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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Navin, Francis P. D. "Traffic congestion catastrophes." Transportation Planning and Technology 11, no. 1 (June 1986): 19–25. http://dx.doi.org/10.1080/03081068608717327.
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Faraj, Mohammed Abdulmaged, and Najmadin Wahid Boskany. "Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network." UHD Journal of Science and Technology 4, no. 2 (December 10, 2020): 123–31. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp123-131.
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Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communications. In this paper, we propose an intelligent, low cost, and efficient microcontroller circuit-based system for controlling cars in traffic light. This system can manage car traffics smarter than traditional approaches, it is capable to dynamically adjust timings of traffic signal. It can rapidly respond to traffic conditions to reduce traffic congestion. For implementing this system, a server, microcontroller board, cameras, as hardware and wireless network between traffic lights as infrastructure for communication are used. The system uses machine learning technique (i.e.,Yolov3 model and OpenCV) for decision depending on existence of emergency cars and number of cars. The experiment results show higher accuracy in managing traffic lights and recognizing the emergency cars.
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Du, Hao Hua, and Meng Pan. "Research on the Influence of Last Digit Restriction to the Traffic Flow Characteristics." Applied Mechanics and Materials 253-255 (December 2012): 1976–81. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1976.
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The Beijing government is taking measures to remit traffic congestions by restriction based on the last digit on a license plate, which influences the characteristic of traffic flow. This paper first builds a model about traffic flow characteristic, including vehicle velocity, roadway reliability and traffic congestion recovery time. The paper also researches different pattern of vehicle velocity before and after the rail number restriction, analyzes the changing trend of road-mobility reliability with traffic demands, and calculates the pattern of traffic congestion recovery time between the restrictions. The result indicates that when traffic flow’s density is large, the vehicle’s velocity is increased to 180% of the original after the restriction; and the recovery time from congestion to smooth is decreased to 75.8% of the original; after taken the restriction measures, the road unblocked reliability in Peak hours is increased, and the traffic condition ameliorate evidently.
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Zhao, Ze Bin, and Yu Tian. "Traffic Behaviors Simulation under Congestion Pricing Revenue Redistribution Strategy." Applied Mechanics and Materials 253-255 (December 2012): 1982–87. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1982.
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On the basis of behavioral model under congestion pricing revenue redistribution, this paper proposes the general idea, process of the simulation and designs the essential simulation module of congestion pricing revenue redistribution based on the Multi-Agent technology and Starlogo. Traffic behaviors are simulated under the situation that congestion pricing revenue is used to raise the level of bus service and extend the road capacity. Also the simulation is compared with other two situations with and without congestion pricing. The results demonstrate that this method overcomes the localization of the traditional micro traffic flow simulation model, and the traffic flow of congesting section can be effectively reduced and distributed more optimal.
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Lutsenko, V. V., N. N. Kucherov, and A. V. Gladkov. "Predicting traffic congestion based on time series analysis." Sovremennaya nauka i innovatsii, no. 2 (42) (2023): 50–58. http://dx.doi.org/10.37493/2307-910x.2023.2.5.
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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion.
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Lutsenko, V. V., N. N. Kucherov, and A. V. Gladkov. "PREDICTING TRAFFIC CONGESTION BASED ON TIME SERIES ANALYSIS." Sovremennaya nauka i innovatsii, no. 1 (41) (2023): 47–55. http://dx.doi.org/10.37493/2307-910x.2023.1.4.
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Traffic congestion is a serious problem in many cities, resulting in lost time, increased air pollution, and reduced quality of life. In the past few years, time series models have been widely used to predict traffic flows and congestion. This study analyzes traffic data collected over several years and develops a predictive model based on time series analysis techniques. The model takes into account various factors that contribute to congestion, such as time of day, day of the week, and junction. The results show that the model effectively predicts traffic congestion with a high degree of accuracy, which can be used to make rational decisions and reduce urban traffic congestion
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Shimizu, H., and E. J. Davison. "Traffic Congestion Control of Traffic Network." IFAC Proceedings Volumes 25, no. 18 (August 1992): 117–22. http://dx.doi.org/10.1016/s1474-6670(17)49955-1.
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Wang, Jian Ling, and Hong Bo Lai. "Congestion Analysis on Urban Traffic Network." Advanced Materials Research 756-759 (September 2013): 1635–38. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1635.
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this paper explores the urban traffic congestion problems, from four angles: the discrimination of the traffic congestion, cause of traffic congestion, congestion characteristics of urban traffic network, measures to solve traffic congestion, to conduct in-depth analysis of traffic congestion.
