期刊文献+
共找到64,310篇文章
< 1 2 250 >
每页显示 20 50 100
MULTI-CLASS TRAFFIC QOS ROUTING FOR LEO SATELLITE NETWORKS 被引量:3
1
作者 蒋文娟 宗鹏 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2012年第3期254-262,共9页
Due to the diversified demands of quality of service(QoS) in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit(LEO) satellite networks. A novel QoS sat... Due to the diversified demands of quality of service(QoS) in volume multimedia application, QoS routings for multiservice are becoming a research hotspot in low earth orbit(LEO) satellite networks. A novel QoS satellite routing algorithm for multi-class traffic is proposed. The goal of the routing algorithm is to provide the distinct QoS for different traffic classes and improve the utilization of network resources. Traffic is classified into three classes and allocated priorities based on their QoS requirements, respectively. A priority queuing mechanism guarantees the algorithm to work better for high-priority classes. In order to control the congestion, a blocking probability analysis model is built up based on the Markov process theory. Finally, according to the classification link-cost metrics, routings for different classes are calculated with the distinct QoS requirments and the status of network resource. Simulations verify the performance of the routing algorithm at different time and in different regions, and results demonstrate that the algorithm has great advantages in terms of the average delay and the blocking probability. Meanwhile, the robustness issue is also discussed. 展开更多
关键词 low earth orbit satellite networks traffic classification routing algorithm quality of service traffic and topology model
在线阅读 下载PDF
DESCRIPTION AND WENO NUMERICAL APPROXIMATION TO NONLINEAR WAVES OF A MULTI-CLASS TRAFFIC FLOW LWR MODEL
2
作者 张鹏 戴世强 刘儒勋 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2005年第6期691-699,共9页
A strict proof of the hyperbolicity of the multi-class LWR ( Lighthill-Whitham-Richards) traffic flow model, as well as the descriptions on those nonlinear waves characterized in the traffic flow problems were given. ... A strict proof of the hyperbolicity of the multi-class LWR ( Lighthill-Whitham-Richards) traffic flow model, as well as the descriptions on those nonlinear waves characterized in the traffic flow problems were given. They were mainly about the monotonicity of densities across shocks and in rarefactions. As the system had no characteristic decomposition explicitly, a high resolution and higher order accuracy WENO( weighted essentially non-oscillatory) scheme was introduced to the numerical simulation, which coincides with the analytical description. 展开更多
关键词 HYPERBOLICITY characteristic traffic wave WENO scheme
在线阅读 下载PDF
Recurrent MAPPO for Joint UAV Trajectory and Traffic Offloading in Space-Air-Ground Integrated Networks
3
作者 Zheyuan Jia Fenglin Jin +1 位作者 Jun Xie Yuan He 《Computers, Materials & Continua》 2026年第1期447-461,共15页
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g... This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs. 展开更多
关键词 Space-air-ground integrated networks UAV traffic offloading reinforcement learning
在线阅读 下载PDF
YOLO-SDW: Traffic Sign Detection Algorithm Based on YOLOv8s Skip Connection and Dynamic Convolution
4
作者 Qing Guo Juwei Zhang Bingyi Ren 《Computers, Materials & Continua》 2026年第1期1433-1452,共20页
Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakt... Traffic sign detection is an important part of autonomous driving,and its recognition accuracy and speed are directly related to road traffic safety.Although convolutional neural networks(CNNs)have made certain breakthroughs in this field,in the face of complex scenes,such as image blur and target occlusion,the traffic sign detection continues to exhibit limited accuracy,accompanied by false positives and missed detections.To address the above problems,a traffic sign detection algorithm,You Only Look Once-based Skip Dynamic Way(YOLO-SDW)based on You Only Look Once version 8 small(YOLOv8s),is proposed.Firstly,a Skip Connection Reconstruction(SCR)module is introduced to efficiently integrate fine-grained feature information and enhance the detection accuracy of the algorithm in complex scenes.Secondly,a