Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact o...Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration.Since reaching a high CACC market penetration level is not occurring in the near future,this study presents a progressive deployment approach that demonstrates to have a great potential of reducing trafc congestions at low CACC penetration levels.Using a previously developed microscopic trafc simulation model of a freeway with an on-ramp—created to induce perturbations and trigger stop-and-go trafc,the CACC system s efect on the trafc performance is studied.The results show signifcance and indicate the potential of CACC systems to improve trafc characteristics which can be used to reduce trafc congestion.The study shows that the impact of CACC is positive and not only limited to a high market penetration.By giving CACC vehicles priority access to high-occupancy vehicle(HOV)lanes,the highway capacity could be signifcantly improved with a CACC penetration as low as 20%.展开更多
Abnormal trafc detection is a crucial topic in the feld of network security.However,existing methods face many challenges when processing complex high-dimensional trafc data.Especially in dealing with redundant featur...Abnormal trafc detection is a crucial topic in the feld of network security.However,existing methods face many challenges when processing complex high-dimensional trafc data.Especially in dealing with redundant features,data sparsity and nonlinear features,traditional methods often sufer from high computational complexity and low detection efciency.It is challenging to capture potential patterns in complex data efectively and cannot fully meet the needs of practical applications.To address these challenges,this paper proposes an enhanced anomaly traffc detection framework using bidirectional generative adversarial networks(BiGAN)and contrastive learning.This method preprocesses high-dimensional data through steps such as data cleaning,normalization,and clustering to improve data quality.It uses BiGAN and contrastive learning technology to enhance the model’s feature representation capabilities.Experimental results show that the method proposed in this paper performs well on multiple trafc data sets and signifcantly improves the accuracy and efciency of anomaly detection.Overall,the solution proposed in this paper efectively overcomes the limitations of existing methods in high-dimensional data processing and provides a more advanced abnormal trafc detection strategy.展开更多
Network trafc anomaly detection is a critical issue in network security.Existing Abnormal trafc detection methods rely on statistical-based or anomaly-based approaches,and these detection methods all require a full un...Network trafc anomaly detection is a critical issue in network security.Existing Abnormal trafc detection methods rely on statistical-based or anomaly-based approaches,and these detection methods all require a full understanding of trafc characteristics and attack patterns.Information entropy has been widely studied in abnormal trafc detection because it can describe the distribution characteristics of network trafc.However,this method makes it difcult to cope with the timing and variability of network trafc.To address these challenges,this paper proposes a network trafc anomaly detection method based on Renyi entropy.Simultaneously,we introduce a fxed time window and utilize an improved EWMA model within this window to dynamically set thresholds for anomaly detection.Experimental results show that the method proposed in this paper is superior to popular abnormal trafc detection methods in terms of efectiveness and efciency,it is better adapted to the dynamic changes of network trafc and provides a more reliable solution for anomaly detection.展开更多
The better management of resources and the potential improvement in trafc congestion via reducing the orbiting time for parking spaces is crucial in a smart city,particularly those with an uneven correlation between t...The better management of resources and the potential improvement in trafc congestion via reducing the orbiting time for parking spaces is crucial in a smart city,particularly those with an uneven correlation between the increase in vehicles and infrastructure.This paper proposes and analyses a novel green IoT-based Pay-As-You-Go(PAYG)smart parking system by utilizing unused garage parking spaces.The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’pricing portfolio with a garage’s current demand.Malta,the world’s fourth-most densely populated country,is considered as a case of a smart city for the implementation of the proposed approach.The results obtained conrm that apart from having a high potential system in such countries,the pricing generated correctly forecasts the demand for a particular garage at a specic time of the day and year.The proposed PAYG smart parking system can effectively contribute to the macro solution to trafc congestion by encouraging potential users to use the system’s services and reducing the orbiting time for parking.展开更多
文摘Cooperative adaptive cruise control(CACC)vehicles are intelligent vehicles that use vehicular ad hoc networks(VANETs)to share trafc information in real time.Previous studies have shown that CACC could have an impact on increasing highway capacities at high market penetration.Since reaching a high CACC market penetration level is not occurring in the near future,this study presents a progressive deployment approach that demonstrates to have a great potential of reducing trafc congestions at low CACC penetration levels.Using a previously developed microscopic trafc simulation model of a freeway with an on-ramp—created to induce perturbations and trigger stop-and-go trafc,the CACC system s efect on the trafc performance is studied.The results show signifcance and indicate the potential of CACC systems to improve trafc characteristics which can be used to reduce trafc congestion.The study shows that the impact of CACC is positive and not only limited to a high market penetration.By giving CACC vehicles priority access to high-occupancy vehicle(HOV)lanes,the highway capacity could be signifcantly improved with a CACC penetration as low as 20%.
基金supported by the National Natural Science Foundation of China(No.61936008).
文摘Abnormal trafc detection is a crucial topic in the feld of network security.However,existing methods face many challenges when processing complex high-dimensional trafc data.Especially in dealing with redundant features,data sparsity and nonlinear features,traditional methods often sufer from high computational complexity and low detection efciency.It is challenging to capture potential patterns in complex data efectively and cannot fully meet the needs of practical applications.To address these challenges,this paper proposes an enhanced anomaly traffc detection framework using bidirectional generative adversarial networks(BiGAN)and contrastive learning.This method preprocesses high-dimensional data through steps such as data cleaning,normalization,and clustering to improve data quality.It uses BiGAN and contrastive learning technology to enhance the model’s feature representation capabilities.Experimental results show that the method proposed in this paper performs well on multiple trafc data sets and signifcantly improves the accuracy and efciency of anomaly detection.Overall,the solution proposed in this paper efectively overcomes the limitations of existing methods in high-dimensional data processing and provides a more advanced abnormal trafc detection strategy.
基金supported by the National Natural Science Foundation of China(No.61936008).
文摘Network trafc anomaly detection is a critical issue in network security.Existing Abnormal trafc detection methods rely on statistical-based or anomaly-based approaches,and these detection methods all require a full understanding of trafc characteristics and attack patterns.Information entropy has been widely studied in abnormal trafc detection because it can describe the distribution characteristics of network trafc.However,this method makes it difcult to cope with the timing and variability of network trafc.To address these challenges,this paper proposes a network trafc anomaly detection method based on Renyi entropy.Simultaneously,we introduce a fxed time window and utilize an improved EWMA model within this window to dynamically set thresholds for anomaly detection.Experimental results show that the method proposed in this paper is superior to popular abnormal trafc detection methods in terms of efectiveness and efciency,it is better adapted to the dynamic changes of network trafc and provides a more reliable solution for anomaly detection.
基金funding by the University of Malta’s Internal Research Grants.
文摘The better management of resources and the potential improvement in trafc congestion via reducing the orbiting time for parking spaces is crucial in a smart city,particularly those with an uneven correlation between the increase in vehicles and infrastructure.This paper proposes and analyses a novel green IoT-based Pay-As-You-Go(PAYG)smart parking system by utilizing unused garage parking spaces.The article also presents an intelligent system that offers the most favorable prices to its users by matching private garages’pricing portfolio with a garage’s current demand.Malta,the world’s fourth-most densely populated country,is considered as a case of a smart city for the implementation of the proposed approach.The results obtained conrm that apart from having a high potential system in such countries,the pricing generated correctly forecasts the demand for a particular garage at a specic time of the day and year.The proposed PAYG smart parking system can effectively contribute to the macro solution to trafc congestion by encouraging potential users to use the system’s services and reducing the orbiting time for parking.
