With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classifica...

This paper discusses necessary components of a GPU-assisted traffic classification method, which is capable of multi-Gbps speeds on commodity hardware. The majority of the traffic classification is pushed to the GPU to offload the CPU, which then may serve other processing intensive tasks, e.g., traffic capture. The paper presents two massively parallelizable algorithms suitable for GPUs. The f...

The increasing popularity of HTTP adaptive video streaming services has dramatically increased bandwidth requirements on operator networks, which attempt to shape their traffic through Deep Packet Inspection (DPI). However, Google and certain content providers have started to encrypt their video services. As a result, operators often encounter difficulties in shaping their encrypted video traff...

Many network management tasks such as managing bandwidth budget and ensuring quality of service objectives rely on accurate classification of network traffic. But the statistical features of encrypted traffics are not stable and do not contain sufficient information for classification all the time. Some applications support multiple protocols, and the behaviors of these applications are complic...

obviously, improving the road safety and the efficient allocation of limited resources to the provinces according to their ranking should be done. this paper presents a hybrid method of multivariate statistical analysis-decision making to evaluate iran road traffic safety. in order to solve the problems of road traffic safety, a macroscopic evaluation and traffic safety level classification in ...

Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated set images almost all signs highways 2014, correspon...

Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirem...

Background: Given that a significant part of deaths due to traffic accidents are preventable in the prehospital phase, so it seems necessary to have an instrument to identify and measure the factors affecting it. This study aimed to develop and psychometrically assess an instrument for measuring preventable prehospital mortality due to traffic accidents. Methods: The study is a mixed qualitati...

Currently, the network traffic classification has two important problems, which are low accuracy and high computation complexity. In order to solve these problems, a novel network traffic classification method using support vector machine with feature weighted-degree (FWD-SVM) is proposed in this study. Our method can efficiently reduce the influence on the sample distribution, relative propert...

The research on network traffic classification has recently become very active. The research community, moved by increasing difficulties in the automated identification of network traffic, started to investigate classification approaches alternative to port-based and payload-based techniques. Despite the large quantity of works published in the past few years on this topic, very few implementat...