C2f module based on Dynamic Snake Convolution(C2f-DySnake)is proposed to dynamically adjust the receptive field information,improve the algorithm’s feature extraction ability for blurred or occluded targets,and reduce the occurrence of false detections and missed detections.Finally,the Wise Powerful IoU v2(WPIoUv2)loss function is proposed to further improve the detection accuracy of the algorithm.Experimental results show that the average precision mAP@0.5 of YOLO-SDW on the TT100K dataset is 89.2%,and mAP@0.5:0.95 is 68.5%,which is 4%and 3.3%higher than the YOLOv8s baseline,respectively.YOLO-SDW ensures real-time performance while having higher accuracy. 展开更多
关键词 traffic sign detection YOLOv8 object detection deep learning
在线阅读 下载PDF
Impact of Data Processing Techniques on AI Models for Attack-Based Imbalanced and Encrypted Traffic within IoT Environments
5
作者 Yeasul Kim Chaeeun Won Hwankuk Kim 《Computers, Materials & Continua》 2026年第1期247-274,共28页
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp... With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy. 展开更多
关键词 Encrypted traffic attack detection data sampling technique AI-based detection IoT environment
在线阅读 下载PDF
Interactive Dynamic Graph Convolution with Temporal Attention for Traffic Flow Forecasting
6
作者 Zitong Zhao Zixuan Zhang Zhenxing Niu 《Computers, Materials & Continua》 2026年第1期1049-1064,共16页
Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating In... Reliable traffic flow prediction is crucial for mitigating urban congestion.This paper proposes Attentionbased spatiotemporal Interactive Dynamic Graph Convolutional Network(AIDGCN),a novel architecture integrating Interactive Dynamic Graph Convolution Network(IDGCN)with Temporal Multi-Head Trend-Aware Attention.Its core innovation lies in IDGCN,which uniquely splits sequences into symmetric intervals for interactive feature sharing via dynamic graphs,and a novel attention mechanism incorporating convolutional operations to capture essential local traffic trends—addressing a critical gap in standard attention for continuous data.For 15-and 60-min forecasting on METR-LA,AIDGCN achieves MAEs of 0.75%and 0.39%,and RMSEs of 1.32%and 0.14%,respectively.In the 60-min long-term forecasting of the PEMS-BAY dataset,the AIDGCN out-performs the MRA-BGCN method by 6.28%,4.93%,and 7.17%in terms of MAE,RMSE,and MAPE,respectively.Experimental results demonstrate the superiority of our pro-posed model over state-of-the-art methods. 展开更多
关键词 traffic flow prediction interactive dynamic graph convolution graph convolution temporal multi-head trend-aware attention self-attention mechanism
在线阅读 下载PDF
Towards Decentralized IoT Security: Optimized Detection of Zero-Day Multi-Class Cyber-Attacks Using Deep Federated Learning
7
作者 Misbah Anwer Ghufran Ahmed +3 位作者 Maha Abdelhaq Raed Alsaqour Shahid Hussain Adnan Akhunzada 《Computers, Materials & Continua》 2026年第1期744-758,共15页
The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an... The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security. 展开更多
关键词 Cyber-attack intrusion detection system(IDS) deep federated learning(DFL) zero-day attack distributed denial of services(DDoS) multi-class Internet of Things(IoT)
在线阅读 下载PDF
A Hybrid Deep Learning Multi-Class Classification Model for Alzheimer’s Disease Using Enhanced MRI Images
8
作者 Ghadah Naif Alwakid 《Computers, Materials & Continua》 2026年第1期797-821,共25页
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru... Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice. 展开更多
关键词 Alzheimer’s disease deep learning MRI images MobileNetV2 contrast-limited adaptive histogram equalization(CLAHE) enhanced super-resolution generative adversarial networks(ESRGAN) multi-class classification
在线阅读 下载PDF
LWDG Method for a Multi-Class Traffic Flow Model on an Inhomogeneous Highway
9
作者 Tiantian Sun Jianxian Qiu 《Advances in Applied Mathematics and Mechanics》 SCIE 2009年第3期438-450,共13页
