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Journal articles on the topic 'Electronic traffic controls Research'

Author: Grafiati

Published: 10 December 2022

Last updated: 31 July 2025

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1

G, Dr Kanjana. "Adaptive Traffic Management System Using Reinforcement Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1387–92. https://doi.org/10.22214/ijraset.2025.67570.

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Traffic congestion is a significant issue in urban areas, leading to increased travel delays, fuel consumption, and environmental pollution. Traditional traffic management systems, which use fixed-timer signals, rule-based controls, manual intervention by traffic police, and electronic sensor-based methods, often struggle to adapt to dynamic traffic conditions. To address these challenges an Adaptive Traffic Management System (ATMS) using Reinforcement Learning (RL) is proposed to optimize signal timings and improve traffic flow. The system dynamically adjusts traffic signal timings based on r

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2

Saad, Aldosary, Ahmed Shalaby, and Abdallah A. Mohamed. "Research on the internet of vehicles assisted traffic management systems for observing traffic density." Computers and Electrical Engineering 101 (July 2022): 108100. http://dx.doi.org/10.1016/j.compeleceng.2022.108100.

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Chen, Feng, Qi Zhang, Yuanhua Jia, and Jian Li. "Research of traffic flow multi-objectives intelligent control method for junction network." Telecommunication Systems 53, no. 1 (2013): 77–84. http://dx.doi.org/10.1007/s11235-013-9679-0.

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Yadav, Pamul, Ashutosh Mishra, and Shiho Kim. "A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles." Sensors 23, no. 10 (2023): 4710. http://dx.doi.org/10.3390/s23104710.

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Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can solve complex problems involving simultaneous controls. Recently, many researchers applied MARL in such applications. However, there is a lack of extensive surveys on the ongoing research to identify the current problems, proposed methods, and future research directions in

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Huang, Yung-Fa, Chuan-Bi Lin, Chien-Min Chung, and Ching-Mu Chen. "Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning." Electronics 10, no. 12 (2021): 1376. http://dx.doi.org/10.3390/electronics10121376.

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In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine lea

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Bouktif, Salah, Abderraouf Cheniki, and Ali Ouni. "Traffic Signal Control Using Hybrid Action Space Deep Reinforcement Learning." Sensors 21, no. 7 (2021): 2302. http://dx.doi.org/10.3390/s21072302.

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Recent research works on intelligent traffic signal control (TSC) have been mainly focused on leveraging deep reinforcement learning (DRL) due to its proven capability and performance. DRL-based traffic signal control frameworks belong to either discrete or continuous controls. In discrete control, the DRL agent selects the appropriate traffic light phase from a finite set of phases. Whereas in continuous control approach, the agent decides the appropriate duration for each signal phase within a predetermined sequence of phases. Among the existing works, there are no prior approaches that prop

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Wu, Fei, Ting Li, Fucai Luo, Shulin Wu, and Chuanqi Xiao. "Intelligent Network Traffic Control Based on Deep Reinforcement Learning." International Journal of Circuits, Systems and Signal Processing 16 (January 14, 2022): 585–94. http://dx.doi.org/10.46300/9106.2022.16.73.

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This paper studies the problems of load balancing and flow control in data center network, and analyzes several common flow control schemes in data center intelligent network and their existing problems. On this basis, the network traffic control problem is modeled with the goal of deep reinforcement learning strategy optimization, and an intelligent network traffic control method based on deep reinforcement learning is proposed. At the same time, for the flow control order problem in deep reinforcement learning algorithm, a flow scheduling priority algorithm is proposed innovatively. Accordin

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Mathiane, Malose John, Chunling Tu, Pius Adewale, and Mukatshung Nawej. "A Vehicle Density Estimation Traffic Light Control System Using a Two-Dimensional Convolution Neural Network." Vehicles 5, no. 4 (2023): 1844–62. http://dx.doi.org/10.3390/vehicles5040099.