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Marks, Danny. "An urban political ecology of Bangkok's awful traffic congestion." Journal of Political Ecology 27, no. 1 (July 27, 2020): 732–58. http://dx.doi.org/10.2458/v27i1.23604.
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Urban political ecology (UPE) can contribute important insights to examine traffic congestion, a significant social and environmental problem underexplored in UPE. Specifically, by attending to power relations, the production of urban space, and cultural practices, UPE can help explain why traffic congestions arises and persists but also creates inequalities in terms of environmental impacts and mobility. Based on qualitative research conducted in 2018, the article applies a UPE framework to Bangkok, Thailand, which has some of the world's worst congestion in one of the world's most unequal countries. The city's largely unplanned and uneven development has made congestion worse in a number of ways. Further, the neglect of public transport, particularly the bus system, and the highest priority given to cars has exacerbated congestion but also reflects class interests as well as unequal power relations. Governance shortcomings, including fragmentation, institutional inertia, corruption, and frequent changes in leadership, have also severely hindered state actors to address congestion. However, due to the poor's limited power, solutions to congestion, are post-political and shaped by elite interests. Analyses of congestion need to consider how socio-political relations, discourses, and a city's materiality shape outcomes.Key Words: urban transport governance, Bangkok traffic congestion, urban political ecology, Thailand political economy, Bangkok's bus system
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Langa, Makhulu Relebogile, and Michael Nthabiseng Moeti. "A Real-Time Notification System for Traffic Congestion on South African National Routes." International Conference on Intelligent and Innovative Computing Applications 2022 (December 31, 2022): 79–91. http://dx.doi.org/10.59200/iconic.2022.009.
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Transportation is an integral part of our daily life, and now with an ever increasing number of cars on the roads, traffic congestion is inevitable. Traffic congestion has a huge impact on service delivery and, in turn, on the economy of the country. Social network has revolutionized our lives, and commuters are now able to vent their frustrations and post live updates regarding these congestions. Social networks have enabled humans to become active live sensors participating in the network communication paradigm. This paper leverages Naïve Bayes classifier of Artificial Intelligence (AI) for data classification, .NET MAUI framework for application development, Social Network Scraping tool for collecting traffic congestion data from Twitter, and Sklearn library of Python to prepare, clean, and make meaning out of the Twitter data. The outcome of the notification system will assist commuters to plan their trips efficiently using alternative roads as suggested by the real-time traffic congestion notification system developed.
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Liu, Xingliang, Jian Wang, Tangzhi Liu, and Jin Xu. "Forecasting Spatiotemporal Boundary of Emergency-Event-Based Traffic Congestion in Expressway Network Considering Highway Node Acceptance Capacity." Sustainability 13, no. 21 (November 4, 2021): 12195. http://dx.doi.org/10.3390/su132112195.
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Emergency events can induce serious traffic congestions in a local area which may propagate to the upstream roads, and even the whole network. Until now, the methodology forecasting spatiotemporal boundary propagation of emergency-event-based traffic congestions, with both explicitness and road network availability, has not been found. This study develops a new method for predicting spatiotemporal boundary of the congestion caused by emergency events, which is more applicable and practical than cell transmission model (CTM)-derived methods. This method divides the expressway network into different sections based on their functions and the shockwave direction caused by the emergency events. It characterizes the velocity of the moving congestion boundary based on kinetic wave theory and volume–density relationship. After determining whether the congestion will spread into the network level through an interchange using a new concept, highway node acceptance capacity (HNAC), we can predict the spatiotemporal boundary and corresponding traffic condition within the boundary. The proposed method is tested under four traffic incident cases with corresponding traffic data collected through field observations. We also compare its prediction performances with other methods used in the literature.
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Andika, Ilham, Andri Irfan Rifai, Muhammad Isradi, and Joewono Prasetijo. "A Traffic Management System for Minimization of Intersection Traffic Congestion: Case Bengkong Junction, Batam." IJEBD (International Journal of Entrepreneurship and Business Development) 5, no. 5 (September 30, 2022): 945–56. http://dx.doi.org/10.29138/ijebd.v5i5.1991.
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Purpose: An increase in the number of vehicles that are not matched by the growth of road area is one of the causes of road Traffic congestion in Batam City. Traffic congestion occurs, especially in business and densely populated residential areas. The government has carried out various engineering and traffic management to keep the road service level under control. This study aims to analyze traffic management's efficiency in reducing Traffic congestion on roads in Batam City.