In this paper,we apply the discontinuous Galerkin method with LaxWendroff type time discretizations(LWDG)using the weighted essentially nonoscillatory(WENO)limiter to solve a multi-class traffic flow model for an inho... In this paper,we apply the discontinuous Galerkin method with LaxWendroff type time discretizations(LWDG)using the weighted essentially nonoscillatory(WENO)limiter to solve a multi-class traffic flow model for an inhomogeneous highway.This model is a kind of hyperbolic conservation law with spatially varying fluxes.The numerical scheme is based on a modified equivalent system which is written as a“standard”hyperbolic conservation form.Numerical experiments for both the Riemann problem and the traffic signal control problem are presented to show the effectiveness of the method. 展开更多
关键词 multi-class traffic flow inhomogeneous highway spatially varying flux nonstrictly hyperbolic conservation laws LWDG method WENO limiter
在线阅读 下载PDF
A relaxation scheme for a multi-class Lighthill-Whitham-Richards traffic flow model 被引量:6
10
作者 Jian-zhong CHEN Zhong-ke SHI Yan-mei HU 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2009年第12期1835-1844,共10页
We present a high-resolution relaxation scheme for a multi-class Lighthill-Whitham-Richards (MCLWR) traffic flow model. This scheme is based on high-order reconstruction for spatial discretization and an implicit-expl... We present a high-resolution relaxation scheme for a multi-class Lighthill-Whitham-Richards (MCLWR) traffic flow model. This scheme is based on high-order reconstruction for spatial discretization and an implicit-explicit Runge-Kutta method for time integration. The resulting method retains the simplicity of the relaxation schemes. There is no need to involve Riemann solvers and characteristic decomposition. Even the computation of the eigenvalues is not required. This makes the scheme particularly well suited for the MCLWR model in which the analytical expressions of the eigenvalues are difficult to obtain for more than four classes of road users. The numerical results illustrate the effectiveness of the presented method. 展开更多
关键词 Relaxation scheme multi-class LWR model traffic flow CWENO reconstruction Implicit-explicit Runge-Kutta
原文传递
Numerical solutions of a multi-class traffic flow model on an inhomogeneous highway using a high-resolution relaxed scheme 被引量:3
11
作者 Jian-zhong CHEN Zhong-ke SHI Yan-mei HU 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2012年第1期29-36,共8页
A high-resolution relaxed scheme which requires little information of the eigenstructure is presented for the multiclass Lighthill-Whitham-Richards (LWR) model on an inhomogeneous highway. The scheme needs only an e... A high-resolution relaxed scheme which requires little information of the eigenstructure is presented for the multiclass Lighthill-Whitham-Richards (LWR) model on an inhomogeneous highway. The scheme needs only an estimate of the upper boundary of the maximum of absolute eigenvalues. It is based on incorporating an improved fifth-order weighted essentially non-oscillatory (WENO) reconstruction with relaxation approximation. The scheme benefits from the simplicity of relaxed schemes in that it requires no exact or approximate Riemann solvers and no projection along characteristic directions. The effectiveness of our method is demonstrated in several numerical examples. 展开更多
关键词 Relaxed scheme multi-class model Weighted essentially non-oscillatory (WENO) reconstruction
原文传递
Predicting Causes of Traffic Road Accidents Using Multi-class Support Vector Machines
12
作者 Elfadil A. Mohamed 《通讯和计算机(中英文版)》 2014年第5期441-447,共7页
关键词 道路交通事故 支持向量机 原因 预测 阿拉伯联合酋长国 多级 数据挖掘技术 肇事车辆
在线阅读 下载PDF
Modelling of Daily Long-Term Urban Road Traffic Flow Distribution: A Poisson Process Approach 被引量:1
13
作者 Jojo D. Lartey 《Open Journal of Modelling and Simulation》 2025年第1期89-105,共17页
Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel... Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management. 展开更多
关键词 Poisson Process Macroscopic traffic Flow Urban Road Long-Term Forecast Multiple Entries-Exits Dynamics
在线阅读 下载PDF
CMBA-FL: Communication-mitigated and blockchain-assisted federated learning for traffic flow predictions 被引量:1
14
作者 Kaiyin Zhu Mingming Lu +2 位作者 Haifeng Li Neal NXiong Wenyong He 《Digital Communications and Networks》 2025年第3期724-733,共10页
