Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of ...Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.展开更多
The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules...The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).展开更多
Hiking trails are a basic type of tourist infrastructure,which,on the one hand,make areas available for tourist traffic,and on the other hand,can contribute to the protection of the natural environment(if they are wel...Hiking trails are a basic type of tourist infrastructure,which,on the one hand,make areas available for tourist traffic,and on the other hand,can contribute to the protection of the natural environment(if they are well designed and maintained).Owing to the variety of performed functions,their designation is determined by several factors:natural,technical,economic,social.Networks of trails change constantly.The aim of this article is to determine exactly what factors influence transformations within the hiking trail networks and what is their significance.To this end,three study areas in the Sudetes were analysed:one on the Polish side of the Sudetes-the Table(Stołowe)Mountains,and two on the Czech side-the rock town near the village of Sloup vČechách and the central part of Zlatohorskávrchovina.An analysis of changes in the shape of the networks over time was carried out,as well as surveys of institutions that were responsible for or influenced these transformations.These areas are characterised by a significant level of changes in the trail network.Among the factors influencing these changes,the tourist attractiveness of the area,the resilience of the environment,the intensity of tourism traffic,the environmental transformations associated with it,the history of tourism development and land ownership changes should be considered the most important.At each stage of forming networks,the key factor should be tourists’needs,including the desire to escape the urbanised environment.For this reason,trails should avoid roads with artificial(hard)surfaces and heavy automobile traffic.展开更多
Mature osteoclasts degrade bone matrix by exocytosis of active proteases from secretory lysosomes through a ruffled border.However,the molecular mechanisms underlying lysosomal trafficking and secretion in osteoclasts...Mature osteoclasts degrade bone matrix by exocytosis of active proteases from secretory lysosomes through a ruffled border.However,the molecular mechanisms underlying lysosomal trafficking and secretion in osteoclasts remain largely unknown.Here,we show with GeneChip analysis that RUN and FYVE domain-containing protein 4(RUFY4)is strongly upregulated during osteoclastogenesis.Mice lacking Rufy4 exhibited a high trabecular bone mass phenotype with abnormalities in osteoclast function in vivo.Furthermore,deleting Rufy4 did not affect osteoclast differentiation,but inhibited bone-resorbing activity due to disruption in the acidic maturation of secondary lysosomes,their trafficking to the membrane,and their secretion of cathepsin K into the extracellular space.Mechanistically,RUFY4 promotes late endosome-lysosome fusion by acting as an adaptor protein between Rab7 on late endosomes and LAMP2 on primary lysosomes.Consequently,Rufy4-deficient mice were highly protected from lipopolysaccharide-and ovariectomy-induced bone loss.Thus,RUFY4 plays as a new regulator in osteoclast activity by mediating endo-lysosomal trafficking and have a potential to be specific target for therapies against bone-loss diseases such as osteoporosis.展开更多
A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow acc...A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.展开更多
As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the clas...As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the classic instantaneous traffic emission model and the limited deceleration capacity microscopic traffic flow model with slow-to-start rules, this paper has investigated the impact of speed humps on traffic flow and the instantaneous emissions of vehicle pollutants in a single lane situation. The numerical simulation results have shown that speed humps have significant effects on traffic flow and traffic emissions. In a free-flow region, the increase of speed humps leads to the continuous rise of CO_(2), NO_(X) and PM emissions. Within some density ranges, one finds that these pollutant emissions can evolve into some higher values under some random seeds. Under other random seeds, they can evolve into some lower values. In a wide moving jam region, the emission values of these pollutants sometimes appear as continuous or intermittent phenomenon. Compared to the refined Na Sch model, the present model has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher volatile organic components(VOC) emissions. Compared to the limited deceleration capacity model without slow-to-start rules, the present model also has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher VOC emissions in a wide moving jam region. These results can also be confirmed or explained by the statistical values of vehicle velocity and acceleration.展开更多
With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)wi...With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.展开更多
In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be sev...In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.展开更多