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One of the world’s challenges is the amount of traffic on the roads. Waiting for the green light is a major cause of traffic congestion. Low throughput rates and eventual congestion come from many traffic signals that are hard coded, irrespective of the volume of the amount of traffic. Instead of depending on predefined time intervals, it is essential to build a traffic signal control system that can react to changing vehicle densities. Emergency vehicles, like ambulances, must be given priority at the intersection so as not to spend more time at the traffic light. Computer vision techniques c

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9

Nedyalkov, Ivan. "Application of GNS3 to Study the Security of Data Exchange between Power Electronic Devices and Control Center." Computers 12, no. 5 (2023): 101. http://dx.doi.org/10.3390/computers12050101.

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This paper proposes the use of the GNS3 IP network modeling platform to study/verify whether the exchanged information between power electronic devices and a control center (Monitoring and Control Centre) is secure. For the purpose of this work, a power distribution unit (PDU) and a UPS (Uninterruptable Power Supply) that are used by internet service providers are studied. Capsa Free network analyzer and Wireshark network protocol analyzer were used as supporting tools. A working model of an IP network in GNS3 has been created through which this research has been carried out. In addition to ch

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Deshpande, Siddhesh, and Sheng-Jen Hsieh. "Cyber-Physical System for Smart Traffic Light Control." Sensors 23, no. 11 (2023): 5028. http://dx.doi.org/10.3390/s23115028.

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In recent years, researchers have proposed smart traffic light control systems to improve traffic flow at intersections, but there is less focus on reducing vehicle and pedestrian delays simultaneously. This research proposes a cyber-physical system for smart traffic light control utilizing traffic detection cameras, machine learning algorithms, and a ladder logic program. The proposed method employs a dynamic traffic interval technique that categorizes traffic into low, medium, high, and very high volumes. It adjusts traffic light intervals based on real-time traffic data, including pedestria

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11

Baskar Ravi, Et al. "Method of Assortment Control for Sector Boundary Traffic Signals Using Organic Computing." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 366–69. http://dx.doi.org/10.17762/ijritcc.v11i4.9841.

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The research focuses on developing anassortment control procedure for traffic signals at sector boundaries using organic computing principles. This study lies at the intersection of urban traffic signal control and artificial intelligence. The proposed procedure comprises various modules, including traffic flow monitoring, self-optimization, self-modification, evolutionary learning, self-assessment, and self-adaptation. The objective is to achieve efficient assortment between traffic signals at sector boundaries, thus preventing congestion and traffic blockages in the intersecting areas.

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12

Abunadi, Ibrahim, Amjad Rehman, Khalid Haseeb, Lorena Parra, and Jaime Lloret. "Traffic-Aware Secured Cooperative Framework for IoT-Based Smart Monitoring in Precision Agriculture." Sensors 22, no. 17 (2022): 6676. http://dx.doi.org/10.3390/s22176676.

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In recent decades, networked smart devices and cutting-edge technology have been exploited in many applications for the improvement of agriculture. The deployment of smart sensors and intelligent farming techniques supports real-time information gathering for the agriculture sector and decreases the burden on farmers. Many solutions have been presented to automate the agriculture system using IoT networks; however, the identification of redundant data traffic is one of the most significant research problems. Additionally, farmers do not obtain the information they need in time, such as data on

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Shi, Zhaolei, Nurbol Luktarhan, Yangyang Song, and Gaoqi Tian. "BFCN: A Novel Classification Method of Encrypted Traffic Based on BERT and CNN." Electronics 12, no. 3 (2023): 516. http://dx.doi.org/10.3390/electronics12030516.

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With the speedy advancement of encryption technology and the exponential increase in applications, network traffic classification has become an increasingly important research topic. Existing methods for classifying encrypted traffic have certain limitations. For example, traditional approaches such as machine learning rely heavily on feature engineering, deep learning approaches are susceptible to the amount and distribution of labeled data, and pretrained models focus merely on the global traffic features while ignoring local features. To solve the above problem, we propose a BERT-based byte

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14

Zhengxing, Xiao, Jiang Qing, Nie Zhe, et al. "Research on intelligent traffic light control system based on dynamic Bayesian reasoning." Computers & Electrical Engineering 84 (June 2020): 106635. http://dx.doi.org/10.1016/j.compeleceng.2020.106635.

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15

Mohamed, Nazar Elfadil, and Intisar Ibrahim Radwan. "Traffic light control design approaches: a systematic literature review." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 5355. http://dx.doi.org/10.11591/ijece.v12i5.pp5355-5363.