As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods fa... As an effective strategy to address urban traffic congestion,traffic flow prediction has gained attention from Federated-Learning(FL)researchers due FL’s ability to preserving data privacy.However,existing methods face challenges:some are too simplistic to capture complex traffic patterns effectively,and others are overly complex,leading to excessive communication overhead between cloud and edge devices.Moreover,the problem of single point failure limits their robustness and reliability in real-world applications.To tackle these challenges,this paper proposes a new method,CMBA-FL,a Communication-Mitigated and Blockchain-Assisted Federated Learning model.First,CMBA-FL improves the client model’s ability to capture temporal traffic patterns by employing the Encoder-Decoder framework for each edge device.Second,to reduce the communication overhead during federated learning,we introduce a verification method based on parameter update consistency,avoiding unnecessary parameter updates.Third,to mitigate the risk of a single point of failure,we integrate consensus mechanisms from blockchain technology.To validate the effectiveness of CMBA-FL,we assess its performance on two widely used traffic datasets.Our experimental results show that CMBA-FL reduces prediction error by 11.46%,significantly lowers communication overhead,and improves security. 展开更多
关键词 Blockchain Communication mitigating Federated learning traffic flow prediction
在线阅读 下载PDF
A Diffusion Model for Traffic Data Imputation 被引量:1
15
作者 Bo Lu Qinghai Miao +5 位作者 Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang 《IEEE/CAA Journal of Automatica Sinica》 2025年第3期606-617,共12页
Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has prov... Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS)in the real world.As a state-of-the-art generative model,the diffusion model has proven highly successful in image generation,speech generation,time series modelling etc.and now opens a new avenue for traffic data imputation.In this paper,we propose a conditional diffusion model,called the implicit-explicit diffusion model,for traffic data imputation.This model exploits both the implicit and explicit feature of the data simultaneously.More specifically,we design two types of feature extraction modules,one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series.This approach not only inherits the advantages of the diffusion model for estimating missing data,but also takes into account the multiscale correlation inherent in traffic data.To illustrate the performance of the model,extensive experiments are conducted on three real-world time series datasets using different missing rates.The experimental results demonstrate that the model improves imputation accuracy and generalization capability. 展开更多
关键词 Data imputation diffusion model implicit feature time series traffic data
在线阅读 下载PDF
TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional Networks 被引量:1
16
作者 Baoquan Liu Xi Chen +2 位作者 Qingjun Yuan Degang Li Chunxiang Gu 《Computers, Materials & Continua》 2025年第2期3179-3201,共23页
With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based... With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%. 展开更多
关键词 Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks
在线阅读 下载PDF
A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation 被引量:1
17
作者 Hamza Murad Khan Anwar Khan +3 位作者 Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani 《Computers, Materials & Continua》 2025年第5期3369-3388,共20页
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models... Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes. 展开更多
关键词 Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model
在线阅读 下载PDF
YOLO-based lightweight traffic sign detection algorithm and mobile deployment 被引量:1
18
作者 WU Yaqin ZHANG Tao +2 位作者 NIU Jianjun CHANG Yan LIU Ganjun 《Optoelectronics Letters》 2025年第4期249-256,共8页
