Journal articles: 'TRAFFIC CONGESTION DETECTION'

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Wiseman, Yair. "Computerized Traffic Congestion Detection System." International Journal of Transportation and Logistics Management 1, no. 1 (December 30, 2017): 1–8. http://dx.doi.org/10.21742/ijtlm.2017.1.1.01.

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Idura Ramli, Nurshahrily, and Mohd Izani Mohamed Rawi. "An overview of traffic congestion detection and classification techniques in VANET." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (October 1, 2020): 437. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp437-444.

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<span>Vehicular traffic congestion has been and still is a major problem for many countries and knowledge about the traffic condition is important in order to schedule, plan and avoid traffic congestion. With recent development in technology, various efforts and methods are proposed in mitigating traffic congestion. Vehicular Ad-hoc NETwork (VANET) is very much in the hype in addressing this issue due to its capabilities and adaptation to scalability, highly dynamic topology as well as cooperative communication. A popular focus is in detecting and classisying traffic congestion which presents various techniques and methodologies. This paper presents an overview of traffic congestion detection and classification methods of various related techniques in VANET, organized from the research perspective. Qualitative analysis is presented to classify these strategies in its system architecture, detection and classification methods, as well as its simulated mobility environment and simulation tools used. The analysis is useful in understanding all the techniques and methods applied in resolving this issue in the research domain. </span>

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Xiang, Yingxiao, Wenjia Niu, Endong Tong, Yike Li, Bowei Jia, Yalun Wu, Jiqiang Liu, Liang Chang, and Gang Li. "Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods." Security and Communication Networks 2021 (October 31, 2021): 1–17. http://dx.doi.org/10.1155/2021/1632825.

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The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.

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Wang, Chao. "An Effective Congestion Control Algorithm based on Traffic Assignment and Reassignment in Wireless Sensor Network." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 8 (December 3, 2020): 1166–74. http://dx.doi.org/10.2174/2352096513999200628095848.

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Background: It is important to improve the quality of service by using congestion detection technology to find the potential congestion as early as possible in wireless sensor network. Methods: So an improved congestion control scheme based on traffic assignment and reassignment algorithm is proposed for congestion avoidance, detection and mitigation. The congestion area of the network is detected by predicting and setting threshold. When the congestion occurs, sensor nodes can be recovery quickly from congestion by adopting reasonable method of traffic reassignment. And the method can ensure the data in the congestion areas can be transferred to noncongestion areas as soon as possible. Results: The simulation results indicate that the proposed scheme can reduce the number of loss packets, improve the throughput, stabilize the average transmission rate of source node and reduce the end-to-end delay. Conclusion: : So the proposed scheme can enhance the overall performance of the network. Keywords: wireless sensor network; congestion control; congestion detection; congestion mitigation; traffic assignment; traffic reassignment.

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Kayarga, Tanuja, and H. M. Navyashree. "A Novel Framework to Control and Optimize the Traffic Congestion Issue in VANET." International Journal of Engineering & Technology 7, no. 2.31 (August 24, 2018): 245. http://dx.doi.org/10.14419/ijet.v7i3.31.18234.

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

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El-Sersy, Heba, and Ayman El-Sayed. "Survey of Traffic Congestion Detection using VANET." Communications on Applied Electronics 1, no. 4 (March 26, 2015): 14–20. http://dx.doi.org/10.5120/cae-1520.

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Cherkaoui, Badreddine, Abderrahim Beni-Hssane, Mohamed El Fissaoui, and Mohammed Erritali. "Road traffic congestion detection in VANET networks." Procedia Computer Science 151 (2019): 1158–63. http://dx.doi.org/10.1016/j.procs.2019.04.165.

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Kalinic, Maja, and Jukka M. Krisp. "Fuzzy inference approach in traffic congestion detection." Annals of GIS 25, no. 4 (October 2, 2019): 329–36. http://dx.doi.org/10.1080/19475683.2019.1675760.

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Bhanja, Urmila, Anita Mohanty, and Bhagyashree Das. "Embedded based real time traffic congestion detection." International Journal of Vehicle Information and Communication Systems 3, no. 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.094976.

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Mohanty, Anita, Bhagyashree Das, and Urmila Bhanja. "Embedded based real time traffic congestion detection." International Journal of Vehicle Information and Communication Systems 3, no. 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.10016393.

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Es Swidi, A., S. Ardchir, A. Daif, and M. Azouazi. "Road users detection for traffic congestion classification." Mathematical Modeling and Computing 10, no. 2 (2023): 518–23. http://dx.doi.org/10.23939/mmc2023.02.518.