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<span>To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review’s aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the stu

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He, Shuilong, Yongliang Wang, Yuye Chen, Fei Xiao, Jucai Deng, and Enyong Xu. "Research on Safety Evaluation of Commercial Vehicle Driving Behavior Based on Data Mining Technology." Journal of Sensors 2021 (November 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/9927348.

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The arrival of big data era of internet of vehicles promotes the rapid development of logistics industry, which also indirectly leads to the high traffic accident rate, resulting in huge casualties and property losses. Driving behavior is considered the most central factor leading to traffic accidents. Therefore, a scientific and effective method for evaluating the safety of commercial vehicle driving behavior is urgently needed. In this study, a comprehensive evaluation model of driving behavior security based on multimembership function is proposed, and entropy weight method (EWM), analytic

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17

Zhang, Baoqun, Cheng Gong, Yan Wang, Longfei Ma, Dongying Zhang, and Shiwei Xia. "Research on the Collaborative Optimization of the Power Distribution Network and Traffic Network Based on Dynamic Traffic Allocation." Energies 16, no. 14 (2023): 5259. http://dx.doi.org/10.3390/en16145259.

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With the increasing penetration rate of electric vehicles, the spatiotemporal coupling relationship between the power distribution network and traffic network is stronger than ever before. Under the dynamic wireless charging mode, traffic jam charging is introduced and the dynamic loading process of traffic flow is described using a cellular transmission model. The charging load is related to traffic flow and serves as a bond between the power distribution network and traffic network. The traffic flow achieves balanced allocation under dynamic user equilibrium conditions, and cooperatively opt

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18

Yu, Chaodong, Jian Chen, and Geming Xia. "Coordinated Control of Intelligent Fuzzy Traffic Signal Based on Edge Computing Distribution." Sensors 22, no. 16 (2022): 5953. http://dx.doi.org/10.3390/s22165953.

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With the development of Internet of Things infrastructures and intelligent traffic systems, the traffic congestion that results from the continuous complexity of urban road networks and traffic saturation has a new solution. In this research, we propose a traffic signal control scenario based on edge computing. We also propose a chemical reaction–cooperative particle swarm optimization (CRO-CPSO) algorithm so that flexible traffic control is sunk to the edge. To implement short-term real-time vehicle waiting time prediction as a collaborative judgment of CRO-CPSO, we suggest a traffic flow pre

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19

Li, Tao, Baoli Gong, Yong Peng, et al. "Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data." Energies 16, no. 17 (2023): 6235. http://dx.doi.org/10.3390/en16176235.

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With the development of the economy, urban road transportation has been continuously improved, and the number of motor vehicles has also increased significantly, leading to serious energy consumption issues. As critical nodes in the urban road transportation network, intersections have become a focal point of research on vehicle energy consumption. To investigate whether traffic signal lights affect fuel consumption and emissions, this study analyzed the operating characteristics, fuel consumption, and emissions of intersections with and without traffic signal lights using real-world vehicle d

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20

Myasnikov, V. V., A. A. Agafonov, and A. S. Yumaganov. "A deterministic predictive traffic signal control model in intelligent transportation and geoinformation systems." Computer Optics 45, no. 6 (2021): 917–25. http://dx.doi.org/10.18287/2412-6179-co-1031.

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In this paper, we propose a traffic signal control method in intelligent transportation and geoinformation systems, based on a deterministic predictive model. The method provides adaptive control based on traffic data, including data from connected and autonomous vehicles. The proposed method is compared with the state-of-the-art traffic signal control solutions: empirical control algorithms and reinforcement learning-based control methods. An advantage of the proposed method is shown and directions of further research are outlined.

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21

Ge, Huimin, Lei Dong, Mingyue Huang, Wenkai Zang, and Lijun Zhou. "Adaptive Kernel Density Estimation for Traffic Accidents Based on Improved Bandwidth Research on Black Spot Identification Model." Electronics 11, no. 21 (2022): 3604. http://dx.doi.org/10.3390/electronics11213604.

