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1
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 (May 2013): 77–84. http://dx.doi.org/10.1007/s11235-013-9679-0.
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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 (June 8, 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 learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.
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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. According to the decision output, the corresponding flow control and control are carried out, so as to realize the load balance of the network. Finally, experiments show, the network traffic bandwidth loss rate of the proposed intelligent network traffic control method is low. Under the condition of random 60 traffic density, the average bisection bandwidth obtained by the proposed intelligent network traffic control method is 4.0mbps and the control error rate is 2.25%. The intelligent network traffic control method based on deep reinforcement learning has high practicability in the practical application process, and fully meets the research requirements.
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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 (March 25, 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 propose a flexible framework combining both discrete and continuous DRL approaches in controlling traffic signal. Thus, our ultimate objective in this paper is to propose an approach capable of deciding simultaneously the proper phase and its associated duration. Our contribution resides in adapting a hybrid Deep Reinforcement Learning that considers at the same time discrete and continuous decisions. Precisely, we customize a Parameterized Deep Q-Networks (P-DQN) architecture that permits a hierarchical decision-making process that primarily decides the traffic light next phases and secondly specifies its the associated timing. The evaluation results of our approach using Simulation of Urban MObility (SUMO) shows its out-performance over the benchmarks. The proposed framework is able to reduce the average queue length of vehicles and the average travel time by 22.20% and 5.78%, respectively, over the alternative DRL-based TSC systems.
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Zhengxing, Xiao, Jiang Qing, Nie Zhe, Wang Rujing, Zhang Zhengyong, Huang He, Sun Bingyu, Wang Liusan, and Wei Yuanyuan. "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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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 (September 3, 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 water pressure and soil conditions. Thus, these solutions consequently reduce the production rates and increase costs for farmers. Moreover, controlling all agricultural operations in a controlled manner should also be considered in developing intelligent solutions. Therefore, this study proposes a framework for a system that combines fog computing with smart farming and effectively controls network traffic. Firstly, the proposed framework efficiently monitors redundant information and avoids the inefficient use of communication bandwidth. It also controls the number of re-transmissions in the case of malicious actions and efficiently utilizes the network’s resources. Second, a trustworthy chain is built between agricultural sensors by utilizing the fog nodes to address security issues and increase reliability by preventing malicious communication. Through extensive simulation-based experiments, the proposed framework revealed an improved performance for energy efficiency, security, and network connectivity in comparison to other related works.
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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 (October 1, 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 study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities.</span>
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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 (January 19, 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-level feature convolutional network (BFCN) model consisting of two novel modules. The first is a packet encoder module, in which we use the BERT pretrained encrypted traffic classification model to capture global traffic features through its attention mechanism; the second is a CNN module, which captures byte-level local features in the traffic through convolutional operations. The packet-level and byte-level features are concatenated as the traffic’s final representation, which can better represent encrypted traffic. Our approach achieves state-of-the-art performance on the publicly available ISCX-VPN dataset for the traffic service and application identification task, achieving F1 scores of 99.11% and 99.41%, respectively, on these two tasks. The experimental results demonstrate that our method further improves the performance of encrypted traffic classification.
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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 hierarchy process (AHP), and fuzzy comprehensive evaluation algorithm are integrated. Firstly, the evaluation system of commercial vehicle safety of driving behavior is established. Secondly, the weight vector of each evaluation index is determined by combining EW-AHP to eliminate the subjectivity of the traditional AHP algorithm. Then, the fuzzy comprehensive evaluation matrix is calculated based on the multimembership function and fuzzy mathematics theory, and the quantitative evaluation of driving behavior safety is realized based on the matrix. Finally, the real road vehicle driving data and driving behavior data are verified by experiments. The experimental results show that the model can accurately and reasonably evaluate the safety of driving behavior, which is of great significance to improve road traffic safety.
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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 suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one-fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for high-standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back-off figures.
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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 (November 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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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 (November 4, 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 of the accident frequency method and the kernel density estimation method, and the clustering degree of recognition results of adaptive kernel density estimation method were analyzed. The results show that: the accident prediction accuracy index values of the accident frequency method, kernel density estimation method, and traffic accident black spot recognition model were 14.39, 16.36, and 18.25, respectively, and the lengths of the traffic accident black spot sections were 184.68, 162.45, and 145.57, respectively, which means that the accident black spot section determined by the accident black spot recognition model was the shortest and the number of traffic accidents identified was the largest. Considering the safety improvement budget of 20% of the road length, the adaptive kernel density estimation method could identify about 69% of the traffic accidents, which was 1.13 times and 1.27 times that of the kernel density estimation method and the accident frequency method, respectively.
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Yu, Chaodong, Jian Chen, and Geming Xia. "Coordinated Control of Intelligent Fuzzy Traffic Signal Based on Edge Computing Distribution." Sensors 22, no. 16 (August 9, 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 prediction system based on fuzzy logic. In addition, we introduce a co-factor (collaborative factor) set based on offline learning to take into account the experiential characteristics of intersections in urban road networks for the generation of strategies by the algorithm. Furthermore, the real case of Changsha County is simulated on the SUMO simulation platform. The issue of traffic flow saturation is improved by our method. Compared with other methods, our algorithm enhances the proportions of vehicles that reach their destinations on time by 13.03%, which maximizes the driving experience for drivers. Meanwhile, our algorithm reduces the driving times of vehicles by 25.34%, thus alleviating traffic jams.