Design/methodology/approach: This study uses a case study at the Bengkong red light intersection, Batam City. The analysis was carried out using The Indonesian Highway Capacity Manual-1997.
Findings: Traffic management and engineering must be implemented gradually to get a suitable implementation model. Actual conditions in the field become essential in determining the right choice of traffic management and engineering.
Research limitations/implications: Traffic management and engineering analyzes were conducted locally at the case study site.
Practical implications: The analysis of traffic management reduces traffic congestion's impact.
Originality/value: Original paper
Paper type: A case study
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Chu, Hung-Chi, Yi-Xiang Liao, Lin-huang Chang, and Yen-Hsi Lee. "Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning." Applied Sciences 9, no. 21 (October 27, 2019): 4558. http://dx.doi.org/10.3390/app9214558.
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In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.
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Venkatcharyulu, S., and V. Mallikarjunareddy. "Traffic volume Analysis of Newly Developing semi-urban Road." E3S Web of Conferences 184 (2020): 01116. http://dx.doi.org/10.1051/e3sconf/202018401116.
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The purpose of the study and analyze traffic vehicular at peak hours, traffic congestion, determine level of service and provide suitable solution to reduce congestion and improve level of service. Hyderabad is a rapidly urbanizing metropolitan city with a population of 68.1lakhs. The increasing population has led to a tremendous increase in vehicular ownership resulting in high motorization rates. Vehicular population in Hyderabad is about 50lakhs. This increase in vehicular population, congestion and hazardous traffic conditions have resulted in traffic congestions, traffic delays, accidents, environ, noise pollution, air pollution and many more. Traffic volume studies are conducted for the counting number vehicles in th study area . . Data obtained from Nizampet road from the Miyapur useful for the identification of Heavy vehicular flow which studied and number of vehicle movement has studied . The data collected from surveys will be used for determination(PHO) and analysing the traffic volume, passenger car unit (PCU), congestion, Peak hour factor and Directional distribution, volume capacity ratio. A report is to be made on the road of 7km, semi urban road Miyapur X road to Nizampet ,(Hyderabad,INDIA) is studied for vehicles count, road conditions, traffic conditions, peak hour volume, pedestrian count where congestion usually happens, manually using tally sheets and recommend some measures for increasing Level of service of the road. The semi urban road has newly grown village which is immediate vicinity to the developed area. Hence the study of This traffic vehicular moment will help the local authorities to take decisions and finally control over movement of the vehicles.
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Wang, Jian Ling, and Hong Bo Lai. "A Fuzzy Logic Method of Traffic State Recognition on Urban Traffic Networks." Advanced Materials Research 546-547 (July 2012): 1071–74. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1071.
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The study object is traffic flow on main road of urban traffic networks, the traffic condition is recognized by traffic flow theory and fuzzy logic method. The average space speed is a variable of the fact flow function, the road congestion degree is described by the ratio of fact flow and traffic capacity; the ratio of congestion time length and total time length is the congestion frequency. Considering congestion degree and congestion frequency, a fuzzy logic method is used to describe the traffic state by three grades: free, congestion and serious congestion. At last, the numerical example is given to analyze traffic state.
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Chee Yee, Ng, Joanne Mun-Yee Lim, and Anis Fariza. "VANET Experimental Results on Detecting Traffic Congestion Using SINR and Delay." Journal of Engineering & Technological Advances 1, no. 1 (2016): 105–15. http://dx.doi.org/10.35934/segi.v1i1.105.
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Vehicular ad hoc network (VANET) is a wireless network that is formed by vehicles that are connected to each other through ad hoc formation. Since nodes in VANET are highly mobile moving in unpredictable environments, therefore VANET should be made adaptable. In order to have an adaptable VANET, several parameters such as end to end delay and Signal to Interface Noise Ratio (SINR), are used to detect traffic congestion/ transmission signal degradation. In this paper, several experiments were conducted to test the suitability of SINR and end to end delay in detecting traffic congestion/ transmission signal degradation in VANET. The experiments were conducted in congested, moderate and traffic free conditions in an urban area of Klang, Malaysia. Results showed that under different traffic congestions and transmission environments, SINR and end to end delay showed different readings, which proved the suitability of SINR and end to end delay in detecting traffic congestion and transmission degradation in VANET.
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Amin, Reuel. "Traffix : Efficient Traffic Control using IoT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 27, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31872.