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One of the important problems that urban residents suffer from is Traffic Congestion. It makes their life more stressful, it impacts several sides including the economy: by wasting time, fuel and productivity. Moreover, the psychological and physical health. That makes road authorities required to find solutions for reducing traffic congestion and guaranteeing security and safety on roads. To this end, detecting road users in real-time allows for providing features and information about specific road points. These last are useful for road managers and also for road users about congested points. The goal is to build a model to detect road users including vehicles and pedestrians using artificial intelligence especially machine learning and computer vision technologies. This paper provides an approach to detecting road users using as input a dataset of 22983 images, each image contains more than one of the target objects, generally about 81000 target objects, distributed on persons (pedestrians), cars, trucks/buses (vehicles), and also motorcycles/bicycles. The dataset used in this study is known as Common Objects in Context (MS COCO) published by Microsoft. Furthermore, six different models were built based on the approaches RCNN, Fast RCNN, Faster RCNN, Mask RCNN, and the 5th and the 7th versions of YOLO. In addition, a comparison of these models using evaluation metrics was provided. As a result, the chosen model is able to detect road users with more than 55% in terms of mean average precision.

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Yang, Xinghai, Fengjiao Wang, Zhiquan Bai, Feifei Xun, Yulin Zhang, and Xiuyang Zhao. "Deep Learning-Based Congestion Detection at Urban Intersections." Sensors 21, no. 6 (March 15, 2021): 2052. http://dx.doi.org/10.3390/s21062052.

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In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

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Khan, Zahid, Anis Koubaa, and Haleem Farman. "Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning." Applied Sciences 10, no. 13 (June 30, 2020): 4541. http://dx.doi.org/10.3390/app10134541.

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Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy due to its complex structure. In the same context, this paper proposes a smart route Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA) scheme for a particular area of interest (AOI), i.e., road intersection point. The proposed scheme has two broad parts: (1) IoV-based congestion detection (IoV-based CD); and (2) IoV-based congestion avoidance (IoV-based CA). In the given area of interest, the congestion detection phase sets a parametric approach to calculate the capacity of each entry point for real-time traffic congestion detection. On each road segment, the installed roadside unit (RSU) assesses the traffic status concerning two factors: (a) occupancy rate and (b) occupancy time. If the values of these factors (either a or b) exceed the threshold limits, then congestion will be detected in real time. Next, IoV-based congestion avoidance triggers rerouting using modified Evolving Graph (EG)-Dijkstra, if the number of arriving vehicles or the occupancy time of an individual vehicle exceeds the thresholds. Moreover, the rerouting scheme in IoV-based congestion avoidance also considers the capacity of the alternate routes to avoid the possibility of moving congestion from one place to another. From the experimental results, we determine that proposed IoV-based congestion detection and avoidance significantly improves (i.e., 80%) the performance metrics (i.e., path cost, travel time, travelling speed) in low segment size scenarios than the previous microscopic congestion detection protocol (MCDP). Although in the case of simulation time, the performance increase depends on traffic congestion status (low, medium, high, massive), the performance increase varies from 0 to 100%.

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Zhang, Xue Li. "Path Reconstruction of Intelligent Traffic Based on Positive Feedback System." Applied Mechanics and Materials 513-517 (February 2014): 3160–64. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3160.

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Traffic congestion are prevalent in worldwide cities. The imbalance between demand and supply of urban traffic is the root cause of this problem. So taking effective measures to regulate traffic demand, and guiding the traffic problems of the supply and demand balance is the best way to solve traffic congestion. This paper improves the TDM measure, and combines with intelligent information platform for the design of a new urban transport demand management adaptability of dynamic traffic data analysis platform. The platform supported by the technology of wireless sensor communications, intelligent terminals, the Internet and cloud computing is facing with the dynamic needs of traffic flow and traffic congestion state to carry out the operations of spatiotemporal data mining, clustering, and track detection, and to apply it into the traffic hot spots, abnormal driving track, traffic congestion trends and traffic flow detection and analysis, which has a good reference value for the improvement of management and service level of traffic intelligent systems.

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Zaitouny, Ayham, Athanasios D. Fragkou, Thomas Stemler, David M. Walker, Yuchao Sun, Theodoros Karakasidis, Eftihia Nathanail, and Michael Small. "Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan." Sensors 22, no. 8 (April 11, 2022): 2933. http://dx.doi.org/10.3390/s22082933.

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Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

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Anbaroglu, B., B. Heydecker, and T. Cheng. "HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 159–64. http://dx.doi.org/10.5194/isprs-archives-xli-b2-159-2016.

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Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

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Anbaroglu, B., B. Heydecker, and T. Cheng. "HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 159–64. http://dx.doi.org/10.5194/isprsarchives-xli-b2-159-2016.

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Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

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Ge, Ling Yan, and Bi Feng Zhu. "Analysis and Optimization of Hangzhou East Area Traffic Based on the Congestion Index Detection Platform." Advanced Materials Research 1030-1032 (September 2014): 2182–86. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2182.