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Liu, Junhao, Qing Cheng, Yuanji Wang, Changqi Yang, Rui Zhou, Xinping Zhu, Di Yao, Junjie Zhou, Yaqian Du, and Shanshan Yang. "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 system, and conducts further research on the pathfinding by constraints submodule. In this paper, the flight approach and departure sequencing problem and runway allocation problem are the main research objects. The dynamic optimal scheduling model of flight approach and departure is established by considering the interests and demands of airlines and airports, and a new scheduling algorithm is proposed. In this paper, a brief introduction to the airport terminal area is given, and the feasibility of the approach/departure optimal scheduling is introduced from the perspective of airlines with a long-range parallel two-runway airport as the research background. Secondly, through the analysis of the flight approach and departure process and the study of the approach and departure cooperative optimization strategy, a single-runway flight approach and departure traffic scheduling model under the joint sequencing strategy is established with the optimization objective of minimizing the total flight delay time, and the model is solved by using the sliding time window algorithm. Then, based on the single-runway scheduling model, a multirunway multiobjective flight optimal scheduling model is established with the objectives of minimizing total delay time, increasing runway throughput per unit time and fairness of flight delay time allocation, and a dynamic algorithm (STW-GA) combining sliding time window algorithm and dual-structured chromosome genetic algorithm is proposed to solve the model.
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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 (October 1, 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 circumstances, the structured network-wide traffic controller based on model predictive control (MPC) theory is compared to a fixed time controller (FTC). Using two scenarios, different indicators are tested, i.e total time spent, vehicle number, queue length. The results show that the model predictive control quickly converges, with the different scenarios, and further improves social welfare.
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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 (March 26, 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 congestion problem. At the same time, due to the increasingreal-time requirements of current traffic prediction, short-term traffic prediction has become a subject of widespread concern and research. Currently, the most widely used model for short-term traffic prediction are deeplearning models. This survey studied the relevant literature on the use of deep learning models to solve shortterm traffic prediction problem in the top journals of transportation in recent years, summarized the currentcommonly used traffic datasets, the mainstream deep learning models and their applications in this field. Finally, the challenges and future development trends of deep learning models applied in this field are discussed.
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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 adjustment, and elbow ideas. Secondly, a phase combination model is established based on the saturation and green split data, and the signal timing is optimized through a bilevel programming model. Finally, the algorithm is evaluated over a certain intersection in Hangzhou, and results show that this algorithm can reduce the average vehicle delay and queue length and improve the traffic capacity of the intersection in the peak hour.
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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 and pollution, and change travel mode. An intelligent driverless vehicle is a key component of the intelligent transportation system, which organically combines various functions such as. Among them, path tracking and motion control play a very important role in intelligent driverless technology. At the same time, accurately tracking the desired feasible path and stable motion control are the basis of intelligent unmanned driving. Based on this, this paper uses artificial intelligence technology to study the path control and behavior decision-making of intelligent driverless trucks, and an improved tracking control method is proposed. Through this improved method, the intelligent unmanned vehicle can track the desired feasible path under different curvatures more accurately and stably. Finally, through the road test experiment of the intelligent unmanned vehicle experimental platform in the actual environment, the effectiveness of the scheme design and related algorithms of intelligent unmanned vehicle motion control in this paper is verified.
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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 (June 17, 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 is proposed in this work to tackle the complicated route and charging optimizationproblems of EVs. The route and charging planning proposed in this work analyzes the information providedby EVs, the charging points, and road traffic information simultaneously, and mediates the traffic jammingby means of a route and charging reservation mechanism. Possible occurrence of traffic congestion is detectedin advance and traffic regulation is carried out by allocating an elastic range to the traveling period for late-bookingEVs, to avoid moving during rush hours. EV owners are also encouraged to provide rideshare services forlate-booking EV users during rush hours. The simulation results reveal that the proposed work can satisfy thepreferred route and charging demands of EV users and alleviate traffic congestion effectively.
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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 (September 24, 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 the performance analysis is carried out by means of performance measures such as traffic throughput, transceivers count, average propagation delay, blocking ratio, and success ratio. It can be inferred that the proposed work obtains enhanced outcomes when compared to the other existing techniques.