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At present, the total length of accident blackspot accounts for 0.25% of the total length of the road network, while the total number of accidents that occurred at accident black spots accounts for 25% of the total number of accidents on the road network. This paper describes a traffic accident black spot recognition model based on the adaptive kernel density estimation method combined with the road risk index. Using the traffic accident data of national and provincial trunk lines in Shanghai and ArcGIS software, the recognition results of black spots were compared with the recognition results

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22

Subramaniam, Mahendrakumar, Chunchu Rambabu, Gokul Chandrasekaran, and Neelam Sanjeev Kumar. "A Traffic Density-Based Congestion Control Method for VANETs." Wireless Communications and Mobile Computing 2022 (October 26, 2022): 1–14. http://dx.doi.org/10.1155/2022/7551535.

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This research presents a vehicle ID-based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID-based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. This leads to identifying a car ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A traffic density based-congestion control algorithm (TDCCA) is sugge

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23

Zheng, Guorong, Yuke Liu, Yazhou Fu, Yingjie Zhao, and Zundong Zhang. "Perimeter Control Method of Road Traffic Regions Based on MFD-DDPG." Sensors 23, no. 18 (2023): 7975. http://dx.doi.org/10.3390/s23187975.

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As urban areas continue to expand, traffic congestion has emerged as a significant challenge impacting urban governance and economic development. Frequent regional traffic congestion has become a primary factor hindering urban economic growth and social activities, necessitating improved regional traffic management. Addressing regional traffic optimization and control methods based on the characteristics of regional congestion has become a crucial and complex issue in the field of traffic management and control research. This paper focuses on the macroscopic fundamental diagram (MFD) and aims

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Liu, Junhao, Qing Cheng, Yuanji Wang, et al. "An Improved Genetic Algorithm-Based Traffic Scheduling Model for Airport Terminal Areas." Journal of Sensors 2022 (March 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/7926335.

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This paper takes the airport terminal area as the main research content and combines genetic algorithm with airport terminal area analysis theory to analyze and study the traffic scheduling in the airport terminal area. Based on the study of traditional traffic scheduling techniques and key techniques of genetic algorithms, this paper participates in the actual project of genetic algorithm-based traffic scheduling, analyzes the requirements of the project, focuses on the design and implementation of the traffic scheduling algorithm module in the genetic algorithm-based traffic scheduling syste

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Khelafa, Ilyas, Abdelhakim Ballouk, and Abdenaceur Baghdad. "Control algorithm for the urban traffic using a realtime simulation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (2021): 3934. http://dx.doi.org/10.11591/ijece.v11i5.pp3934-3942.

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Many types of research have been interesting by real-time control of urban networks. This paper, basing on a simplified urban traffic model, proposes a novel control approach based on model predictive control concept to reduce congestion and improve the safety of cars on the roads. The contributions of this paper are: First, we consider vehicle heterogeneity, represented by a mathematical model called “S Model” and integrate it with a realtime simulator to evaluate the performance of controllers on real traffic conditions. Second, in order to assess each controller's success under particular c

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Hou, Yue, Xin Zheng, Chengyan Han, Wei Wei, Rafał Scherer, and Dawid Połap. "Deep Learning Methods in Short-Term Traffic Prediction: A Survey." Information Technology and Control 51, no. 1 (2022): 139–57. http://dx.doi.org/10.5755/j01.itc.51.1.29947.

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Nowadays, traffic congestion has become a serious problem that plagues the development of many cities aroundthe world and the travel and life of urban residents. Compared with the costly and long implementation cyclemeasures such as the promotion of public transportation construction, vehicle restriction, road reconstruction, etc., traffic prediction is the lowest cost and best means to solve traffic congestion. Relevant departmentscan give early warnings on congested road sections based on the results of traffic prediction, rationalize thedistribution of police forces, and solve the traffic c

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Wang, Feng, and Zhaofeng Zhang. "Route Control and Behavior Decision of Intelligent Driverless Truck Based on Artificial Intelligence Technology." Wireless Communications and Mobile Computing 2022 (September 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/7025081.