This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolu... This paper proposes a lightweight traffic sign detection system based on you only look once(YOLO).Firstly,the classification to fusion(C2f)structure is integrated into the backbone network,employing deformable convolution and bi-directional feature pyramid network(BiFPN)_Concat to improve the adaptability of the network.Secondly,the simple attention module(SimAm)is embedded to prioritize key features and reduce the complexity of the model after the C2f layer at the end of the backbone network.Next,the focal efficient intersection over union(EloU)is introduced to adjust the weights of challenging samples.Finally,we accomplish the design and deployment for the mobile app.The results demonstrate improvements,with the F1 score of 0.8987,mean average precision(mAP)@0.5 of 98.8%,mAP@0.5:0.95 of 75.6%,and the detection speed of 50 frames per second(FPS). 展开更多
关键词 c f layer simple attention module simam reduce complexity traffic sign detection prioritize key features backbone networkemploying classification backbone networknextthe
原文传递
Learning-Based Delay Sensitive and Reliable Traffic Adaptation for DC-PLC and 5G Integrated Multi-Mode Heterogeneous Networks
19
作者 Tian Gexing Wang Ruiqiuyu +6 位作者 Pan Chao Zhou Zhenyu Yang Junzhong Zhao Chenkai Chen Bei Yang Sen Shahid Mumtaz 《China Communications》 2025年第4期65-80,共16页
Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power li... Low-carbon smart parks achieve selfbalanced carbon emission and absorption through the cooperative scheduling of direct current(DC)-based distributed photovoltaic,energy storage units,and loads.Direct current power line communication(DC-PLC)enables real-time data transmission on DC power lines.With traffic adaptation,DC-PLC can be integrated with other complementary media such as 5G to reduce transmission delay and improve reliability.However,traffic adaptation for DC-PLC and 5G integration still faces the challenges such as coupling between traffic admission control and traffic partition,dimensionality curse,and the ignorance of extreme event occurrence.To address these challenges,we propose a deep reinforcement learning(DRL)-based delay sensitive and reliable traffic adaptation algorithm(DSRTA)to minimize the total queuing delay under the constraints of traffic admission control,queuing delay,and extreme events occurrence probability.DSRTA jointly optimizes traffic admission control and traffic partition,and enables learning-based intelligent traffic adaptation.The long-term constraints are incorporated into both state and bound of drift-pluspenalty to achieve delay awareness and enforce reliability guarantee.Simulation results show that DSRTA has lower queuing delay and more reliable quality of service(QoS)guarantee than other state-of-the-art algorithms. 展开更多
关键词 DC-PLC and 5G integration multi-mode heterogeneous networks traffic adaptation traffic admission control traffic partition
在线阅读 下载PDF
The Evolution of Traffic Lights:A Comprehensive Analysis of Traffic Management Systems in Shanghai
20
作者 Zhichen Eden Guo 《Journal of Electronic Research and Application》 2025年第1期330-336,共7页
This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context... This paper comprehensively analyzes the evolution of traffic light systems in Shanghai,highlighting the technological advancements and their impact on traffic management and safety.Starting from the historical context of the first traffic light in London in 1868 to the modern automated systems,the study explores the complexity and adaptability of traffic lights in Shanghai.Through field surveys and interviews with traffic engineers,the paper debunks common misconceptions about traffic light operation,revealing a sophisticated network that responds to real-time traffic dynamics using software like the Sydney Coordinated Adaptive Traffic System(SCATS)6.The study also discusses the importance of pedestrian safety,suggesting future enhancements such as Global Positioning System(GPS)based emergency systems and accommodations for color-blind individuals.The paper further delves into the potential of Artificial Intelligence(AI)and Vehicle-to-Infrastructure(V21)technology in revolutionizing traffic light systems,emphasizing their role in improving traffic flow and safety.The findings underscore Shanghai’s progressive approach to traffic management,showcasing the city’s commitment to optimizing traffic control solutions for the benefit of both vehicles and pedestrians. 展开更多
关键词 traffic management traffic light traffic network Smart city V2I(vehicle-to-infrastructure)
在线阅读 下载PDF
上一页 1 2 250 下一页 到第
使用帮助 返回顶部