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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 (July 22, 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. Typical traffic data have been found to be insufficient and less efficient in AI-based ITS solutions. Advanced data cleaning and preprocessing methods offer a solution for this problem. Such techniques enable quality improvement and augmenting additional information in the traffic congestion dataset. One such efficient technique is the generative adversarial network (GAN), which has attracted much interest from the research community. This research work reports on the generation of a traffic congestion dataset with enhancement through GAN-based augmentation. The GAN-enhanced traffic congestion dataset is then used for training artificial intelligence (AI)-based models. In this research work, a five-layered convolutional neural network (CNN) deep learning model is proposed for traffic congestion classification. The performance of the proposed model is compared with that of a number of other well-known pretrained models, including ResNet-50 and DenseNet-121. Promising results present the efficacy of the proposed scheme using GAN-based data augmentation in a five-layered convolutional neural network (CNN) model for traffic congestion classification. The proposed technique attains accuracy of 98.63% compared with the accuracies of ResNet-50 and DenseNet-121, 90.59% and 93.15%, respectively. The proposed technique can be used for urban traffic planning and maintenance managers and stakeholders for the efficient deployment of intelligent transportation system (ITS).
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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 (June 23, 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 Optical Networks that can effectively manage allocation of resources to individual multi-service traffic streams. The research outcomes presented in this article deal with the problems of finding traffic characteristics in blocking optical switching networks with known structures. This article aims at presenting an analysis of the influence of traffic management threshold mechanisms on the traffic characteristics of multi-service blocking Clos switching networks that are used in the nodes of elastic optical networks as well as their influence on the traffic efficiency of network nodes. The analysis was carried out on the basis of research studies performed in a specially dedicated purpose-made simulation environment. The article presents a description of the simulation environment used in the experiments. The study was focused on the influence of the threshold mechanism, which is one of the most commonly used and elastic traffic management mechanisms, and on the traffic characteristics of switching networks that service different mixtures of multi-service Erlang, Engset and Pascal traffic streams. The conducted study validates the operational effectiveness and practicality of the application of the threshold mechanism to model traffic characteristics of nodes in an elastic optical network.
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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 (June 22, 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 driving characteristics research and microscopic modelling, volume–delay BPR functions were estimated—for a street with and without traffic calming. Later, a toy network of two roads of the same length, connecting the same origin and destination, was simulated using an equilibrium traffic assignment method. Simulations were conducted both with the use of PTV Vissim and Visum software and through individual calculations. According to the results of this paper, there was a difference in traffic volume according to the equilibrium traffic assignment in the aforementioned toy network as a function of total network traffic volume.
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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 (November 10, 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 that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.
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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 (October 29, 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 learning network and traffic signal control strategy with traffic efficiency and safety aspects. At the same time, the waiting time of pedestrians and vehicles passing through the intersection is considered, and the Discrete Traffic State Encoding (DTSE) method is applied and improved to define the more comprehensive states and rewards. In the training of the neural network, the multi-process operation method is adopted, and multiple environments are run for training simultaneously to improve the model’s training efficiency. Finally, extensive simulation experiments are conducted on actual intersection scenarios using the simulation software Simulation of Urban Mobility (SUMO). The results show that compared to Dueling DQN, the waiting time due to our method decreased by 58.76% and the number of people waiting decreased by 51.54%. The proposed method can reduce both the number of people waiting and the waiting time at intersections.
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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 (February 4, 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, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.
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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 (March 17, 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 velocity. These values were calculated separately for three different conditions of traffic flow management. Amounts of fuel consumed were experimentally investigated in real traffic situations, whereas CO2 production was calculated by applying the actual European standard entitled EN 16258:2012 associated with a guideline for measuring emission values, as well as by examining specific traffic flow parameters. The key objective of the manuscript is to present multiple scenarios towards striving to minimize environmental impacts and improve transport operation’s economic consequences when implementing proper traffic flow management. As for crucial findings, we quantified fuel consumption and CO2 emissions based on real data on the number and type of vehicles crossing the examined intersection and traffic light switching intervals. The results show that most of the CO2 was produced while waiting and in the accelerating phase in front of traffic lights, whereby in the running phase through the intersection, significantly less fuel was used. This study represents a mosaic fragment of research addressing endeavors to reduce CO2 production in urban transport. Following the experiments conducted, we can see a notable contribution towards reducing CO2 production with known and tested interventions in the existing transport infrastructure. A procedure embracing individual research steps may be deemed as an approach methodology dealing with traffic flow management with an aim to decrease the environmental and economic impacts of traffic and transport operation; this is where the novelty of the research lies.
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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 (January 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 enter the intersection without interrupting the mainline traffic flow. A probability function is developed to estimate the probability of CAVs creating additional usable gaps. The proposed logic is simulated in unsignalized and semi-actuated signalized intersections, and a field investigation is conducted. Simulation results show that the minor road delays and queue length are minimized without causing a significant delay to the mainline. Results show that major road interruptions are reduced at a semi-actuated signal control scenario when CAVs’ penetration increases. It can be observed that deploying CAVs in the road network with the proposed method can positively impact traffic efficiency, where the intersection’s performance and safety are improved.
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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 implemented for an embedded application and with excellent classification accuracy. We choose to work with an improved network LeNet-5 model for the classification of road signs. We trained our model network on the German Traffic Sign Recognition Benchmark (GTSRB) database and also on the Belgian Traffic Sign Data Set (BTSD), and it gave good results compared to other models tested by us and others tested by different researchers. The accuracy was 99.84% on GTSRB and 98.37% on BTSD. The lightness and the reduced number of parameters of our model (0.38 million) based on the enhanced LeNet-5 network pushed us to test our model for an embedded application using a webcam. The results we found are efficient, which emphasize the effectiveness of our method.