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With the increase in global car ownership, the demand for traffic safety is very strong. Research shows that drivers account for more than 90% of global traffic accidents. Driverless cars can reduce traffic accidents caused for these reasons and greatly improve traffic safety. At the same time, driverless real-time path planning can select the best driving route for vehicles, reduce traffic congestion, and improve the efficiency of transportation. To sum up, driverless vehicles are considered an important solution to ensure traffic safety, improve traffic efficiency, reduce energy consumption

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Shen, Guojiang, Xiangyu Zhu, Wei Xu, Longfeng Tang, and Xiangjie Kong. "Research on Phase Combination and Signal Timing Based on Improved K-Medoids Algorithm for Intersection Signal Control." Wireless Communications and Mobile Computing 2020 (May 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/3240675.

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Aiming at the problem of intersection signal control, a method of traffic phase combination and signal timing optimization based on the improved K-medoids algorithm is proposed. Firstly, the improvement of the traditional K-medoids algorithm embodies in two aspects, namely, the selection of the initial medoids and the parameter k, which will be applied to the cluster analysis of historical saturation data. The algorithm determines the initial medoids based on a set of probabilities calculated from the distance and determines the number of clusters k based on an exponential function, weight adj

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Huang, Chenn-Jung, Kai-Wen Hu, Hsing-Yi Ho, and Hung-Wen Chuang. "Congestion-Preventing Routing and Charging Scheduling Mechanism for Electric Vehicles in Dense Urban Areas." Information Technology and Control 50, no. 2 (2021): 284–307. http://dx.doi.org/10.5755/j01.itc.50.2.27780.

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Traffic congestion in metropolitan areas all over the world has become a critical issue that governments mustdeal with effectively. Traffic congestion during rush hours causes vehicle drivers to arrive late at their destinations,resulting in significant economic losses. Although researchers have proposed solutions to the traffic congestionproblem, little research work has presented a joint route and charging planning strategy for electric vehicles(EVs) that alleviates traffic congestion problems simultaneously. Accordingly, a congestion-preventing route and charging planning mechanism for EVs

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KUMARNATH, J., and K. BATRI. "Optimized Traffic Grooming through modified PSO based Iterative Hungarian algorithm in Optical Networks." Information Technology and Control 50, no. 3 (2021): 546–57. http://dx.doi.org/10.5755/j01.itc.50.3.28672.

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Due to huge size of the data and quick transmission of data between the nodes present in the optical network, a condition of network traffic is created among the nodes of the network. This issue of traffic can be overcome by employing numerous traffic grooming techniques. In this research paper, the best suitable shortest path is determined by the multi objective modified PSO algorithm and an innovative visibility graph based Iterative Hungarian Traffic grooming algorithm is implemented to reduce the blocking ratio through improving the allocation of bandwidth between the users. Then finally t

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Jilani, Umair, Muhammad Asif, Munaf Rashid, Ali Akbar Siddique, Syed Muhammad Umar Talha, and Muhammad Aamir. "Traffic Congestion Classification Using GAN-Based Synthetic Data Augmentation and a Novel 5-Layer Convolutional Neural Network Model." Electronics 11, no. 15 (2022): 2290. http://dx.doi.org/10.3390/electronics11152290.

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Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic congestion on the roads has been more frequent and severe with the continuous rise in the numbers of cars on the road. The estimation of traffic flow, or conversely, traffic congestion identification, is of critical importance in a wide variety of applications, including intelligent transportation systems (ITS). Recently, artificial intelligence (AI) has been in the limelight for sophisticated ITS solutions. However, AI-based schemes are typically heavily dependent on the quantity and quality of data

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Sobieraj, Maciej, Piotr Zwierzykowski, and Erich Leitgeb. "Modelling and Optimization of Multi-Service Optical Switching Networks with Threshold Management Mechanisms." Electronics 10, no. 13 (2021): 1515. http://dx.doi.org/10.3390/electronics10131515.

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DWDM networks make use of optical switching networks that allow light waves of multiple lengths to be serviced and provide the possibility of converting them appropriately. Research work on optical switching networks focuses on two main areas of interest: new non-blocking structures for optical switching networks and finding traffic characteristics of switching networks of the structures that are already well known. In practical design of switching nodes in optical networks, in many cases, the Clos switching networks are successfully used. Clos switching networks are also used in Elastic Optic

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Han, Guangjie, Qi Zheng, Lyuchao Liao, Penghao Tang, Zhengrong Li, and Yintian Zhu. "Deep Reinforcement Learning for Intersection Signal Control Considering Pedestrian Behavior." Electronics 11, no. 21 (2022): 3519. http://dx.doi.org/10.3390/electronics11213519.