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With the rapid development of urbanization in China and the motorization’s fast pace of high speed as well as the national automobile industry process, many cities in our country have been facing a huge problem - traffic congestion in recent years. And the essence of the problem is the imbalance between road traffic supply and traffic demand in the process of urban development. Aimed at the problem of traffic congestion, this paper based on Hangzhou city’s traffic congestion index of monitoring data from testing platform and statistical data from field survey , studied the Hangzhou east area of road traffic running situation, analyzed the causes of the east area of Hangzhou road congestion, and thus to adjust and optimize the road traffic system of the area, put forward reasonable system solutions and proposals to improve the management level

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Balasubramanian, Saravana Balaji, Prasanalakshmi Balaji, Asmaa Munshi, Wafa Almukadi, T. N. Prabhu, Venkatachalam K, and Mohamed Abouhawwash. "Machine learning based IoT system for secure traffic management and accident detection in smart cities." PeerJ Computer Science 9 (March 8, 2023): e1259. http://dx.doi.org/10.7717/peerj-cs.1259.

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In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.

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Mohanty, Anita, Sudipta Mahapatra, and Urmila Bhanja. "Traffic congestion detection in a city using clustering techniques in VANETs." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (March 1, 2019): 884. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp884-891.

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<p>Road traffic congestion, a serious illness in developing regions, is one of the biggest problems in our day-to-day life, resulting in delays, wastage of fuel and money. In this paper, a new model is developed using Simulation of Urban Mobility (SUMO) simulator for simulating a realistic traffic scenario for a large city like Bhubaneswar where, traffic congestion is a critical issue. In a city, traffic congestion is characterised by many parameters such as rapid growth of population, number of four wheelers, inadequate and poor road infrastructures and shortage of physical plan to govern the developments, which are focused on enhancing the volume of the roads by raising the number of lanes, over-passes, underpasses and over-bridges at many junctions. However, for the success of these master plans to fully overcome the congestion issues, it is necessary to transmit the congestion information to vehicles coming towards a congestion area by using a Vehicular Ad-hoc Network. This paper analyzes clustering techniques in Vehicular Ad-hoc Networks to detect congestion in roads with the minimal infrastructural support. The raw data from vehicles are classified using cluster analysis. Out of a number of algorithms that are used to solve the congestion detection problem, three important algorithms such as Centroid based -means, object based FCM and FKM algorithms are compared in this work on the basis of data points and number of clusters. The results of the algorithms are close to each other, but fuzzy techniques are preferable as the traffic situations are dynamic in nature.</p>

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Wang, Wan-Xiang, Rui-Jun Guo, and Jing Yu. "Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example." Advances in Mechanical Engineering 10, no. 6 (June 2018): 168781401878148. http://dx.doi.org/10.1177/1687814018781482.

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Traffic congestion index reflects the state of traffic flow. The detection and analysis on traffic congestion index can be used to estimate the operation status of roads, to plan and organize road traffic for traffic managers, and to make the reasonable decisions of travelers to travel. The traffic conditions of several evaluation indexes were analyzed. Based on the theory of fuzzy mathematics, some membership functions of the evaluating indexes were designed. Three calculation methods of traffic congestion index were proposed. Their calculation results were compared mutually. The conclusion revealed that using saturation calculated by the corresponding service level of traffic congestion index not well reflect the traffic situation, what’s more, travel speed is used to calculate the congestion index of the first method. Using comprehensive parameters can calculate the congestion index of the third method. Both them are roughly similar and in line with the actual traffic phenomenon.

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Wang, Chishe, Yuting Chen, Jie Wang, and Jinjin Qian. "An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios." Applied Sciences 13, no. 12 (June 15, 2023): 7174. http://dx.doi.org/10.3390/app13127174.

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Traffic congestion detection based on vehicle detection and tracking algorithms is one of the key technologies for intelligent transportation systems. However, in expressway surveillance scenarios, small vehicle size and vehicle occlusion present severe challenges for this method, including low vehicle detection accuracy and low traffic congestion detection accuracy. To address these challenges, this paper proposes an improved version of the CrowdDet algorithm by introducing the Involution operator and bi-directional feature pyramid network (BiFPN) module, which is called IBCDet. The proposed IBCDet module can achieve higher vehicle detection accuracy in expressway surveillance scenarios by enabling long-distance information interaction and multi-scale feature fusion. Additionally, a vehicle-tracking algorithm based on IBCDet is designed to calculate the running speed of vehicles, and it uses the average running speed to achieve traffic congestion detection according to the Chinese expressway level of serviceability (LoS) criteria. Adequate experiments are conducted on both the self-built Nanjing Raoyue expressway monitoring video dataset (NJRY) and the public dataset UA-DETRAC. The experimental results demonstrate that the proposed IBCDet outperforms the commonly used object detection algorithms in both vehicle detection accuracy and traffic congestion detection accuracy.

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Pillai, Arjun, Kajal Chourasia, and Bhavya Agarwal. "Neural Network Based Traffic Monitoring using UAVs." International Journal of Engineering and Advanced Technology 8, no. 4s2 (August 1, 2020): 45–50. http://dx.doi.org/10.35940/ijeat.d1003.0484s219.

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In large and growing metropolitan areas, the rise in traffic congestion is becoming an inescapable problem. It is estimated that the traffic congestion in metro cities costs the nation approximately 1.5 lakh crore rupees every year. With the increase in congestion, accident rate increases proportionally. The reckless driving and increased speed are the root cause of road accidents. We propose a speed detection algorithm to detect and monitor the speed of vehicles crossing a certain threshold speed limit. On national highways, the long queues at toll booths lead to loss of time and money. We propose image processing and convolutional neural network based algorithm to address the problem of traffic congestion, ease the flow of traffic, anomalies detection and ultimately reduce pollution and fuel consumption.