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Zhang, Jinxi, Wenying Zhu, Xueying Wu, and Tianshan Ma. "Traffic Information Collection Using Wireless Sensor Network Positioning Technology." Journal of Sensors 2021 (September 3, 2021): 1–10. http://dx.doi.org/10.1155/2021/6156258.
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The wireless sensor network integrates sensor technology, microelectromechanical technology, distributed information processing technology, and wireless communication technology. In order to solve this problem, this paper designs and proposes an anchor node self-location algorithm. Aiming at the positioning accuracy of wireless sensor network nodes, this paper proposes an improved algorithm for sensor network node positioning that uses error correction methods to reduce accumulated distance errors and positioning errors. In this paper, the designed routing algorithm is simulated and implemented, and the performance of the routing algorithm is evaluated based on different network topologies. From the analysis results, compared with the existing typical routing algorithms, the routing algorithms designed in this paper can effectively reduce the energy consumption of the network and prolong the lifetime of the network. The core of the algorithm is to integrate the known and available information of the system to locate unknown anchor nodes. This greatly reduces the number of anchor nodes whose initial position information is required by the system, and under the condition of less impact on the positioning accuracy of the system, the cost of the system is reduced and the scope of application of the system is improved. This paper has deeply studied the positioning and tracking problems in wireless sensor networks, including node positioning, biochemical gas source positioning, and target tracking, and designed and developed a platform for positioning and tracking application research to lay the foundation for further application research. In the study of the above problems, new methods of positioning and tracking with theoretical and practical value are proposed for different practical application scenarios, and the performance is verified and evaluated through computer simulation.
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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 (April 1, 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 system using a separate subsystem for the input traffic modelling allowing to change the number of sources in the LoRaWAN network. The simulation results made it possible to establish the dependences of the network server’s buffer memory, the probability of packet loss from the incoming self-similar traffic parameters, and reveal the possibilities of traffic modelling in the MATLAB system.
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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 fluctuation of risk in ship traffic. In this paper, a typical navigation control system of inland-river is selected as the research object, and the three-dimensional calculation model of the ship traffic system is established. The numerical simulation of catastrophe control and intelligent sensor in ship traffic systems under different ventilation conditions and the navigation water level is carried out by using computational fluid dynamics method, and the spatial distribution and fluctuation mechanism of ship traffic systems are analyzed. The results show that the maximum variation of sensitivity decreases monotonously with the decrease of the plate angle. When the other design parameters of the sensor remain unchanged, the maximum variation of sensitivity reaches the minimum when the plate angle is 10° which indicates that a small plate angle is helpful to reduce the eccentricity error. When the pitch increases from 56.32 mm to 98 mm, the maximum change of sensitivity decreases fastest, and the decrease trend of the maximum change of sensitivity slows down. In the range of 380 mm pitch, the maximum change of sensitivity decreases monotonously with the increase of pitch, which indicates that increasing the properly pitch is helpful to reduce the eccentricity error.
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Sütő, József. "An Improved Image Enhancement Method for Traffic Sign Detection." Electronics 11, no. 6 (March 10, 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 perform real-time traffic sign detection in embedded systems. In this paper, we focus on image enhancement, which is the first step of many object detection methods. We propose an improved probability-model-based image enhancement method for traffic sign detection. To demonstrate the efficiency of the proposed method, we compared it with other widely used image enhancement approaches in traffic sign detection. The experimental results show that our method increases the performance of object detection. In combination with the Selective Search object proposal algorithm, the average detection accuracies were 98.64% and 99.1% on the GTSDB and Swedish Traffic Signs datasets. In addition, its relatively low computational cost allows for its usage in embedded systems.
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Anandhalli, Mallikarjun, Vishwanth P. Baligar, Pavana Baligar, Pooja Deepsir, and Mithali Iti. "Vehicle detection and tracking for traffic management." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 66. http://dx.doi.org/10.11591/ijai.v10.i1.pp66-73.
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<span lang="EN-US">The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.</span>
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Zhang, Zhenghua, Jin Qian, Chongxin Fang, Guoshu Liu, and Quan Su. "Coordinated Control of Distributed Traffic Signal Based on Multiagent Cooperative Game." Wireless Communications and Mobile Computing 2021 (June 1, 2021): 1–13. http://dx.doi.org/10.1155/2021/6693636.
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In the adaptive traffic signal control (ATSC), reinforcement learning (RL) is a frontier research hotspot, combined with deep neural networks to further enhance its learning ability. The distributed multiagent RL (MARL) can avoid this kind of problem by observing some areas of each local RL in the complex plane traffic area. However, due to the limited communication capabilities between each agent, the environment becomes partially visible. This paper proposes multiagent reinforcement learning based on cooperative game (CG-MARL) to design the intersection as an agent structure. The method considers not only the communication and coordination between agents but also the game between agents. Each agent observes its own area to learn the RL strategy and value function, then concentrates the Q function from different agents through a hybrid network, and finally forms its own final Q function in the entire large-scale transportation network. The results show that the proposed method is superior to the traditional control method.