To investigate the influence mechanism of geostress on rockburst characteristics,three groups of gneiss rockburst experiments were conducted under different initial geostress conditions.A high-speed photography system...To investigate the influence mechanism of geostress on rockburst characteristics,three groups of gneiss rockburst experiments were conducted under different initial geostress conditions.A high-speed photography system and acoustic emission(AE)monitoring system were used to monitor the entire rockburst process in real time.The experimental results show that when the initial burial depth increases from 928 m to 1320 m,the proportion of large fracture scale in rockburst increases by 154.54%,and the AE energy increases by 565.63%,reflecting that the degree and severity of rockburst increase with the increase of burial depth.And then,two mechanisms are proposed to explain this effect,including(i)the increase of initial geostress improves the energy storage capacity of gneiss,and then,the excess energy which can be converted into kinetic energy of debris ejection increases,consequently,a more pronounced violent ejection phenomenon is observed at rockburst;(ii)the increase of initial geostress causes more sufficient plate cracks of gneiss after unloading ofσh,which provides a basis for more severe ejection of rockburst.What’s more,a precursor with clear physical meaning for rockburst is proposed under the framework of dynamic response process of crack evolution.Finally,potential value in long term rockburst warning of the precursor obtained in this study is shown via the comparison of conventional precursor.展开更多
Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Ne...Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.展开更多
Speed limit measures are ubiquitous due to the complexity of the road environment,which can be supplied with the help of vehicle to everything(V2X)communication technology.Therefore,the influence of speed limit on tra...Speed limit measures are ubiquitous due to the complexity of the road environment,which can be supplied with the help of vehicle to everything(V2X)communication technology.Therefore,the influence of speed limit on traffic system will be investigated to construct a two-lane lattice model accounting for the speed limit effect during the lane change process under V2X environment.Accordingly,the stability condition and the mKdV equation are closely associated with the speed limit effect through theory analysis.Moreover,the evolution of density and hysteresis loop is simulated to demonstrate the positive role of the speed limit effect on traffic stability in the cases of strong reaction intensity and high limited speed.展开更多
In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly d...In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific nodes.This study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node information.The proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet metadata.This makes it well-suited for direct application in IoT environments due to its low resource consumption.In experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based features.Additionally,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and OneClassSVM.Furthermore,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling methods.This study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.展开更多
The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience ...The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.展开更多
Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produce...Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.展开更多
The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industria...The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.展开更多
Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledgin...Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.展开更多
Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on de...Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.展开更多
With the advancement of wireless network technology,vast amounts of traffic have been generated,and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated.While signatu...With the advancement of wireless network technology,vast amounts of traffic have been generated,and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated.While signature-based detection methods,static analysis,and dynamic analysis techniques have been previously explored for malicious traffic detection,they have limitations in identifying diversified malware traffic patterns.Recent research has been focused on the application of machine learning to detect these patterns.However,applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process.In this study,we examined methods for effectively utilizing machine learning-based malicious traffic detection approaches for lightweight devices.We introduced the suboptimal feature selection model(SFSM),a feature selection technique designed to reduce complexity while maintaining the effectiveness of malicious traffic detection.Detection performance was evaluated on various malicious traffic,benign,exploits,and generic,using the UNSW-NB15 dataset and SFSM sub-optimized hyperparameters for feature selection and narrowed the search scope to encompass all features.SFSM improved learning performance while minimizing complexity by considering feature selection and exhaustive search as two steps,a problem not considered in conventional models.Our experimental results showed that the detection accuracy was improved by approximately 20%compared to the random model,and the reduction in accuracy compared to the greedy model,which performs an exhaustive search on all features,was kept within 6%.Additionally,latency and complexity were reduced by approximately 96%and 99.78%,respectively,compared to the greedy model.This study demonstrates that malicious traffic can be effectively detected even in lightweight device environments.SFSM verified the possibility of detecting various attack traffic on lightweight devices.展开更多
Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled...Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.展开更多
Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportati...Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.展开更多
基金supported by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation“Research and System Development of Highway Asset Digitalization Technology inUse Based onHigh-PrecisionMap”(Project Number:202203)in part by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation:Research and Demonstration Application of Key Technologies for Precise Sensing of Expressway Thrown Objects(No.202204).