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This paper aims to alleviate traffic congestion brought on by antiquated, ineffective traffic management systems that are based on a predefined countdown. Long red light delays are the result of these traditional systems, which have a predefined countdown regardless of the actual traffic on a given road. Our system makes sure that time set for the traffic lights reflects the traffic density in real time, which ensures efficient use of time. In order to do this, we first compute the traffic density, which is ascertained by combining image processing methods along with the use of ultrasonic sensors. The Raspberry Pi then processes this data and then manages the time set for the traffic light. Furthermore, the use of SQL Alchemy ensures that this processed information is stored to the cloud, where it may be utilized in events of sensor failure or system malfunction. Index Terms—Image Processing, Raspberry Pi, Traffic Congestion, Ultrasonic sensors
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Mote, Prof Ranjeet S., Dharmesh A. Chhajed, Sambhav V. Dwidmuthe, and Komal N. More. "Smart Traffic Congestion Control." IJARCCE 6, no. 5 (May 30, 2017): 304–7. http://dx.doi.org/10.17148/ijarcce.2017.6556.
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Bo, Ku, Sun Ping, and Ke Yun. "Research on the Algorithm of Automatic Urban Traffic Congestion Identification." Advanced Materials Research 926-930 (May 2014): 3204–7. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3204.
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A method of traffic congestion identification based on traffic assignment model and SA-PC method is presented through analysis of municipal traffic congestion characteristic. Whether traffic congestion occurs or not is considered as a special classification problem. If the branch road of city is not affected by the signal light, the traffic situation is divided into tow parts: congestion and unimpeded. Using data associated with the traffic parameter for congestion and non congestion, an incremental SA-PC method is trained to detect whether traffic congestion occur or not. Experimental results based on microcosmic traffic simulation indicate that this method is not only feasible but also effective.
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Kumari, R. Sandhya, and Ashok D. Hanjagi. "An analysis of traffic congestion zones along Mysore road, Bangalore." Geo Eye 7, no. 2 (December 15, 2018): 4–6. http://dx.doi.org/10.53989/bu.ge.v7i2.2.
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The term traffic congestion means the stumbling block of traffic paths by lined up vehicles. Bangalore roads are well versed with the traffic problems especially Congestion. This research paper in particular discusses the traffic congestion zones identified along the Mysore road of Bangalore. The parameters deployed to identify these zones being the merging of multiple traffic streams, bottlenecks, etc. The findings of the research show that the identified congestion zones lack proper planning, and thus suitable suggestions are being listed out to ease the traffic congestion problems namely road extension, construction of skywalk or underpass near junctions wherein existence of traffic signals results in Congestion. Keywords: Traffic congestion; bottlenecks; skywalks; underpass
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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 (March 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 Internet of Things (IOT) technologies are used. Nowadays many junctions and roads are equipped with surveillance cameras connected to traffic management systems. Therefore, techniques like YOLO can be viable tools for automatic accident detection and traffic control.
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Zhao, Xueting, Liwei Hu, Xingzhong Wang, and Jiabao Wu. "Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems." Sustainability 14, no. 24 (December 16, 2022): 16907. http://dx.doi.org/10.3390/su142416907.
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In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation.
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Qin, Jiayu, Gang Mei, and Lei Xiao. "Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning." Sustainability 13, no. 1 (December 30, 2020): 266. http://dx.doi.org/10.3390/su13010266.
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Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.
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Zhang, Sen, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, and Jianjun Hu. "Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks." Sensors 19, no. 10 (May 14, 2019): 2229. http://dx.doi.org/10.3390/s19102229.
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Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.
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Fan, Dandan, Dawei Li, Fangzheng Cheng, and Guanghua Fu. "Effects of congestion charging and subsidy policy on vehicle flow and revenue with user heterogeneity." Mathematical Biosciences and Engineering 20, no. 7 (2023): 12820–42. http://dx.doi.org/10.3934/mbe.2023572.
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<abstract> <p>Traffic congestion is a major issue in urban traffic networks. Both congestion charging and subsidy policy can solve traffic congestion to some extent, but which one is better? Based on this, this paper constructs a typical transit network consisting of three travel tools in four common travel modes. Travelers' values of time affect their choice of transportation in the congestion network, thus a stochastic user equilibrium model is established by considering travelers' heterogenous values of time to evaluate the effects of different combinations of congestion charging and subsidy policies on vehicle flow and revenue. Numerical results indicate that the effectiveness of congestion charging and subsidy policy in alleviating traffic congestion depends on the object of charging or subsidizing. Congestion charging for private cars can reduce traffic flow and alleviate traffic congestion, but charging for ridesharing cars does not reduce traffic flow and may even cause traffic congestion. Subsidizing public buses does not reduce traffic flow, but it can ease congestion by coordinating traffic flow on both edges of the dual-modal transport. The combination of no subsidy for public buses and charging for both private cars and ridesharing cars can obtain the greatest revenue, but it does not alleviate traffic congestion. Although the combination of charging for private cars and subsidizing public buses does not bring the most benefits, it can reduce traffic flow, and its revenue is also considerable. This study can provide quantitative decision support for the government to ease traffic congestion and improve government revenue.</p> </abstract>
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Nguyen, Duc-Binh, Chyi-Ren Dow, and Shiow-Fen Hwang. "An Efficient Traffic Congestion Monitoring System on Internet of Vehicles." Wireless Communications and Mobile Computing 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/9136813.