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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 that makes it possible to realize trade relations between states. It is an effective tool of foreign economic activity, without which it would be almost impossible. The efficiency of the organization international cargo transportation significantly depends on the coordinated organization of the work the border transfer station, which carries out a complete list of operations in cooperation with customs, border and other state control services. In order to increase the technology of passing international freight flows through border transfer stations, it is necessary to introduce the latest information and control systems that will reduce the duration of train processing at border transfer stations and, as a result, reduce downtime and delayed cars. Research on the development of technologies and means electronic data exchange that provide information support for international cargo transportation is promising. The electronic data exchange system must comply with the international transport infrastructure, be based on agreed technical parameters and meet the needs of compatibility of transportation technologies as a criterion for the integration of the national transport system into the world system. It is proposed to carry out preliminary declaration of goods and processing transport documents before the departure of the train to the border transfer station to reduce the technological time of processing trains. This will reduce the processing time of the transit train by 105 minutes. The main advantages of the introduction electronic document management: simplification of document management; making effective management decisions; increasing the reliability of the processed information, reducing the downtime of cars. Thus, the introduction of the system pre-declaration of goods and processing of transport documents in the processing international freight traffic will reduce material and labor costs and speed up the passage of goods through customs clearance.
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Mohamed Hatim, Shahirah, Haryani Haron, Shamsul Jamel Elias, and Nor Shahniza Kamal Bashah. "Smart optimization in 802.11p media access control protocol for vehicular ad hoc network." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 2 (April 1, 2023): 2206. http://dx.doi.org/10.11591/ijece.v13i2.pp2206-2213.
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<span lang="EN-US">The innovative idea presented in this research is that advancements in automotive networks and embedded devices can be used to assess the impact of congestion control on throughput and packet delivery ratio (PDR), or so-called multimedia content delivery. Vehicle networking and the distribution of multimedia content have become essential factors in getting packets to their intended recipients due to the availability of bandwidth. Vehicle-to-infrastructure (V2I) communication systems are crucial in vehicular ad hoc networks (VANETs), which permit vehicles to connect by distributing and delivering traffic data and transmission packet schemes. High levels of mobility and changing network topology necessitate dispersed monitoring and execution for congestion control. The amount of traffic congestion for packet transfers could be reduced by enhancing congestion management in terms of throughput and PDR percentages. In a highway setting, the Taguchi approach has been used to optimize the parameters for congestion control. Based on throughput and PDR performance measures, this technique minimizes superfluous traffic information and lowers the likelihood of network congestion. The simulation results have shown that the proposed approach performs better since it increases network performance while effectively utilizing bandwidth. The effectiveness of the suggested technique is evaluated using a typical VANETs scenario for V2I communication while driving on a highway.</span>
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Xu, Dongxin, Yueqiang Han, Xianghui Han, Ya Wang, and Guoye Wang. "Narrow Tilting Vehicle Drifting Robust Control." Machines 11, no. 1 (January 10, 2023): 90. http://dx.doi.org/10.3390/machines11010090.
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The narrow tilting vehicle receives extensive public attention because of traffic congestion and environmental pollution, and the active rolling motion control is a traffic safety precaution that reduces the rollover risk caused by the structure size of the narrow vehicle. The drifting motion control reflects the relatively updated attentive research of the regular-size vehicle, which can take full advantage of the vehicle’s dynamic performance and improve driving safety, especially when tires reach their limits. The narrow tilting vehicle drifting control is worthy of research to improve the driving safety of the narrow tilting vehicle, especially when tires reach the limit. The nonlinear narrow tilting vehicle dynamic model is established with the UniTire model to describe the vehicle motion characteristics and is simplified to reduce the computation of the drifting controller design. The narrow tilting vehicle drifting controller is designed based on the robust theory with uncertain external disturbances. The controller has a wide application, validity, and robustness and whose performance is verified by realizing different drifting motions with different initial driving motions. The narrow tilting vehicle drifting robust control has some practical and theoretical significance for more research.
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Noei, Shirin, Mohammadreza Parvizimosaed, and Mohammadreza Noei. "Longitudinal Control for Connected and Automated Vehicles in Contested Environments." Electronics 10, no. 16 (August 18, 2021): 1994. http://dx.doi.org/10.3390/electronics10161994.
