The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considerin...The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considering the characteristic of Chinese traffic, artificial neural network was used to predict traffic accident, and an improved BP artificial neural network model according with Chinese the situation of a country was proposed. The urban traffic flow prediction was simulated under the particular situation, the simulation result shows that the improved BP artificial neural network can fit the urban traffic flow prediction very well and have high performance.展开更多
In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is...In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks.展开更多
VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and c...VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.展开更多
This paper is concerned with the design of expressway toll station problem based on neural network and traffic flow. Firstly, the design of the toll plaza is mainly through analyzing the daily traffic flow, different ...This paper is concerned with the design of expressway toll station problem based on neural network and traffic flow. Firstly, the design of the toll plaza is mainly through analyzing the daily traffic flow, different charging mode of construction cost and waiting time of the United States. Secondly, exploring traffic conditions is divided into two kinds, based on the traffic flow speed-density flow model. Then, a fuzzy-BP neural network model is constructed, with capacity, cost, and safety factor as the input layers and performance as the output layer. It is concluded that this scheme will reduce the occurrence of traffic accidents, so it is desirable. Considering that the increase in unmanned vehicles will lead to an increase in safety performance, we increase the number of electronic toll stations to improve security performance and reduce the occurrence of traffic accidents.展开更多
Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous rese...Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).展开更多
Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion with...Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.展开更多
To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase spa...To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.展开更多
Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local...Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local PMC,considering marginal cost of partial links, is normallycalculated to approximate the global PMC. When analyzingthe marginal cost at a congested diverge intersection, ajump-point phenomenon may occur. It manifests as alikelihood that a vehicle may unsteadily lift up （down） inthe cumulative flow curve of the downstream links. Previously,the jump-point caused delay was ignored whencalculating the local PMC. This article proposes an analyticalmethod to solve this delay which can contribute toobtaining a more accurate local PMC. Next to that, we usea simple case to calculate the previously local PMC and themodified one. The test shows a large gap between them,which means that this delay should not be omitted in thelocal PMC calculation.展开更多
Traffic flow prediction has been applied into many wireless communication applications(e.g., smart city, Internet of Things). With the development of wireless communication technologies and artificial intelligence, ho...Traffic flow prediction has been applied into many wireless communication applications(e.g., smart city, Internet of Things). With the development of wireless communication technologies and artificial intelligence, how to design a system for real-time traffic flow prediction and receive high accuracy of prediction are urgent problems for both researchers and equipment suppliers. This paper presents a novel real-time system for traffic flow prediction. Different from the single algorithm for traffic flow prediction, our novel system firstly utilizes dynamic time wrapping to judge whether traffic flow data has regularity,realizing traffic flow data classification. After traffic flow data classification, we respectively make use of XGBoost and wavelet transform-echo state network to predict traffic flow data according to their regularity. Moreover, in order to realize real-time classification and prediction, we apply Spark/Hadoop computing platform to process large amounts of traffic data. Numerical results show that the proposed novel system has better performance and higher accuracy than other schemes.展开更多
文摘The traffic flow is interrelated to traffic congestion, the big traffic flow directly results in traffic congestion of some section. In this paper, on the basis of the research of overseas traffic accident, considering the characteristic of Chinese traffic, artificial neural network was used to predict traffic accident, and an improved BP artificial neural network model according with Chinese the situation of a country was proposed. The urban traffic flow prediction was simulated under the particular situation, the simulation result shows that the improved BP artificial neural network can fit the urban traffic flow prediction very well and have high performance.
基金ACKNOWLEDGEMENT This work was supported by National Natural Science Foundation of China （Grant No. 61231008）, National Basic Research Program of China （973 Program） （Grant No. 2009CB320404）, Program for Changjiang Scholars and Innovative Research Team in University （Grant No. IRT0852）, and the 111 Project （Grant No. B08038）.
文摘In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks.
文摘VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.
文摘This paper is concerned with the design of expressway toll station problem based on neural network and traffic flow. Firstly, the design of the toll plaza is mainly through analyzing the daily traffic flow, different charging mode of construction cost and waiting time of the United States. Secondly, exploring traffic conditions is divided into two kinds, based on the traffic flow speed-density flow model. Then, a fuzzy-BP neural network model is constructed, with capacity, cost, and safety factor as the input layers and performance as the output layer. It is concluded that this scheme will reduce the occurrence of traffic accidents, so it is desirable. Considering that the increase in unmanned vehicles will lead to an increase in safety performance, we increase the number of electronic toll stations to improve security performance and reduce the occurrence of traffic accidents.
基金Supported by the Support Program of the National 12th Five Year-Plan of China(2015BAK25B03)
文摘Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).
文摘Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.
基金Sponsored by the National Eleventh Five year Plan Key Project of Ministry of Science and Technology of China (Grant No. 2006BAJ03A05-05)
文摘To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.
文摘Path marginal cost （PMC） is the change in totaltravel cost for flow on the network that arises when timedependentpath flow changes by 1 unit. Because it is hardto obtain the marginal cost on all the links, the local PMC,considering marginal cost of partial links, is normallycalculated to approximate the global PMC. When analyzingthe marginal cost at a congested diverge intersection, ajump-point phenomenon may occur. It manifests as alikelihood that a vehicle may unsteadily lift up （down） inthe cumulative flow curve of the downstream links. Previously,the jump-point caused delay was ignored whencalculating the local PMC. This article proposes an analyticalmethod to solve this delay which can contribute toobtaining a more accurate local PMC. Next to that, we usea simple case to calculate the previously local PMC and themodified one. The test shows a large gap between them,which means that this delay should not be omitted in thelocal PMC calculation.
基金partly supported by the National Natural Science Foundation of China(Grants No.61571240,61671474)the Jiangsu Science Fund for Excellent Young Scholars(No.BK20170089)+2 种基金the ZTE program“The Prediction of Wireline Network Malfunction and Traffic Based on Big Data,”(No.2016ZTE04-07)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX18_0916)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Traffic flow prediction has been applied into many wireless communication applications(e.g., smart city, Internet of Things). With the development of wireless communication technologies and artificial intelligence, how to design a system for real-time traffic flow prediction and receive high accuracy of prediction are urgent problems for both researchers and equipment suppliers. This paper presents a novel real-time system for traffic flow prediction. Different from the single algorithm for traffic flow prediction, our novel system firstly utilizes dynamic time wrapping to judge whether traffic flow data has regularity,realizing traffic flow data classification. After traffic flow data classification, we respectively make use of XGBoost and wavelet transform-echo state network to predict traffic flow data according to their regularity. Moreover, in order to realize real-time classification and prediction, we apply Spark/Hadoop computing platform to process large amounts of traffic data. Numerical results show that the proposed novel system has better performance and higher accuracy than other schemes.