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Existing intelligent transport systems (ITS) do not fully consider and resolve accuracy, instantaneity, and compatibility challenges while resolving traffic congestion in Internet of Vehicles (IoV) environments. This paper proposes a traffic congestion monitoring system, which includes data collection, segmented structure establishment, traffic-flow modelling, local segment traffic congestion prediction, and origin-destination traffic congestion service for drivers. Macroscopic model-based traffic-flow factors were formalized on the basis of the analysis results. Fuzzy rules-based local segment traffic congestion prediction was performed to determine the traffic congestion state. To enhance prediction efficiency, this paper presents a verification process for minimizing false predictions which is based on the Rankine-Hugoniot condition and an origin-destination traffic congestion service is also provided. To verify the feasibility of the proposed system, a prototype was implemented. The experimental results demonstrate that the proposed scheme can effectively monitor traffic congestion in terms of accuracy and system response time.
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RAJA, Dr V. SAI SHANMUGA, Dr G. GUNASEKARAN, and CHINCHU NAIR. "OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (March 13, 2024): 1–11. http://dx.doi.org/10.55041/ijsrem29177.
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A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving vehicles in traffic scene video streams captured by stationary cameras. The following is the detection and tracking methodology. The adaptive background subtraction technique is initially used to separate the moving vehicles from the traffic scene. Using videos to isolated picture blobs are recognized as individual vehicles once the background is subtracted. Following blob identification, vehicles in a certain area are counted and classified. A count of vehicles was observed with an accuracy of ideal camera calibration. To support the goal, data is gathered from video footage of vehicles traveling toward and away from the camera in order to count and use signal switching. The created system's results demonstrate that, with more enhancements, it applicable to count and categorize vehicles in real-time. After that, an optimization framework makes use of these predictions to dynamically modify signal timings in response to shifting traffic conditions. The optimization method seeks to increase overall traffic flow efficiency, decrease delays, and shorten travel times. To sum up, utilizing real-time traffic data to optimize signal control presents a viable approach to improving urban traffic management. This method makes use yolo techniques to facilitate the creation of flexible and effective traffic control, which in turn helps to create more sustainable and seamless urban transportation networks. KEYWORDS: Image classification, Video tracking, Information analysis, Vehicle detection, Signal Switching.
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Patel, Nilanchal, and Alok Bhushan Mukherjee. "Assessment of network traffic congestion through Traffic Congestability Value (TCV): a new index." Bulletin of Geography. Socio-economic Series 30, no. 30 (December 1, 2015): 123–34. http://dx.doi.org/10.1515/bog-2015-0039.
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Abstract Traffic congestion is a major and growing problem in urban areas across the globe. It reduces the effective spatial interaction between different locations. To mitigate traffic congestion, not only the actual status of different routes needs to be known but also it is imperative to determine network congestion in different spatial zones associated with distinct land use classes. In the present paper, a new formula is proposed to quantify traffic congestion in the different spatial zones of a study area characterized by distinct land use classes. The proposed formula is termed the Traffic Congestability Value (TCV). The formula considers three major influencing factors: congestion index value, pedestrian movement and road surface conditions; since these parameters are significantly related to land use in a region. The different traffic congestion parameters, i.e. travel time, average speed and the proportion of time stopped, were collected in real time. Lower values of TCV correspond to a higher degree of congestion in the respective spatial zones and vice-versa and the results were validated in the field. TCV differs from the previous approaches to quantifying traffic congestion since it focuses on the causes of network congestion while in previous works the focus was generally on link flow congestion.
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Gu, Yuan Li, Xiao Qian Yan, and Jing Yi Wu. "Spatial and Temporal Characteristics for Traffic Congestion Based on Microwave Data." Advanced Materials Research 838-841 (November 2013): 2107–12. http://dx.doi.org/10.4028/www.scientific.net/amr.838-841.2107.