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Xu, Ling, Qun Ba, and Shan Hu. "Reserch on Traffic Congestion Detection Using Realtime Video." Applied Mechanics and Materials 241-244 (December 2012): 2100–2106. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2100.

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To detection the realtime information of the traffic congestion on the road, a method based on realtime video analysis was present. The method, firstly figure out the density of the vehicles on the lane, and then calculates optical flow velocity vetors of corner points on vehicles, finnaly, judges the current condition of the traffic flow by fuzzy logic based on the conditions of denisty and velocity. The proposed method is capable to accurately and timely detect the status of traffic congestion.

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Sheikh, Muhammad Sameer, Jun Liang, and Wensong Wang. "An Improved Automatic Traffic Incident Detection Technique Using a Vehicle to Infrastructure Communication." Journal of Advanced Transportation 2020 (January 13, 2020): 1–14. http://dx.doi.org/10.1155/2020/9139074.

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Traffic incident detection is one of the major research areas of intelligent transportation systems (ITSs). In recent years, many mega-cities suffer from heavy traffic flow and congestion. Therefore, monitoring traffic scenarios is a challenging issue due to the nature and the characteristics of a traffic incident. Reliable detection of traffic incidents and congestions provide useful information for enhancing traffic safety and indicate the characteristics of traffic incidents, traffic violation, driving pattern, etc. This paper investigates the estimation of traffic incident from a hybrid observer (HO) method, and detects a traffic incident by using an improved automatic incident detection (AID) technique based on the lane-changing speed mechanism in the highway traffic environment. First, we developed the connection between vehicles and roadside units (RSUs) by using a beacon mechanism. Then, they will exchange information once the vehicles get access to a wireless medium. Second, we utilized the probabilistic approach to collect the traffic information data, by using a vehicle to infrastructure (V2I) communication. Third, we estimated the traffic incident by using an HO method which can provide an accurate estimation of an event occurring. Finally, in order to detect traffic incident accurately, we applied the probabilistic data collected through V2I communication based on lane-changing speed mechanism. The experimental results and analysis obtained from simulations show that the proposed method outperforms other methods in terms of obtaining a better estimation of traffic incident which agrees well with the theoretical incident, around 30% faster detection of traffic incidents and 25% faster dissipation of traffic congestion. With regard to duration of an incident, the proposed system obtained a better Kaplan–Meier (KM) curve, influenced by the shortest duration of time to clear the traffic incident, in comparison with the other methods.

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Peng, Ming Long, Xin Rong Liang, Chao Jun Dong, and Yan Yan Liu. "Freeway Traffic Congestion Identification Based on Fuzzy Logic Inference." Applied Mechanics and Materials 397-400 (September 2013): 2227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2227.

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Traffic congestion detection is the basis of dynamic traffic control and real time guidance. This study proposes a fuzzy logic based traffic congestion identification method. The components of a fuzzy logic inference are firstly formulated. According to such information as the speed and occupancy of freeway traffic flow, and the weather conditions on the freeway, a congestion identification method based on fuzzy logic inference is then designed. Gauss curves are assumed for the membership functions of the input and output variables, and 45 fuzzy rules are also established. Finally, the congestion identification method is simulated. Simulation results verify the effectiveness of the above method. Fuzzy logic inference is suitable for estimating the traffic congestion index.

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V, Mahalakshmi, and Dr Manjunath S. "Automatic Detection of Pedestrian Crossing Platform using Congestion Monitoring." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (August 31, 2023): 275–79. http://dx.doi.org/10.22214/ijraset.2023.55178.

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Abstract: One of the primary problems faced globally is the amount of traffic on the roads and the number of pedestrian accidents. The risk when crossing or walking on roads in urban and rural regions with significant traffic is a major factor in these incidents. A novel idea is put forth to avert such situations. Using congestion monitoring, automatic detection of pedestrian crossing platforms. An IR (Infrared) sensor module is used to continuously monitor pedestrian and traffic congestion. When there are more pedestrians on the road, the traffic light for automobiles turns red, allowing pedestrians to cross at the zebra crossing. In the proposed system, we are counting the packed pedestrians at the signal point and performing congestion monitoring of the pedestrian and vehicle count, which will be sensed by IR Infrared Sensors. This technique guarantees pedestrian crossing safety and prevents drivers from flouting the law, which could cause accidents.

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Koukounaris, Athanasios I., Konstantina P. Marousi, and P. E. Yorgos J. Stephanedes. "Congestion detection and diversion in coastal urban traffic." Transportation Research Procedia 41 (2019): 255–59. http://dx.doi.org/10.1016/j.trpro.2019.09.045.