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Using deep reinforcement learning to solve traffic signal control problems is a research hotspot in the intelligent transportation field. Researchers have recently proposed various solutions based on deep reinforcement learning methods for intelligent transportation problems. However, most signal control optimization takes the maximization of traffic capacity as the optimization goal, ignoring the concerns of pedestrians at intersections. To address this issue, we propose a pedestrian-considered deep reinforcement learning traffic signal control method. The method combines a reinforcement lear

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Shi, Yanjun, Yuhan Qi, Lingling Lv, and Donglin Liang. "A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control." Machines 9, no. 11 (2021): 280. http://dx.doi.org/10.3390/machines9110280.

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Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed

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Paszkowski, Jan, Marcus Herrmann, Matthias Richter, and Andrzej Szarata. "Modelling the Effects of Traffic-Calming Introduction to Volume–Delay Functions and Traffic Assignment." Energies 14, no. 13 (2021): 3726. http://dx.doi.org/10.3390/en14133726.

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Traffic calming is introduced to minimise the negative results of motor vehicle use, for example, low safety level or quality of life, high noise and pollution. It can be implemented through the introduction of road infrastructure reducing the velocity and the traffic volume. In this paper, we studied how traffic-calming influences the traffic assignment. For the research, a traffic-calming measure of speed cushions on the Stachiewicza street in Krakow was taken. A method of extracting trajectories from aerial footage was shown, and it was used to build a model. For a given example, through dr

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Tomar, Ishu, Indu Sreedevi, and Neeta Pandey. "State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends." Electronics 11, no. 3 (2022): 465. http://dx.doi.org/10.3390/electronics11030465.

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The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accident

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Alanazi, Fayez, and Ping Yi. "Control logic algorithm to create gaps for mixed traffic: A comprehensive evaluation." Open Engineering 12, no. 1 (2022): 273–92. http://dx.doi.org/10.1515/eng-2022-0035.

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Abstract Over the last decade, the increase in the number of vehicles has affected traffic performance, causing traffic congestion. However, intersections, where different flows intersect, are among the primary causes of traffic congestion besides bottlenecks. Bottlenecking in the minor stream is mainly due to the extended queueing, specifically due to minimal gaps in the mainline stream as the intersection’s high priority exists with the major stream. This research aims to control connected and automated vehicles (CAVs) to help generate additional usable gaps for the minor road vehicles to en

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Jereb, Borut, Ondrej Stopka, and Tomáš Skrúcaný. "Methodology for Estimating the Effect of Traffic Flow Management on Fuel Consumption and CO2 Production: A Case Study of Celje, Slovenia." Energies 14, no. 6 (2021): 1673. http://dx.doi.org/10.3390/en14061673.

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The manuscript discusses the investigation of vehicle flow in a predesignated junction by an appropriate traffic flow management with an effort to minimize fuel consumption, the production of CO2, an essential greenhouse gas (hereinafter referred to as GHG), and related transport costs. The particular research study was undertaken in a frequented junction in the city of Celje, located in the eastern part of Slovenia. The results obtained summarize data on consumed fuel and produced CO2 amounts depending on the type of vehicle, traffic flow mixture, traffic light signal plan, and actual vehicle

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Moumen, Idriss, Jaafar Abouchabaka, and Najat Rafalia. "Enhancing urban mobility: integration of IoT road traffic data and artificial intelligence in smart city environment." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 2 (2023): 985. http://dx.doi.org/10.11591/ijeecs.v32.i2.pp985-993.

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<span>Efficient traffic management poses a significant challenge in smart cities, requiring the integration of diverse approaches. This paper presents an artificial intelligence framework that integrates internet of things (IoT) road traffic data to optimize traffic flow in smart city environments. Real-time traffic data is collected using IoT edge sensors, processed using machine learning (support vector machines, logistic regression, k-nearest neighbors) and deep learning long short-term memory (LTSM) algorithms, and utilized to develop accurate short-term and long-term traffic forecas

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Gonçalves, Fábio, Gonçalo O. Silva, Alexandre Santos, et al. "Urban Traffic Simulation Using Mobility Patterns Synthesized from Real Sensors." Electronics 12, no. 24 (2023): 4971. http://dx.doi.org/10.3390/electronics12244971.