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Based on empirical microwave data on the second Ring Road in Beijing, spatial and temporal characteristics of traffic congestion were analyzed. Three specific indexs for the congestion have been analyzed, i.e., congestion duration, time percentage of traffic congestion and key congestion sections. The hazard-based traffic congestion duration model was established through survival method. Temporal-spatial distribution of traffic congestion was studied using non-parametric method. The discrepancies of traffic congestion on different time and different links were considered. Results show that there has a big difference on congestion duration and congestion number between different links. Hazard rate will be less than 10% when congestion lasting more than 20 minutes. Three characteristics of the key congested points are confirmed. The judgment for key congestion sections show that key congestion segments have original affect to other sections.
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Kadkhodaei, Masoud, and Rouzbeh Shad. "Analysis and Evaluation of Traffic Congestion Control Methods in Touristic Metropolis Using Analytical Hierarchy Process (AHP)." Civil Engineering Journal 4, no. 3 (April 7, 2018): 602. http://dx.doi.org/10.28991/cej-0309119.
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One of the most important issues of urban transport management in metropolitan cities is the control of traffic congestion in the central parts of the city or other densely populated areas. Typical ways to control traffic congestion in metropolitan areas are to create a prohibited traffic area, alternate traffic plan (even and odd), and congestion pricing. In this paper, these traffic congestion control methods have been compared and evaluated. The methodology of this research is analytical hierarchy analysis (AHP). Based on the results, the most effective measures for assessing traffic congestion control methods in metropolitan cities were improving traffic conditions, social welfare, reducing environmental pollution and improving the safety of intra-urban travel. The best Traffic congestion control options were also priced for traffic congestion, roaming traffic (odd and odd), and the creation of traffic barriers. The results of analyzes and paired comparisons in analytic hierarchy analysis were also obtained using “Expert choice” software.
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Zhang, Wei, Guozhen Tan, Nan Ding, and Guangyuan Wang. "Traffic Congestion Evaluation and Signal Control Optimization Based on Wireless Sensor Networks: Model and Algorithms." Mathematical Problems in Engineering 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/573171.
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This paper presents the model and algorithms for traffic flow data monitoring and optimal traffic light control based on wireless sensor networks. Given the scenario that sensor nodes are sparsely deployed along the segments between signalized intersections, an analytical model is built using continuum traffic equation and develops the method to estimate traffic parameter with the scattered sensor data. Based on the traffic data and principle of traffic congestion formation, we introduce the congestion factor which can be used to evaluate the real-time traffic congestion status along the segment and to predict the subcritical state of traffic jams. The result is expected to support the timing phase optimization of traffic light control for the purpose of avoiding traffic congestion before its formation. We simulate the traffic monitoring based on theMobile Centurydataset and analyze the performance of traffic light control on VISSIM platform when congestion factor is introduced into the signal timing optimization model. The simulation result shows that this method can improve the spatial-temporal resolution of traffic data monitoring and evaluate traffic congestion status with high precision. It is helpful to remarkably alleviate urban traffic congestion and decrease the average traffic delays and maximum queue length.
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Musri S, M. Ali, Siti Fatimah, and Saiful Anwar Matondang. "Simulation Model to Reduce the Traffic Jams with a Stochastic Program." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 18 (January 7, 2022): 37–41. http://dx.doi.org/10.37394/232015.2022.18.5.
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Traffic congestion needs a simulation model to reduce its effects on traffics jams and pollution. The traffic cessation caused by the large number of vehicles exceeding the capacity of the road users. This study applied a stochastic program to the traffic congestion; it causes most of the working hours to be spent on roads that indirectly place a negative impact on economic growth. It also causes serious air pollution that will worsen the overall environmental condition. Data obtained show the factors causing traffic congestion in the city of Medan and with approach the stochastic program model used to solve this problem. Data indicated that there are four factors causing traffic congestion in Medan, which are non-growth of road, economic growth, population growth, and increase of motor vehicle.Population factor; the existence of good population growth caused by natural and migration growth. It concludes that the traffic jams are due to the socio-economic factors; namely the development of community business activities. Also socio-cultural factors; the existence of changes in the pattern of life and public order due to outside influences, communication, and information systems.
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Zhang, Wenjing. "Countermeasures for Urban Traffic Congestion in China from the Perspective of System Dynamics." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–15. http://dx.doi.org/10.1155/2022/3509902.