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Yang, Yuan Feng, Xue Feng Xian, Li Li Liao, and Min Ya Zhao. "A Feature Extraction Approach of Traffic Congestion from Video." Advanced Materials Research 490-495 (March 2012): 1058–62. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1058.

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To avoid the difficulty of collecting accurate traffic flow data, this paper proposes a novel approach for congestion features extraction from traffic video. The approach firstly segments the traffic video into shots and the shot motion content feature is extracted. Then, we extract the key frames applying an improved global k-means clustering algorithm. The last congestion feature of the global optical flow energy is computed based on the key frames. The numerical experiments on traffic surveillance video show the validity and high accuracy for traffic congestion detection using the propose method in this paper

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Bretherton, David, Keith Wood, and Neil Raha. "Traffic Monitoring and Congestion Management in the SCOOT Urban Traffic Control System." Transportation Research Record: Journal of the Transportation Research Board 1634, no. 1 (January 1998): 118–22. http://dx.doi.org/10.3141/1634-15.

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The SCOOT Urban Traffic Control system is now operating in over 170 cities worldwide, including 7 systems in North America. Since the first system was installed, there has been a continuous program of research and development to provide new facilities to meet the requirement of the traffic manager. The latest version of SCOOT (Version 3.1) incorporates a traffic information database, ASTRID, and an incident-detection system, INGRID, and provides a number of facilities for congestion control. The traffic monitoring facilities of SCOOT, including a new facility to estimate emissions from vehicles, and the current program of work to enhance the incident-detection system and to provide additional facilities to manage incidents and congestion are reported in this paper. The work is being carried out as part of the European Union, DGXIII 4th Framework project, COSMOS, with additional funding from the UK Department of Transport. The enhanced system is to be installed in the Kingston Borough of London, where it will be tested in combination with congestion warning information provided by variable message signs.

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Dongare, Tejas, Dhiraj Huljute, Pranit Jadhav, Anuj Lad, and Prof Sheetal Marawar. "A Review on Traffic Management and Road Analysis of Porwal Road." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (January 31, 2023): 881–84. http://dx.doi.org/10.22214/ijraset.2023.48508.

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Abstract: Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement, and bad traffic management has to lead to traffic congestion. One of the major problems with Indian cities is that the existing infrastructure cannot be expanded more, and thus the only option available is better management of the traffic. Traffic congestion has a negative impact on the economy, the environment, and the overall quality of life. Hence it is high time to effectively manage the traffic congestion problem. In cities, where the number of vehicles continuously increases faster than the available traffic infrastructure to support them, congestion is a difficult issue to deal with and it becomes even worse in case of car accidents. There are various methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor network, etc. Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement, and bad traffic management have led to traffic congestion. This problem affects many aspects of modern society, including economic development, traffic accidents, increase in greenhouse emissions, time spent, and health damages.

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Nurhadryani, Yani, Wulandari Wulandari, and Muhammad Naufal Farras Mastika. "Vehicle Detection Monitoring System using Internet of Things." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 5 (October 31, 2022): 749–60. http://dx.doi.org/10.29207/resti.v6i5.4082.

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The overcapacity of vehicle numbers is one of the significant causes of the traffic congestion problem on Indonesia roadway. The government applies a One-way system (SSA) as one proposed solution to unravel the congestion. However, several congestion points are still found during the SSA implementation. Thus, this research offers an alternative method to detect congestion using IoT technology. The system automatically enumerates the number, classifies the type, and computes the speed averages of vehicles to identify the severity of congestion based on the Indonesian Highway Capacity Manual (IHCM) published by the Ministry of Public Works 2014. We utilize ultrasonic sensors to detect the vehicles and send the data to the server in real-time. The research successfully develops an IoT system for traffic congestion detection. Communication between nodes and API can be done well. Data exchange involving encryption and decryption with AES-256 is successfully done. Website application developed in this research is successfully show the severity level of the congestion and their vehicle numbers. The average accuracy of the system is 78,97%. The system detected more vehicles than actual numbers due to the misreading value of the sensors.

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Shariat Mohaymany, Afshin, and Matin Shahri. "Evaluating the impact of new congestion charging scheme using smartphone-based data: a spatial change detection study." Canadian Journal of Civil Engineering 47, no. 9 (September 2020): 1105–15. http://dx.doi.org/10.1139/cjce-2019-0106.

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Traffic congestion in urban areas is a challenging issue in transportation planning. Policy options have been proposed to evaluate the impacts of interventional action through change detection or before–after studies. In this research, low-cost traffic image data collected by smartphone-based application have been employed and the impact of new congestion charging scheme (CCS) upon congestion within congestion charging zone (CCZ) as well as the entire network in Tehran, the capital of Iran has been investigated. Applying statistical tests indicated the significance of change in congestion within CCZ by applying the new CCS. Differential Moran’s I as spatial autocorrelation index specified the spatial patterns of congestion between the critical time of changing the scheme on weekdays (17:00–19:00) and weekend (6:00–13:00) after implementing the new CCS. The approach in this paper can be used with a low-cost appropriate instrument to monitor the probable change in traffic congestion by introducing any new scheme or sudden change.