文摘Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.
文摘The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).
文摘Hiking trails are a basic type of tourist infrastructure,which,on the one hand,make areas available for tourist traffic,and on the other hand,can contribute to the protection of the natural environment(if they are well designed and maintained).Owing to the variety of performed functions,their designation is determined by several factors:natural,technical,economic,social.Networks of trails change constantly.The aim of this article is to determine exactly what factors influence transformations within the hiking trail networks and what is their significance.To this end,three study areas in the Sudetes were analysed:one on the Polish side of the Sudetes-the Table(Stołowe)Mountains,and two on the Czech side-the rock town near the village of Sloup vČechách and the central part of Zlatohorskávrchovina.An analysis of changes in the shape of the networks over time was carried out,as well as surveys of institutions that were responsible for or influenced these transformations.These areas are characterised by a significant level of changes in the trail network.Among the factors influencing these changes,the tourist attractiveness of the area,the resilience of the environment,the intensity of tourism traffic,the environmental transformations associated with it,the history of tourism development and land ownership changes should be considered the most important.At each stage of forming networks,the key factor should be tourists’needs,including the desire to escape the urbanised environment.For this reason,trails should avoid roads with artificial(hard)surfaces and heavy automobile traffic.
基金supported by grants from the National Research Foundation of Korea(RS-2023-00217798 and 2021R1A2C3003675 to S.Y.L.)by the Korea Basic Science Institute National Research Facilities&Equipment Center grant(2019R1A6C1010020).M.K.was supported in part by scholarship from Ewha Womans University.
文摘Mature osteoclasts degrade bone matrix by exocytosis of active proteases from secretory lysosomes through a ruffled border.However,the molecular mechanisms underlying lysosomal trafficking and secretion in osteoclasts remain largely unknown.Here,we show with GeneChip analysis that RUN and FYVE domain-containing protein 4(RUFY4)is strongly upregulated during osteoclastogenesis.Mice lacking Rufy4 exhibited a high trabecular bone mass phenotype with abnormalities in osteoclast function in vivo.Furthermore,deleting Rufy4 did not affect osteoclast differentiation,but inhibited bone-resorbing activity due to disruption in the acidic maturation of secondary lysosomes,their trafficking to the membrane,and their secretion of cathepsin K into the extracellular space.Mechanistically,RUFY4 promotes late endosome-lysosome fusion by acting as an adaptor protein between Rab7 on late endosomes and LAMP2 on primary lysosomes.Consequently,Rufy4-deficient mice were highly protected from lipopolysaccharide-and ovariectomy-induced bone loss.Thus,RUFY4 plays as a new regulator in osteoclast activity by mediating endo-lysosomal trafficking and have a potential to be specific target for therapies against bone-loss diseases such as osteoporosis.
文摘A significant obstacle in intelligent transportation systems(ITS)is the capacity to predict traffic flow.Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately.However,accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors.This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory(Conv-BiLSTM)with attention mechanisms.Prior studies neglected to include data pertaining to factors such as holidays,weather conditions,and vehicle types,which are interconnected and significantly impact the accuracy of forecast outcomes.In addition,this research incorporates recurring monthly periodic pattern data that significantly enhances the accuracy of forecast outcomes.The experimental findings demonstrate a performance improvement of 21.68%when incorporating the vehicle type feature.