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In recent years, urban traffic congestion has seriously affected the healthy development of urbanization in China. And many measures to combat congestion have had little effect . The purpose of this paper is to find out the most reasonable and sustainable measures to control traffic congestion . Based on the theory of system dynamics, this study constructs a model of the formation mechanism of urban traffic congestion in China, and analyzes the thinking error of the traditional strategy of “building roads to eliminate traffic congestion” This study includes the current policy measures to control traffic congestion in the system dynamics model and analyzes the influence of each measure on the formation mechanism of urban traffic congestion. Then, it critiques the unsustainability of rigid policies, such as the vehicle number limit and the “similar road building to control traffic congestion” policies. This study reveals that of the five policies adopted by the government to alleviate traffic congestion, and come to the conclusion: the “sparse block collocation” policy is the most sustainable and fundamental congestion control measure. To achieve efficient traffic congestion control and support the healthy development of urbanization in China in the future, the government should increase the balance of infrastructure investment to improve the slow environment of public transport, adhere to public transport-oriented land development policies, raise the cost of motor vehicle travel, and promote urban traffic.
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ISMIRANTI, NI WAYAN NINING, I. PUTU EKA N. KENCANA, and I. KOMANG GDE SUKARSA. "ANALISIS PRIORITAS SOLUSI KEMACETAN LALU LINTAS DI KOTA DENPASAR DENGAN MENGGUNAKAN METODE ANALYTIC NETWORK PROCESS." E-Jurnal Matematika 5, no. 1 (January 30, 2016): 7. http://dx.doi.org/10.24843/mtk.2016.v05.i01.p114.
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The aim of this research is to find the alternative solutions that could be used to handle the traffic congestions in the Denpasar City and the priorities of each alternative. The main problem of this research is determining the appropriateness of alternatives and its criterias that could be used to set the priorities of the alternatives. Based on the interview with the transport experts of Denpasar City, there are three main factors that affect the traffic congestion i.e (1) the ratio of the volume of vehicles on the road capacity, (2) the existing traffic management, and the traffic regulation . The interviewee also suggest that there are six alternatives that can be used to handle traffic congestion. These alternatives are (1)improve the public transport system, (2) use technology to monitor and enforce the rules,(3) create a 3 in 1 rule, (4) create road pricing rule,(5) optimize the existing management in the road, and (6) create rule of road zoning. Based on the calculations by Analytic Network Process (ANP) method, improving the public transport system is the best alternative among others that is appropriate to handle traffic congestion in the Denpasar City.
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Wang, Li, Shimin Lin, Jingfeng Yang, Nanfeng Zhang, Ji Yang, Yong Li, Handong Zhou, Feng Yang, and Zhifu Li. "Dynamic Traffic Congestion Simulation and Dissipation Control Based on Traffic Flow Theory Model and Neural Network Data Calibration Algorithm." Complexity 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5067145.
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Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole.
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Kou, Wei, and Lin Cheng. "Research on the Urban Transportation Development in Our Country Based on the Traffic Congestion Pricing." Applied Mechanics and Materials 97-98 (September 2011): 1032–37. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.1032.
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With the development and realization of industrialization and urbanization in the world, urban traffic volume grows rapidly; many big cities face more and more serious traffic problem. As a mean of traffic demand management, traffic congestion pricing has important significance in theory and practice. Traffic congestion pricing can counteract external diseconomy caused by network congestion, and the price of congestion is tantamount to the difference between social marginal cost and private marginal cost. This paper analyzes the economic theory of congestion pricing. Combined the effect of traffic congestion pricing that implemented in the developed countries, it researches the influence of urban transportation development in our country in the future based on the implementing congestion pricing.
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Li, Linxin, Shuo Zhao, and Cong Chu. "Analysis of Congestion Problems at Green Dream Roundabout Intersection in Yubei District." Journal of Innovation and Development 5, no. 3 (December 20, 2023): 76–78. http://dx.doi.org/10.54097/h2n1l8wc.
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Traffic congestion can have a series of adverse effects: roads, highways, The traffic efficiency of highways and other transportation facilities is reduced, resulting in more serious traffic congestion; people's travel time becomes longer, a lot of time and energy are wasted, and daily work and life are affected; the risk of traffic accidents is increased, and the vehicles are dense when traffic congestion is dense, and traffic accidents are prone to occur; environmental pollution affects economic development; transportation costs are increased, and economic pressure is caused to enterprises and individuals, congestion has become a hot topic of discussion among the general public at present, especially in Chongqing, and solving traffic congestion is the urgent yearning of the people. Based on this background, this paper takes the traffic congestion at the intersection of Lvmeng Roundabout in Yubei District, Chongqing as the research object, and judges the traffic congestion level of the Lvmeng Roundabout intersection by referring to relevant domestic research articles and using intelligent traffic data to collect and analyze, and deeply analyzes the causes of traffic congestion at this intersection and gives a mitigation plan.