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Gao, Zhi Min, Fa Sheng Liu, and Meng Chen. "Urban Transportation Crowded Recognition Technology and Application." Applied Mechanics and Materials 97-98 (September 2011): 907–10. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.907.

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The urban road traffic congestion has not only brought many inconvenient for people's routine work and life, but also will restrict the growth of the economical, to accelerate the urban environment worsening and serious influence the city sustainable development. This paper studies based on the dynamic detection of urban road traffic congestion condition recognition technology can fast and accurate discover in the road network which already had the traffic congestion or soon occurs, then estimated the crowded proliferation scope and duration, which are advantageous to carry on the transportation induction and the traffic control promptly. And according to the different target client to the different emphasis point to the distinguish algorithm, has designed the urban road traffic congestion recognition grading warning system.

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Agrawal, K., M. K. Nigam, S. Bhattacharya, and G. Sumathi. "Ambulance detection using image processing and neural networks." Journal of Physics: Conference Series 2115, no. 1 (November 1, 2021): 012036. http://dx.doi.org/10.1088/1742-6596/2115/1/012036.

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Abstract Ambulance Detection using Image Processing and Neural Network is a vehicle detection and tracking system, which recognizes the vehicle (i.e., Ambulance in this case) amidst the traffic congestion. Due to the fact from past few years, the range of vehicles usage of the road is growing each day that results in traffic congestion, for better management of this traffic this system is useful. Traffic Congestion, as mentioned above, can be observed at an ever-growing pace in countries like India and Thailand, where the roads’ width and length make it impossible to make a separate lane for the emergency vehicle (like that of ambulance); Hence making it hard for the vehicle to pass through the traffic at the earliest possible time. The Ambulance tracking system is activated at the mapped junctions and that program detects the ambulance coming close to it and turns the traffic light to Green for the next 15 seconds. Geocoding is the practice of transforming addresses (like a physical address) to location information (like longitude and latitude) that can be used to locate a label on a map or to mark a grid. They plan to provide ambulances with this software to make it easy to transform addresses into a programmable format for review and retrieval. This data is converted to a system that shows all the crossings it must pass to meet the endpoint.

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Aliari, Sanaz, and Kaveh F. Sadabadi. "Automatic Detection of Major Freeway Congestion Events using Wireless Traffic Sensor Data: Machine Learning Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (May 25, 2019): 436–42. http://dx.doi.org/10.1177/0361198119843859.

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Monitoring the dynamics of traffic in major corridors can provide invaluable insight for traffic planning purposes. An important requirement for this monitoring is the availability of methods to automatically detect major traffic events and to annotate the abundance of travel data. This paper introduces a machine learning-based approach for reliable detection and characterization of highway traffic congestion events from hundreds of hours of traffic speed data. Indeed, the proposed approach is a generic approach for detection of changes in any given time series, which is the wireless traffic sensor data in the present study. The speed data is initially time-windowed by a 10 h-long sliding window and fed into three neural networks that are used to detect the existence and duration of congestion events (slowdowns) in each window. The sliding window captures each slowdown event multiple times and results in increased confidence in congestion detection. The training and parameter tuning are performed on 17,483 h of data that include 168 slowdown events. These data are collected and labeled as part of the ongoing probe data validation studies at the Center for Advanced Transportation Technologies at the University of Maryland. The neural networks are carefully trained to reduce the chances of over-fitting to the training data. The experimental results show that this approach is able to successfully detect most of the congestion events, while significantly outperforming a heuristic rule-based approach. Moreover, the proposed approach is shown to be more accurate in estimation of the start time and end time of the congestion events.

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Shang, Qiang, Yang Yu, and Tian Xie. "A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering." Sustainability 14, no. 17 (September 5, 2022): 11068. http://dx.doi.org/10.3390/su141711068.

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As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for traffic state classification. In this paper, we propose a hybrid new traffic state classification method based on unsupervised clustering. Firstly, the k-medoids clustering algorithm is used to cluster the daily traffic speed data from multiple detection points in the selected area, and then the cluster-center detection points of the cluster with congestion are selected for further analysis. Then, the self-tuning spectral clustering algorithm is used to cluster the speed, flow, and occupancy data of the target detection point to obtain the traffic state classification results. Finally, several state-of-the-art methods are introduced for comparison, and the results show that performance of the proposed method are superior to comparable methods.

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Stojanović, Natalija, and Dragan Stojanović. "BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL." Facta Universitatis, Series: Automatic Control and Robotics 19, no. 2 (December 8, 2020): 087. http://dx.doi.org/10.22190/fuacr2002087s.

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With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform.

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Le, Thi Thuy Duong, Dang Hai Hoang, and Thieu Nga Pham. "Avoiding Congestion for Coap Burst Traffic." EAI Endorsed Transactions on Internet of Things 9, no. 1 (March 29, 2023): e2. http://dx.doi.org/10.4108/eetiot.v9i1.2655.