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Smart cities are an ongoing research topic with multiple sub-research areas, from traffic control to optimization and even safety. However, testing the new methodologies or technologies directly in the real world is an almost impossible feat that, inclusively, can result in disaster. Thus, there is the importance of simulation. Simulation enables testing new and complex methodologies and gauging their impact in a realistic context without adding any safety issues. Additionally, these can accurately map real-world conditions depending on the simulation configuration. One key aspect of the simul

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Baek, Ui-Jun, Boseon Kim, Jee-Tae Park, Jeong-Woo Choi, and Myung-Sup Kim. "A Multi-Task Classification Method for Application Traffic Classification Using Task Relationships." Electronics 12, no. 17 (2023): 3597. http://dx.doi.org/10.3390/electronics12173597.

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As IT technology advances, the number and types of applications, such as SNS, content, and shopping, have increased across various fields, leading to the emergence of complex and diverse application traffic. As a result, the demand for effective network operation, management, and analysis has increased. In particular, service or application traffic classification research is an important area of study in network management. Web services are composed of a combination of multiple applications, and one or more application traffic can be mixed within service traffic. However, most existing researc

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Zaibi, Ameur, Anis Ladgham, and Anis Sakly. "A Lightweight Model for Traffic Sign Classification Based on Enhanced LeNet-5 Network." Journal of Sensors 2021 (April 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/8870529.

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For several years, much research has focused on the importance of traffic sign recognition systems, which have played a very important role in road safety. Researchers have exploited the techniques of machine learning, deep learning, and image processing to carry out their research successfully. The new and recent research on road sign classification and recognition systems is the result of the use of deep learning-based architectures such as the convolutional neural network (CNN) architectures. In this research work, the goal was to achieve a CNN model that is lightweight and easily implement

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Li, Qingfeng, Yaqiu Liu, Tong Niu, and Xiaoming Wang. "Improved Resnet Model Based on Positive Traffic Flow for IoT Anomalous Traffic Detection." Electronics 12, no. 18 (2023): 3830. http://dx.doi.org/10.3390/electronics12183830.

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The Internet of Things (IoT) has been highly appreciated by several nations and societies as a worldwide strategic developing sector. However, IoT security is seriously threatened by anomalous traffic in the IoT. Therefore, creating a detection model that can recognize such aberrant traffic is essential to ensuring the overall security of the IoT. We outline the main approaches that are used today to detect anomalous network traffic and suggest a Resnet detection model based on fused one-dimensional convolution (Conv1D) for this purpose. Our method combines one-dimensional convolution and a Re

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Xia, Chunyan, Song Huang, Changyou Zheng, Zhen Yang, Tongtong Bai, and Lele Sun. "TraModeAVTest: Modeling Scenario and Violation Testing for Autonomous Driving Systems Based on Traffic Regulations." Electronics 13, no. 7 (2024): 1197. http://dx.doi.org/10.3390/electronics13071197.

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Current testing methods for autonomous driving systems primarily focus on simple traffic scenarios, generating test cases based on traffic accidents, while research on generating edge test cases for complex driving environments by traffic regulations is not adequately comprehensive. Therefore, we propose a method for scenario modeling and violation testing using an autonomous driving system based on traffic regulations named TraModeAVTest. Initially, TraModeAVTest constructs a Petri net model for complex scenarios based on the combination relationships of basic traffic regulation scenarios and

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Pustovoitov, Pavlo, Vitalii Voronets, Oleksandr Voronets, Halyna Sokol, and Maksym Okhrymenko. "Assessment of QOS indicators of a network with UDP and TCP traffic under a node peak load mode." Eastern-European Journal of Enterprise Technologies 1, no. 4 (127) (2024): 23–31. http://dx.doi.org/10.15587/1729-4061.2024.299124.