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Yang, Shuxia, Yu Ji, Di Zhang, and Jing Fu. "Equilibrium between Road Traffic Congestion and Low-Carbon Economy: A Case Study from Beijing, China." Sustainability 11, no. 1 (January 4, 2019): 219. http://dx.doi.org/10.3390/su11010219.
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China has allocated low-carbon targets into all regions and trades, and road traffic also has its own emission reduction targets. Congestion may increase carbon emissions from road traffic. It is worthwhile to study whether it is possible to achieve the goal of road traffic reduction by controlling congestion; that is, to achieve the equilibrium between traffic congestion and a low-carbon economy. The innovation of this paper is mainly reflected in the innovative topic selection, the introduction of a traffic index, and the establishment of the first traffic congestion and low-carbon economic equilibrium model. First, the relevant calculation method of the traffic index is introduced, and the traffic index is used to quantify the traffic congestion degree. Using the traffic index, GDP, and road passenger traffic volume, a nonlinear regression model of road traffic carbon emissions is constructed. Then, the calculation method of the carbon emission intensity of road traffic in the region is proposed. The equilibrium model of traffic congestion and a low-carbon economy is constructed to look for the degree of road traffic congestion that may occur under the permitted carbon emission intensity. Taking Beijing, where electric vehicles account for less than 3% of the total vehicles, as an example, it is difficult to achieve the equilibrium target between road traffic congestion and a low-carbon economy by alleviating traffic congestion in 2020. If the target of traffic carbon emission reduction in 2020 is adjusted from 40%–45% to 19.7% based on 2005, the equilibrium will be achieved. A negative correlation between road traffic carbon emissions and the reciprocal of the traffic index (1/TI) is found after eliminating the effects of GDP and PTV (road passenger traffic volume). As the traffic index decreases by units, the carbon emission reduction accelerates. The results show that carbon reduction targets cannot be simply allocated to various industries. The results of the research on the degree of the impact of traffic congestion on carbon emissions can be used as a basis for carbon reduction decisions of the traffic sector. The research method of this paper can provide a reference for the study of the equilibrium of traffic congestion and a low-carbon economy in other regions.
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Mahapatra, Sarita, Krishna Chandra Rath, and Srikanta Pattnaik. "Regression based neural network model for prediction of road traffic congestion : A case study of Bhubaneswar." Journal of Statistics & Management Systems 26, no. 1 (2023): 107–16. http://dx.doi.org/10.47974/jsms-951.
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The prediction of road traffic congestion is the most important and essential aspect to reduce the suffering of population of urban cities which is primarily carried by roads. The lack of a traffic congestion data unavailability and evaluation standard makes the effect of traffic congestion prediction more difficult and worsen. Traffic congestion occurs due to increase in number of vehicles on roads which reduces speed of vehicles, increases delay time, and increasing vehicular queuing in traffic. Due to traffic congestion, not only delaying time increases but also its threat to slower down the economic growth rate of our country and also have high impact on our personal growth, living condition with high level of pollution and undesirable feature of overloaded streets. Traffic congestion predicting modeling plays very important role so we need a innovative approach to predicting the congestion on roads. In this paper, we aim to provide a model which studies the real time environment characteristics of the road, and analyzed the data, congestion location identification, directional movement of all those locations surveyed and forecasting traffic location where traffic congestion may occur in near future.
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Memon, Tarique Rafique, Nisar Ahmed, Abdul Karim, Ahmed Ali, and Waris Gul Memon. "Traffic Congestion Alert System using GSM." Journal of Applied Engineering & Technology (JAET) 1, no. 1 (December 31, 2017): 22–26. http://dx.doi.org/10.55447/jaet.01.01.42.
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The increase in the number of vehicles has led to Traffic congestion. During the buy hours of the day, the traffic is at its peak and there are various problems related to traffic congestion. For the emergency conditions such ambulance, fire engines to pass through, the congestion possesses a hindrance. To recover for the lost time spent in congestion people tend to hurry and disperse the congestion, causing accidents. The smoke released from the vehicles and the honking of the horns causes air and noise pollution. To overcome these problems, a system has to be designed which can alert for congestion. Traffic congestion alert system using GSM does the needful and thus helps in reducing the traffic congestion.