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Congestion is an important issue in Internet of Things (IoT) networks with constrained devices and a growing number of applications. This paper investigated the problem of congestion control for burst traffic in such networks. We highlight the shortcomings of the current constrained application protocol (CoAP) in its inability to support burst traffic and rate control. Subsequently, we propose an analytical model for CoAP burst traffic and a new rate-control algorithm for CoAP to avoid congestion. A CoAP sender increases or decreases the transmission rate depending on the congestion detection. Using simulations, we compared the performance of the proposed algorithm with the current CoAP in various traffic scenarios. Experimental results show that the proposed algorithm is efficient for burst traffic and provides better performance in terms of delay, throughput, retransmission, packet duplication, and packet loss compared to CoAP.

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Jiang, Tian, Jing An, Hao Zhi Zhang, and Zhen Guo Qian. "Traffic Information Collection System for Congestion Identification and Relief." Applied Mechanics and Materials 178-181 (May 2012): 2680–85. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.2680.

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While research focuses on using available traffic data sources to identify and relieve traffic congestion, less work is devoted answering the question of how and where to collect traffic data, so that traffic control systems can perform in an optimal, and cost-efficient manner. In this paper, We present a framework to assess traffic detection systems, by introducing the level of detection as value to allow for an objective comparison of multiple detector placement scenarios. The framework allows the usage of the framework for network operations, as well as planning purposes. By translating traffic operation goals into data demand functions, and detector capabilities, combined with their location, into data supply functions, it is possible to optimize detector locations with well know tools from operations research. The latter one is important, since it allows for including additional boundary conditions, such as costs.

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Chughtai, Omer, Nasreen Badruddin, Maaz Rehan, and Abid Khan. "Congestion Detection and Alleviation in Multihop Wireless Sensor Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/9243019.

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Multiple traffic flows in a dense environment of a mono-sink wireless sensor network (WSN) experience congestion that leads to excessive energy consumption and severe packet loss. To address this problem, a Congestion Detection and Alleviation (CDA) mechanism has been proposed. CDA exploits the features and the characteristics of the sensor nodes and the wireless links between them to detect and alleviate node- and link-level congestion. Node-level congestion is detected by examining the buffer utilisation and the interval between the consecutive data packets. However, link-level congestion is detected through a novel procedure by determining link utilisation using back-off stage of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). CDA alleviates congestion reactively by either rerouting the data traffic to a new less congested, more energy-efficient route or bypassing the affected node/link through ripple-based search. The simulation analysis performed in ns-2.35 evaluates CDA with Congestion Avoidance through Fairness (CAF) and with No Congestion Control (NOCC) protocols. The analysis shows that CDA improves packet delivery ratio by 33% as compared to CAF and 54% as compared to NOCC. CDA also shows an improvement in throughput by 16% as compared to CAF and 36% as compared to NOCC. Additionally, it reduces End-To-End delay by 17% as compared to CAF and 38% as compared to NOCC.

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Trinh, Truc, and Khang Nguyen. "A Vietnamese benchmark for vehicle detection and real-time empirical evaluation." Can Tho University Journal of Science 14, no. 3 (November 29, 2022): 45–52. http://dx.doi.org/10.22144/ctu.jen.2022.042.

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The current situation of traffic in Vietnam has many outstanding problems, especially traffic congestion, since the supply of infrastructure has often not been able to keep up with the growth in mobility. Thus, proposing monitoring plans to support authorities to make suitable and prompt decisions has always received large attention from the community. Meanwhile, applying information technology, especially advanced models which could process or analyze traffic data in real time is recently considered to be a priority solution due to the time, accuracy, and cost saving that it can potentially achieve. Therefore, this paper outlines research on three advanced real-time object detection methods: YOLOX, YOLOF, and YOLACT and the development of the newest Vietnamese traffic dataset named UIT-VinaDeveS22. The work contains both theoretical and empirical analysis, which are expected to create premises for further studies into addressing problems such as traffic density management, traffic separation, and traffic congestion.

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Anjaneyulu, Mohandu, and Mohan Kubendiran. "Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique." Sustainability 15, no. 1 (December 21, 2022): 74. http://dx.doi.org/10.3390/su15010074.

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A vital problem faced by urban areas, traffic congestion impacts wealth, climate, and air pollution in cities. Sustainable transportation systems (STSs) play a crucial role in traffic congestion prediction for adopting transportation networks to improve the efficiency and capacity of traffic management. In STSs, one of the essential functional areas is the advanced traffic management system, which alleviates traffic congestion by locating traffic bottlenecks to intensify the interpretation of the traffic network. Furthermore, in urban areas, accurate short-term traffic congestion forecasting is critical for designing transport infrastructure and for the real-time optimization of traffic. The main objective of this paper was to devise a method to predict short-term traffic congestion (STTC) every 5 min over 1 h. This paper proposes a hybrid Xception support vector machine (XPSVM) classifier model to predict STTC. Primarily, the Xception classifier uses separable convolution, ReLU, and convolution techniques to predict the feature detection in the dataset. Secondarily, the support vector machine (SVM) classifier operates maximum marginal separations to predict the output more accurately using the weight regularization technique and a fine-tuned binary hyperplane mechanism. The dataset used in this work was taken from Google Maps and comprised snapshots of Bangalore, Karnataka, taken using the Selenium automation tool. The experimental outcome showed that the proposed model forecasted traffic congestion with an accuracy of 97.16%.