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The object of research is Markov models of network nodes with UDP (User Datagram Protocol) and TCP (Transmission Control Protocol) traffic and their differences. The task solved is the lack of Markov models of network nodes describing the behavior of TCP traffic from the point of view of packet retransmissions and packet delivery guarantees. Markov models of network nodes describing traffic behavior with guaranteed packet delivery have been further advanced. Given the comparison of the models, the differences from the classic models serving TCP traffic were shown, for each packet flow, an addi

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Zhang, Shuo, Fangyu Shen, Yaping Liu, Zhikai Yang, and Xinyu Lv. "A Novel Traffic Obfuscation Technology for Smart Home." Electronics 12, no. 16 (2023): 3477. http://dx.doi.org/10.3390/electronics12163477.

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With the widespread popularity of smart home devices and the emergence of smart home integration platforms such as Google, Amazon, and Xiaomi, the smart home industry is in a stage of vigorous development. While smart homes provide users with convenient and intelligent living, the problem of smart home devices leaking user privacy has become increasingly prominent. Smart home devices give users the ability to remotely control home devices, but they also reflect user home activities in traffic data, which brings the risk of privacy leaks. Potential attackers can use traffic classification techn

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Mukhamejanova, Almira D., Elans A. Grabs, Kumyssay K. Tumanbayeva, and Eleonora M. Lechshinskaya. "Traffic simulation in the LoRaWAN network." Bulletin of Electrical Engineering and Informatics 11, no. 2 (2022): 1117–25. http://dx.doi.org/10.11591/eei.v11i2.3484.

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LoRaWAN is one of the most commonly used technologies serving the internet of things (IoT) and machine-to-machine (M2M) devices. The traffic growth in the LoRaWAN network gives rise to many problems, which are solved using mathematical modelling. The actual task, in this case, is the development of a traffic simulation model in the LoRaWAN network. This article discusses the issues of traffic simulation in the LoRaWAN network and its research using the MATLAB system. The authors have developed a LoRaWAN network server model as a queuing system with incoming self-similar traffic in the MATLAB s

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Qi, Ji, Yuying Yuan, Wei Li, Fangjing Zhang, and Yali Li. "Risk Spatial Distribution and Fluctuation Mechanism of Ship Traffic System Based on Catastrophe Control and Intelligent Sensor." Wireless Communications and Mobile Computing 2022 (February 24, 2022): 1–15. http://dx.doi.org/10.1155/2022/4471351.

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Navigation control system is an important navigation building of inland-river in China. Because of its special semiclosed structure, when the efficiency of the mutation control system is low, the ship system cannot identify the ship, which may endanger the life safety of the crew and cause water traffic accidents. Based on catastrophe control and intelligent sensor, this paper studies the spatial distribution and fluctuation mechanism of risk in the ship traffic system, constructs a catastrophe control model, and combines with intelligent sensors to identify the spatial distribution and fluctu

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Berestov, Ihor, Oksana Pestremenko-Skrypka, Hanna Shelekhan, and Tetiana Berestova. "Digitalization of the Processes of Customs Control and Customs Clearance of Goods in Railway Transport." Central Ukrainian Scientific Bulletin. Technical Sciences 2, no. 5(36) (2022): 291–98. http://dx.doi.org/10.32515/2664-262x.2022.5(36).2.291-298.

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The article is devoted to the research of proposals on the organization of rational customs control, processing and passing of trains at the border transfer stations of Ukraine in the service international traffic. For quality work and fast processing of cars there is a need to improve the information component of the transportation process export and import freight flows through border transmission stations. This possibility is provided by the use of electronic declaration during the registration of international cargo operations. International transportation of goods is a necessary detail th

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Sütő, József. "An Improved Image Enhancement Method for Traffic Sign Detection." Electronics 11, no. 6 (2022): 871. http://dx.doi.org/10.3390/electronics11060871.

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Traffic sign detection (TRD) is an essential component of advanced driver-assistance systems and an important part of autonomous vehicles, where the goal is to localize image regions that contain traffic signs. Over the last decade, the amount of research on traffic sign detection and recognition has significantly increased. Although TRD is a built-in feature in modern cars and several methods have been proposed, it is a challenging problem due to the high computational demand, the large number of traffic signs, complex traffic scenes, and occlusions. In addition, it is not clear how can we pe

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