基金funded by the National Natural Science Foundation of China (Grant No. 11875031)the key research projects of Natural Science of Anhui Provincial Colleges and Universities (Grant No. 2022AH050252)。
文摘As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the classic instantaneous traffic emission model and the limited deceleration capacity microscopic traffic flow model with slow-to-start rules, this paper has investigated the impact of speed humps on traffic flow and the instantaneous emissions of vehicle pollutants in a single lane situation. The numerical simulation results have shown that speed humps have significant effects on traffic flow and traffic emissions. In a free-flow region, the increase of speed humps leads to the continuous rise of CO_(2), NO_(X) and PM emissions. Within some density ranges, one finds that these pollutant emissions can evolve into some higher values under some random seeds. Under other random seeds, they can evolve into some lower values. In a wide moving jam region, the emission values of these pollutants sometimes appear as continuous or intermittent phenomenon. Compared to the refined Na Sch model, the present model has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher volatile organic components(VOC) emissions. Compared to the limited deceleration capacity model without slow-to-start rules, the present model also has lower instantaneous emissions such as CO_(2), NO_(X) and PM and higher VOC emissions in a wide moving jam region. These results can also be confirmed or explained by the statistical values of vehicle velocity and acceleration.
基金Project supported by the Fundamental Research Funds for Central Universities,China(Grant No.2022YJS065)the National Natural Science Foundation of China(Grant Nos.72288101 and 72371019).
文摘With the development of intelligent and interconnected traffic system,a convergence of traffic stream is anticipated in the foreseeable future,where both connected automated vehicle(CAV)and human driven vehicle(HDV)will coexist.In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system,we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference(FVD)model.Subsequently,adopting the linear stability theory,we propose the linear stability condition for the model through using the small perturbation method,and the validity of the heterogeneous model is verified by comparing with the FVD model.Through nonlinear theoretical analysis,we further derive the KdV-Burgers equation,which captures the propagation characteristics of traffic density waves.Finally,by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow,the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated.Subsequent analysis reveals emergent traffic phenomena.The experimental findings demonstrate that as CAV permeability increases,the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation,leading to enhanced stability of the traffic system.Furthermore,higher initial traffic density renders the traffic system more susceptible to congestion,resulting in local clustering effect and stop-and-go traffic phenomenon.Remarkably,the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing.Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow.This research contributes to providing theoretical guidance for future CAV applications,aiming to enhance urban road traffic efficiency and alleviate congestion.
基金support from the National Natural Science Foundation of China (No.52204202)the Hunan Provincial Natural Science Foundation of China (No.2023JJ40058)the Science and Technology Program of Hunan Provincial Departent of Transportation (No.202122).
文摘In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.
基金support from the National Natural Science Foundation of China(No.41941018,No.52074299)the Fundamental Research Funds for the Central Universities(No.2023JCCXSB02)the China Geological Survey Project(DD20221816,DD20211376)are gratefully acknowledged.
文摘To investigate the influence mechanism of geostress on rockburst characteristics,three groups of gneiss rockburst experiments were conducted under different initial geostress conditions.A high-speed photography system and acoustic emission(AE)monitoring system were used to monitor the entire rockburst process in real time.The experimental results show that when the initial burial depth increases from 928 m to 1320 m,the proportion of large fracture scale in rockburst increases by 154.54%,and the AE energy increases by 565.63%,reflecting that the degree and severity of rockburst increase with the increase of burial depth.And then,two mechanisms are proposed to explain this effect,including(i)the increase of initial geostress improves the energy storage capacity of gneiss,and then,the excess energy which can be converted into kinetic energy of debris ejection increases,consequently,a more pronounced violent ejection phenomenon is observed at rockburst;(ii)the increase of initial geostress causes more sufficient plate cracks of gneiss after unloading ofσh,which provides a basis for more severe ejection of rockburst.What’s more,a precursor with clear physical meaning for rockburst is proposed under the framework of dynamic response process of crack evolution.Finally,potential value in long term rockburst warning of the precursor obtained in this study is shown via the comparison of conventional precursor.
基金the Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2024-1008.
文摘Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial resources.This study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic challenges.MANETs are wireless networks that are based on mobile devices and may self-organize.The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts.This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within theMANETs.The framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real time.Real-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce congestion.The framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban planners.Extensive simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and scalability.Experimental results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.