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Keerthana, Jeniffer. "Traffic Density Detection and Signal Adjustment Using IR Sensor." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3117–21. http://dx.doi.org/10.22214/ijraset.2023.54018.

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Abstract: The main issue in today's society is traffic congestion in urban areas. When the number of vehicles is increased quickly, both peak and off-peak hours experience traffic congestion. This results in less effective road traffic management. Systems for controlling traffic lights rely on the traffic signals' set time intervals. These time-based signals waste time for the side of a small number of vehicles on the road, which is greater than another road of vehicles at a high pace, and make them wait for a very long period. The advanced method focuses on the minimal amount of time that automobiles on a road waste. Therefore, it gives the density-detected lane extra time and gives the other lanes the same amount of time. The lane with low density. The IR sensor and the 8051 series AT89S52 microcontroller can be used to accomplish this.

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CHEN, JING, EVAN TAN, and ZHIDONG LI. "A MACHINE LEARNING FRAMEWORK FOR REAL-TIME TRAFFIC DENSITY DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 07 (November 2009): 1265–84. http://dx.doi.org/10.1142/s0218001409007673.

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Traffic flow information can be employed in an intelligent transportation system to detect and manage traffic congestion. One of the key elements in determining the traffic flow information is traffic density estimation. The goal of traffic density estimation is to determine the density of vehicles on a given road from loop detectors, traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and traffic radars, there is a growing trend of using video-content-understanding technique to determine the traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy traffic congestions. In this paper, we attempt to address the problem of real-time traffic density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the traffic density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of traffic density estimation. Thus, we propose a novel feature extraction scheme to represent traffic density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average traffic density estimation accuracy of 96.6% over various different illumination and weather conditions.

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Hall, Randolph W., and Nilesh Vyas. "Buses as a Traffic Probe: Demonstration Project." Transportation Research Record: Journal of the Transportation Research Board 1731, no. 1 (January 2000): 96–103. http://dx.doi.org/10.3141/1731-12.

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The congestion probe feature of the Orange County Transportation Authority (California) bus probe project was evaluated by comparing automobile and bus trajectories and examining alternative congestion detection methods. The focus was city streets on which delays occur at signalized intersections and bus delays at bus stops. The analysis revealed that when automobiles have long delays, buses traveling nearby on the same route are also likely to be delayed. The reverse situation, however, is not always true, because buses frequently wait for extended periods when they run ahead of schedule. Any useful bus probe algorithm needs to distinguish between actual congestion and a stopping delay. Although the transit probe was designed to measure congestion on roadway segments, a more useful approach would be to measure congestion approaching major intersections, where delays are likely to occur. Moreover, because delays randomly fluctuate according to a vehicle’s arrival time relative to the signal cycle, the most sensible approach is to set off a "congestion alarm" when a vehicle is delayed by more than one cycle at an intersection. A congestion alarm would indicate oversaturation and delay well above normal.

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Shrivastava, Disha, and Arun Agrawal. "Traffic Congestion Detection in Vehicular Adhoc Networks using GPS." IOSR Journal of Computer Engineering 16, no. 2 (2014): 63–69. http://dx.doi.org/10.9790/0661-16216369.

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Wang, Qi, Jia Wan, and Yuan Yuan. "Locality constraint distance metric learning for traffic congestion detection." Pattern Recognition 75 (March 2018): 272–81. http://dx.doi.org/10.1016/j.patcog.2017.03.030.

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Yong-chuan, Zhang, Zuo Xiao-qing, Zhang li-ting, and Chen Zhen-ting. "Traffic Congestion Detection Based On GPS Floating-Car Data." Procedia Engineering 15 (2011): 5541–46. http://dx.doi.org/10.1016/j.proeng.2011.08.1028.

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Li, Shen, Jian Zhang, Gang Zhong, and Bin Ran. "A Simulation Approach to Detect Arterial Traffic Congestion Using Cellular Data." Journal of Advanced Transportation 2022 (February 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/8811139.

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Cellular data provide a promising way for congestion detection with low cost and high coverage, and the simulation study is a feasible solution to verify the detection method. This paper presents a simulation approach that uses cellular data to detect traffic congestion on urban arterials based on the relationship between cellular data and traffic status. The virtual testbed, which includes three main modules, is developed to perform the cellular activities generation, collection, and aggregation process between cell phones and cell stations. Then, the correlation between cellular data and traffic status data is studied. Finally, three scenarios using the data from testbed are demonstrated to measure the performance of the proposed method under different conditions. The results indicate that the proposed approach is a feasible and efficient way to simulate cellular data generation, collection, and aggregation process. Also, it can be the base for further analysis to detect traffic congestion on arterials using cellular data.

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Journal articles on the topic 'TRAFFIC CONGESTION DETECTION'

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