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The Society of Automotive Engineers (SAE) defines six levels of driving automation, ranging from Level 0 to Level 5. Automated driving systems perform entire dynamic driving tasks for Levels 3–5 automated vehicles. Delegating dynamic driving tasks from driver to automated driving systems can eliminate crashes attributed to driver errors. Sharing status, sharing intent, seeking agreement, or sharing prescriptive information between road users and vehicles dedicated to automated driving systems can further enhance dynamic driving task performance, safety, and traffic operations. Extensive simulation is required to reduce operating costs and achieve an acceptable risk level before testing cooperative automated driving systems in laboratory environments, test tracks, or public roads. Cooperative automated driving systems can be simulated using a vehicle dynamics simulation tool (e.g., CarMaker and CarSim) or a traffic microsimulation tool (e.g., Vissim and Aimsun). Vehicle dynamics simulation tools are mainly used for verification and validation purposes on a small scale, while traffic microsimulation tools are mainly used for verification purposes on a large scale. Vehicle dynamics simulation tools can simulate longitudinal, lateral, and vertical dynamics for only a few vehicles in each scenario (e.g., up to ten vehicles in CarMaker and up to twenty vehicles in CarSim). Conventional traffic microsimulation tools can simulate vehicle-following, lane-changing, and gap-acceptance behaviors for many vehicles in each scenario without simulating vehicle powertrain. Vehicle dynamics simulation tools are more compute-intensive but more accurate than traffic microsimulation tools. Due to software architecture or computing power limitations, simplifying assumptions underlying convectional traffic microsimulation tools may have been a necessary compromise long ago. There is, therefore, a need for a simulation tool to optimize computational complexity and accuracy to simulate many vehicles in each scenario with reasonable accuracy. This research proposes a traffic microsimulation tool that employs a simplified vehicle powertrain model and a model-based fault detection method to simulate many vehicles with reasonable accuracy at each simulation time step under noise and unknown inputs. Our traffic microsimulation tool considers driver characteristics, vehicle model, grade, pavement conditions, operating mode, vehicle-to-vehicle communication vulnerabilities, and traffic conditions to estimate longitudinal control variables with reasonable accuracy at each simulation time step for many conventional vehicles, vehicles dedicated to automated driving systems, and vehicles equipped with cooperative automated driving systems. Proposed vehicle-following model and longitudinal control functions are verified for fourteen vehicle models, operating in manual, automated, and cooperative automated modes over two driving schedules under three malicious fault magnitudes on transmitted accelerations.
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AlArnaout, Zakwan, Nour Mostafa, Samer Alabed, Wael Hosny Fouad Aly, and Ahmed Shdefat. "RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks." Sensors 23, no. 1 (December 22, 2022): 102. http://dx.doi.org/10.3390/s23010102.
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In the recent past, Distributed Denial of Service (DDoS) attacks have become more abundant and present one of the most serious security threats. In a DDoS attack, the attacker controls a botnet of daemons residing in vulnerable hosts that send a significant amount of traffic to flood the victim or the network infrastructure. In this paper, a common type of DDoS attacks known as “TCP SYN-Flood” is studied. This type of attack uses spoofed Internet Protocol (IP) addresses for SYN packets by exploiting the weakness in Transmission Control Protocol (TCP) 3-Way handshake used by the TCP/IP suite of protocols, which make the web servers unreachable for legitimate users or even worse, it might lead to server crash. In this paper, a resilient, efficient, lightweight, and robust IP traceback algorithm is proposed using an IP tracing packet for each attack path. The proposed algorithm suggests that edge routers—where the attack starts from—observe the traffic pattern passing through, and if the observed traffic carries the signature of TCP SYN-Flood DDoS attack and a high percentage of it is destined to a particular web server(s), it starts the tracing process by generating an IP trace packet, which accompanies the attack path recording the routers’ IP addresses on the path between the attacker/daemon and the victim, which can extract the path and react properly upon receiving it by discarding any SYN packets originating from that attacker/daemon. To our knowledge, this is the first research that efficiently traces these kinds of attacks while they are running. The proposed solution has low computation and message overhead, efficient detection and tracing time, and converges in near optimal time. The results are validated using extensive simulation runs.
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Li, Huanping, Jian Wang, Guopeng Bai, and Xiaowei Hu. "Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads." Energies 14, no. 12 (June 10, 2021): 3425. http://dx.doi.org/10.3390/en14123425.
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In order to explore the changes that autonomous vehicles would bring to the current traffic system, we analyze the car-following behavior of different traffic scenarios based on an anti-collision theory and establish a traffic flow model with an arbitrary proportion (p) of autonomous vehicles. Using calculus and difference methods, a speed transformation model is established which could make the autonomous/human-driven vehicles maintain synchronized speed changes. Based on multi-hydrodynamic theory, a mixed traffic flow model capable of numerical calculation is established to predict the changes in traffic flow under different proportions of autonomous vehicles, then obtain the redistribution characteristics of traffic flow. Results show that the reaction time of autonomous vehicles has a decisive influence on traffic capacity; the q-k curve for mixed human/autonomous traffic remains in the region between the q-k curves for 100% human and 100% autonomous traffic; the participation of autonomous vehicles won’t bring essential changes to road traffic parameters; the speed-following transformation model minimizes the safety distance and provides a reference for the bottom program design of autonomous vehicles. In general, the research could not only optimize the stability of transportation system operation but also save road resources.
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Burdzik, Rafał, Ireneusz Celiński, and Maciej Kłaczyński. "Railway Line Occupancy Control Based on Distance Determination Sound Method." Sensors 22, no. 13 (July 2, 2022): 5003. http://dx.doi.org/10.3390/s22135003.