基金Project supported by the Guangxi Natural Science Foundation,China(Grant No.2022GXNSFDA035080)the Central Government Guidance Funds for Local Scientific and Technological Development,China(Grant No.Guike ZY22096024)the National Natural Science Foundation,China(Grant No.61963008).
文摘Speed limit measures are ubiquitous due to the complexity of the road environment,which can be supplied with the help of vehicle to everything(V2X)communication technology.Therefore,the influence of speed limit on traffic system will be investigated to construct a two-lane lattice model accounting for the speed limit effect during the lane change process under V2X environment.Accordingly,the stability condition and the mKdV equation are closely associated with the speed limit effect through theory analysis.Moreover,the evolution of density and hysteresis loop is simulated to demonstrate the positive role of the speed limit effect on traffic stability in the cases of strong reaction intensity and high limited speed.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509,Development of Security Monitoring Technology Based Network Behavior against Encrypted Cyber Threats in ICT Convergence Environment).
文摘In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted traffic.While research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific nodes.This study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node information.The proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet metadata.This makes it well-suited for direct application in IoT environments due to its low resource consumption.In experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based features.Additionally,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and OneClassSVM.Furthermore,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling methods.This study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.
基金National Natural Science Foundation of China under Grant Nos.U1939210 and 51825801。
文摘The post-earthquake emergency period,which is a sensitive time segment just after an event,mainly focuses on saving life and restoring social order.To improve the seismic resilience of city road networks,a resilience evaluation method used in the post-earthquake emergency period is proposed.The road seismic damage index of a city road network can consider the influence of roads,bridges and buildings along the roads,etc.on road capacity after an earthquake.A function index for a city road network is developed,which reflects the connectivity,redundancy,traffic demand and traffic function of the network.An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation,to enable decision support for city emergency management and achieve the best seismic resilience of the city road network.The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period.
基金The Science and Technology Research and Development Program Project of China Railway Group Ltd provided funding for this study(Project Nos.2020-Special-02 and 2021Special-08)。
文摘Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.
基金This work was supported by the Basic Science Research Program through the NationalResearch Foundation ofKorea(NRF)funded by the Ministry of Education under Grant RS-2023-00237300 and Korea Institute of Planning and Evaluation for Technology in Food,Agriculture and Forestry(IPET)through the Agriculture and Food Convergence Technologies Program for Research Manpower Development,funded by Ministry of Agriculture,Food and Rural Affairs(MAFRA)(Project No.RS-2024-00397026).
文摘The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications,such as remote health monitoring,industrial monitoring,transportation,and smart agriculture.Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes.This paper presents a traffic-aware,cluster-based,and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks.The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies.The protocol considers multi-criteria attributes,i.e.,energy,traffic load,and distance parameters to select the next hop for data delivery towards the base station.The performance of the proposed protocol is evaluated through the network simulator NS3.40.For different traffic rates,number of nodes,and different packet sizes,the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio,energy consumption,end-to-end delay,and network lifetime.For 100 nodes,the proposed protocol achieved a 13%improvement in packet delivery ratio,10 ms improvement in delay,and 10 mJ improvement in average energy consumption over LoRaWAN.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia through Research Group No.(RG-NBU-2022-1234).
文摘Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.
基金the Key JCJQ Program of China:2020-JCJQ-ZD-021-00 and 2020-JCJQ-ZD-024-12.
文摘Website fingerprinting,also known asWF,is a traffic analysis attack that enables local eavesdroppers to infer a user’s browsing destination,even when using the Tor anonymity network.While advanced attacks based on deep neural network(DNN)can performfeature engineering and attain accuracy rates of over 98%,research has demonstrated thatDNNis vulnerable to adversarial samples.As a result,many researchers have explored using adversarial samples as a defense mechanism against DNN-based WF attacks and have achieved considerable success.However,these methods suffer from high bandwidth overhead or require access to the target model,which is unrealistic.This paper proposes CMAES-WFD,a black-box WF defense based on adversarial samples.The process of generating adversarial examples is transformed into a constrained optimization problem solved by utilizing the Covariance Matrix Adaptation Evolution Strategy(CMAES)optimization algorithm.Perturbations are injected into the local parts of the original traffic to control bandwidth overhead.According to the experiment results,CMAES-WFD was able to significantly decrease the accuracy of Deep Fingerprinting(DF)and VarCnn to below 8.3%and the bandwidth overhead to a maximum of only 14.6%and 20.5%,respectively.Specially,for Automated Website Fingerprinting(AWF)with simple structure,CMAES-WFD reduced the classification accuracy to only 6.7%and the bandwidth overhead to less than 7.4%.Moreover,it was demonstrated that CMAES-WFD was robust against adversarial training to a certain extent.
