ofselfish- ness, we also devise a decentralised tolling scheme, which we prove to minimise traffic congestion levels. Our theoretical results are supported by an extensive empirical evaluation on realistic traffic networks. 1.
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Ye Xaofei and Chen Jun. "Analysis on traffic congestion propagation influenced by traffic congestion information." In 2011 International Conference on Electric Technology and Civil Engineering (ICETCE). IEEE, 2011. http://dx.doi.org/10.1109/icetce.2011.5775757.

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Hounsell, N. B. "Protecting buses from congestion using traffic signal control." In IEE Colloquium on Urban Congestion Management. IEE, 1995. http://dx.doi.org/10.1049/ic:19951301.

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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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Sayers, T. M. "Improving the traffic responsiveness of signal controllers using fuzzy logic." In IEE Colloquium on Urban Congestion Management. IEE, 1995. http://dx.doi.org/10.1049/ic:19951298.

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Deshmukh, Shubhangi M., and Bhairavi N. Savant. "Traffic congestion alerting system." In 2016 International Conference on Computing Communication Control and automation (ICCUBEA). IEEE, 2016. http://dx.doi.org/10.1109/iccubea.2016.7860110.

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Bell, M. G. H. "Traffic diversion due to motorway tolls." In IEE Seminar on Electronic Tolling and Congestion Charging. IEE, 1999. http://dx.doi.org/10.1049/ic:19990520.

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Reports on the topic "Traffic congestion"

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Snyder, Corey, Daniel Gonzales, Minh Do, and Tian Ma. Congestion Estimation Using Traffic Cameras. Office of Scientific and Technical Information (OSTI), May 2019. http://dx.doi.org/10.2172/1762323.

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., Susilawati. Will self-driving cars solve traffic congestion? Edited by Sara Phillips. Monash University, February 2023. http://dx.doi.org/10.54377/1f93-023d.

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

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Chin, Shih-Miao, Patricia S. Hu, and Diane Davidson. Making the Traffic Operations Case for Congestion Pricing: Operational Impacts of Congestion Pricing. Office of Scientific and Technical Information (OSTI), February 2011. http://dx.doi.org/10.2172/1048704.

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Ewing, Reid, Guang Tian, and Torrey Lyons. Does Compact Development Increase or Reduce Traffic Congestion? Transportation Research and Education Center, October 2017. http://dx.doi.org/10.15760/trec.187.

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Bento, Antonio, Kevin Roth, and Andrew Waxman. Avoiding Traffic Congestion Externalities? The Value of Urgency. Cambridge, MA: National Bureau of Economic Research, April 2020. http://dx.doi.org/10.3386/w26956.

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Bigazzi, Alexander. Traffic Congestion Mitigation as an Emissions Reduction Strategy. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.131.

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Zhu, X., S. Mena, and Z. Sarker. Video Traffic Models for RTP Congestion Control Evaluations. RFC Editor, May 2019. http://dx.doi.org/10.17487/rfc8593.

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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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Abstract:
In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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10

Kumar, Kaushal, and Yupeng Wei. Attention-Based Data Analytic Models for Traffic Flow Predictions. Mineta Transportation Institute, March 2023. http://dx.doi.org/10.31979/mti.2023.2211.

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Abstract:
Traffic congestion causes Americans to lose millions of hours and dollars each year. In fact, 1.9 billion gallons of fuel are wasted each year due to traffic congestion, and each hour stuck in traffic costs about $21 in wasted time and fuel. The traffic congestion can be caused by various factors, such as bottlenecks, traffic incidents, bad weather, work zones, poor traffic signal timing, and special events. One key step to addressing traffic congestion and identifying its root cause is an accurate prediction of traffic flow. Accurate traffic flow prediction is also important for the successful deployment of smart transportation systems. It can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, it can also help reduce carbon emissions and the risks of traffic incidents. Although numerous methods have been developed for traffic flow predictions, current methods have limitations in utilizing the most relevant part of traffic flow data and considering the correlation among the collected high-dimensional features. To address this issue, this project developed attention-based methodologies for traffic flow predictions. We propose the use of an attention-based deep learning model that incorporates the attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This attention mechanism can calculate the importance level of traffic flow data and enable the model to consider the most relevant part of the data while making predictions, thus improving accuracy and reducing prediction duration.
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