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The purpose of this research paper is to present the application of the developed sound method as a supporting tool to deal with railway traffic flow control. It is found that controlling railway line occupancy is the main issue associated with railway traffic flow. For this purpose, the line occupancy control based on a sound method has been developed. The concept of using sound waves as a source of information about approaching people, animals, vehicles, etc., has been known for centuries, and is due to the natural properties of the sense of hearing. There are many engineering attempts on the use of this phenomenon, which are mostly based on applications of distributed fiber-optic sensing technology. This paper presents the results of the sound pressure measurement in the immediate proximity of the rail to analyze and evaluate the use of the acoustic wave as an information carrier on approaching rail vehicles. The purpose of this research is to discuss the sound method introduced here, and apply it in different circumstances.
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Munigety, Caleb Ronald. "Motion planning methods for autonomous vehicles in disordered traffic systems: a comparative analysis and future research directions." International Journal of Vehicle Autonomous Systems 15, no. 2 (2020): 152. http://dx.doi.org/10.1504/ijvas.2020.10030411.
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Munigety, Caleb Ronald. "Motion planning methods for autonomous vehicles in disordered traffic systems: a comparative analysis and future research directions." International Journal of Vehicle Autonomous Systems 15, no. 2 (2020): 152. http://dx.doi.org/10.1504/ijvas.2020.108435.
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Jasim, Mahdi Nsaif, and Methaq Talib Gaata. "K-Means clustering-based semi-supervised for DDoS attacks classification." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3570–76. http://dx.doi.org/10.11591/eei.v11i6.4353.
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Network attacks of the distributed denial of service (DDoS) form are used to disrupt server replies and services. It is popular because it is easy to set up and challenging to detect. We can identify DDoS attacks on network traffic in a variety of ways. However, the most effective methods for detecting and identifying a DDoS attack are machine learning approaches. This attack is considered to be among the most dangerous internet threats. In order for supervised machine learning algorithms to function, there needs to be tagged network traffic data sets. On the other hand, an unsupervised method uses network traffic analysis to find assaults. In this research, the K-Means clustering algorithm was developed as a semi-supervised approach for DDoS classification. The proposed algorithm is trained and tested with the CICIDS2017 dataset. After using the proposed hybrid feature selection methods and applying multiple training, testing, and carefully sorting DDoS traffic through a series of experiments, the optimum 2 centroids were found to be DDoS and normal. The generated centroids can be used to classify network traffic. So the proposed method succeeded to cluster the network traffic to safe and theat.
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Alshayeb, Suhaib, Aleksandar Stevanovic, Nikola Mitrovic, and Elio Espino. "Traffic Signal Optimization to Improve Sustainability: A Literature Review." Energies 15, no. 22 (November 11, 2022): 8452. http://dx.doi.org/10.3390/en15228452.
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Optimizing traffic signals to improve traffic progression relies on minimizing mobility performance measures (e.g., delays and stops). However, delay and stop minimizations do not necessarily lead to minimal sustainability measures (e.g., fuel consumption and emissions). For that reason, researchers have focused, for decades, on integrating traffic models, signal optimization models, and fuel consumption and emissions models to minimize sustainability metrics while keeping acceptable levels of mobility metrics. Therefore, this paper reviews, classifies, and analyzes studies found in the literature regarding optimizing sustainable traffic signals. This paper provides researchers with a good starting point to further develop solutions which can address sustainable traffic control. To achieve that, this study details the most notable sustainable signal timing optimization studies from six perspectives: traffic models, fuel consumption and emissions models, optimization methods, objective functions, operating conditions, and reported sustainability savings. Outcomes of this research show that the previous studies deployed many combinations of elements from the six-perspective mentioned above, leading to a wide range of fuel consumption and emissions savings. The study also concludes that the available fuel consumption and emissions models are relatively old. Hence, future research is needed to develop new fuel consumption and emissions models based on recently collected data.
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Czekaj, Maciej, Ernest Jamro, and Kazimierz Wiatr. "Estimating the Memory Consumption of a Hardware IP Defragmentation Block." Electronics 10, no. 16 (August 20, 2021): 2015. http://dx.doi.org/10.3390/electronics10162015.
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IP fragmentation is still prevalent on the Internet. Defragmented traffic is a prerequisite for many network processing algorithms. This work focuses on the size and organization of a flow table, which is an essential ingredient of the hardware IP defragmentation block. Previous research suggests that fragmented IP traffic is highly local, and a relatively small flow table (on the order of a thousand entries) can process most of the traffic. Samples of IP traffic were obtained from public data sources and used for a statistical analysis, revealing the key factors in achieving design goals. The findings were backed by an extensive design space exploration of the software defragmentation model, which resulted in the efficiency estimates. To provide a robust score of the simulation model, a new validation technique is employed that helps to overcome the limitations of the samples.