基金supported by the Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korean Government(MOTIE)(P0008703,The Competency Development Program for Industry Specialists)MSIT under the ICAN(ICT Challenge and Advanced Network of HRD)Program(No.IITP-2022-RS-2022-00156310)supervised by the Institute of Information&Communication Technology Planning and Evaluation(IITP).
文摘With the advancement of wireless network technology,vast amounts of traffic have been generated,and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated.While signature-based detection methods,static analysis,and dynamic analysis techniques have been previously explored for malicious traffic detection,they have limitations in identifying diversified malware traffic patterns.Recent research has been focused on the application of machine learning to detect these patterns.However,applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process.In this study,we examined methods for effectively utilizing machine learning-based malicious traffic detection approaches for lightweight devices.We introduced the suboptimal feature selection model(SFSM),a feature selection technique designed to reduce complexity while maintaining the effectiveness of malicious traffic detection.Detection performance was evaluated on various malicious traffic,benign,exploits,and generic,using the UNSW-NB15 dataset and SFSM sub-optimized hyperparameters for feature selection and narrowed the search scope to encompass all features.SFSM improved learning performance while minimizing complexity by considering feature selection and exhaustive search as two steps,a problem not considered in conventional models.Our experimental results showed that the detection accuracy was improved by approximately 20%compared to the random model,and the reduction in accuracy compared to the greedy model,which performs an exhaustive search on all features,was kept within 6%.Additionally,latency and complexity were reduced by approximately 96%and 99.78%,respectively,compared to the greedy model.This study demonstrates that malicious traffic can be effectively detected even in lightweight device environments.SFSM verified the possibility of detecting various attack traffic on lightweight devices.
文摘Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.
基金supported by the Key Research and Development Program of Xinjiang Uygur Autonomous Region(No.2022B01008)the National Natural Science Foundation of China(No.62363032)+4 种基金the Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2023D01C20)the Scientific Research Foundation of Higher Education(No.XJEDU2022P011)National Science and Technology Major Project(No.2022ZD0115803)Tianshan Innovation Team Program of Xinjiang Uygur Autonomous Region(No.2023D14012)the“Heaven Lake Doctor”Project(No.202104120018).
文摘Along with the progression of Internet of Things(IoT)technology,network terminals are becoming continuously more intelligent.IoT has been widely applied in various scenarios,including urban infrastructure,transportation,industry,personal life,and other socio-economic fields.The introduction of deep learning has brought new security challenges,like an increment in abnormal traffic,which threatens network security.Insufficient feature extraction leads to less accurate classification results.In abnormal traffic detection,the data of network traffic is high-dimensional and complex.This data not only increases the computational burden of model training but also makes information extraction more difficult.To address these issues,this paper proposes an MD-MRD-ResNeXt model for abnormal network traffic detection.To fully utilize the multi-scale information in network traffic,a Multi-scale Dilated feature extraction(MD)block is introduced.This module can effectively understand and process information at various scales and uses dilated convolution technology to significantly broaden the model’s receptive field.The proposed Max-feature-map Residual with Dual-channel pooling(MRD)block integrates the maximum feature map with the residual block.This module ensures the model focuses on key information,thereby optimizing computational efficiency and reducing unnecessary information redundancy.Experimental results show that compared to the latest methods,the proposed abnormal traffic detection model improves accuracy by about 2%.