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Kherraki, Amine, and Rajae El Ouazzani. "Deep convolutional neural networks architecture for an efficient emergency vehicle classification in real-time traffic monitoring." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 1 (March 1, 2022): 110. http://dx.doi.org/10.11591/ijai.v11.i1.pp110-120.
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Nowadays, intelligent transportation system (ITS) has become one of the most popular subjects of scientific research. ITS provides innovative services to traffic monitoring. The classification of emergency vehicles in traffic surveillance cameras provides an early warning to ensure a rapid reaction in emergency events. Computer vision technology, including deep learning, has many advantages for traffic monitoring. For instance, convolutional neural network (CNN) has given very good results and optimal performance in computer vision tasks, such as the classification of vehicles according to their types, and brands. In this paper, we will classify emergency vehicles from the output of a closed-circuit television (CCTV) camera. Among the advantages of this research paper is providing detailed information on the emergency vehicle classification topic. Emergency vehicles have the highest priority on the road and finding the best emergency vehicle classification model in realtime will undoubtedly save lives. Thus, we have used eight CNN architectures and compared their performances on the Analytics Vidhya Emergency Vehicle dataset. The experiments show that the utilization of DenseNet121 gives excellent classification results which makes it the most suitable architecture for this research topic, besides, DenseNet121 does not require a high memory size which makes it appropriate for real-time applications.<p> </p>
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Siswaya, Siswaya, Sunardi Sunardi, and Anton Yudhana. "Analisis Sistem Traffic Light Untuk Optimalisasi dan Antisipasi Kemacetan Lalu Lintas Berbasis Android." Respati 16, no. 3 (November 10, 2021): 86. http://dx.doi.org/10.35842/jtir.v16i3.423.
Full textAbstract:
INTISARI Sistem pengatur lampu lalu lintas banyak memanfaatkan pengatur waktu tetap yang sudah diprogram terlebih dahulu, akibatnya polisi lalu lintas tidak bisa intervensi saat kondisi macet. Kemajuan bidang elektronika memberikan harapan baru pada pengendalian lampu lalu lintas yang lebih efektif dan efisian. Gabungan Integrated Circuit (IC) dengan berbagai jenis perangkat lunak melahirkan sistem pengatur lalu lintas yang cerdas. Peningkatan kemampuan dan kehandalan dapat dilakukan dengan mengaplikasikan sejumlah sensor deteksi objek dan aplikasi android pada smartphone. Tujuan utama penelitian ini adalah menghasilkan inovasi pengatur lampu lalu lintas yang lebih akurat sehingga bisa mengatasi kemacetan lalu lintas dan dalam kondisi darurat.Pengaturan lalu lintas dilakukan dengan memasang beberapa sensor terhadap kendaraan yang lewat, setiap ruas jalan dipasang sensor untuk antrian kendaraan pendek (sensor jarak dekat/SD) dan antrian kendaraan panjang (sensor jarak jauh/SJ), sehingga lamanya lampu hijau menyala salah satunya ditentukan oleh kedua sensor tersebut, selain waktu standar yang telah dimasukkan dalam programnya. Perangkat pendukung lainnya antara lain bluetooth, chip mikrokontroler arduino, dan aplikasi yang terinstal pada smartphone sebagai kontrol dalam kondisi darurat. Dalam kondisi normal pengatur lalu lintas akan menggunakan program yang tertanam dalam chip mikrokontroler, tapi dalam kondisi lalu lintas padat maka sejumlah sensor akan berperan memberikan masukan ke chip untuk mengatur lama lampu hijau menyala, sehingga lalu lintas lancar. Dalam kondisi darurat ada kendaraan ambulan atau lainnya yang membutuhkan prioritas maka memanfaatkan aplikasi android pada smartphone.Kata kunci— Pengatur lalu lintas, sensor deteksi objek, Integrated Circuit (IC), mikrokontroler, android . ABSTRACT The traffic light control system uses a lot of fixed timers that have been programmed in advance, as a result the traffic police cannot intervene in traffic jams. Advances in electronics provide new hope for more effective and efficient traffic light control. The combination of Integrated Circuit (IC) with various types of software gives birth to an intelligent traffic control system. Improved capability and reliability can be done by applying a number of object detection sensors and android applications on smartphones. The main objective of this research is to produce an innovation of a more accurate traffic light regulator so that it can overcome traffic jams and in emergency conditions.Traffic regulation is done by installing several sensors on passing vehicles, each road section is installed with sensors for short vehicle queues (close proximity sensors/SD) and long vehicle queues (remote sensors/SJ), so that the length of time the green light is on is one of them determined by the two sensors, in addition to the standard time that has been included in the program. Other supporting devices include bluetooth, arduino microcontroller chips, and applications installed on smartphones as controls in emergency conditions. Under normal conditions the traffic controller will use a program embedded in the microcontroller chip, but in heavy traffic conditions a number of sensors will play a role in providing input to the chip to regulate the length of time the green light is on, so that traffic flows smoothly. In an emergency, there is an ambulance or other vehicle that requires priority, then use the android application on a smartphone.Keywords — Traffic controller, object detection sensor, Integrated Circuit (IC), microcontroller, android.