Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,592
result(s) for
"Traffic patterns."
Sort by:
Novel approach for detection of IoT generated DDoS traffic
The problem of detecting anomalies in network traffic caused by the distributed denial of service (DDoS) attack so far has mainly been investigated in terms of detection of illegitimate DDoS traffic generated by conventional terminal devices (PCs, laptops, mobile devices, tablets, servers). Technological development has resulted in the emergence of the Internet of Things (IoT) concept, whose implementation implies numerous terminal devices with a low level of implemented protection. The large growth and prediction of future growth is noticeable in the environment of a smart home and smart office. IoT devices in such environments are increasingly being used as a platform for generating DDoS traffic due to its numeracy and low level of protection. The aim of this research is to propose a novel approach for detection of DDoS traffic generated by IoT devices in a form of conceptual network anomaly detection model. Proposed conceptual model is based on device classes which are dependent on individual device traffic characteristics.
Journal Article
Performance evaluation of delay in optical packet switching using various traffic patterns
Delay is an important parameter in optical packet switching networks and it affects the performance of the network. In this communication, a mathematical analysis is carried out to evaluate the delay. Delay values are analysed for variable length packets for various traffic patterns, viz. non-uniform, Poisson and ON–OFF traffic patterns for various service classes using reservation bit algorithm. The results of the class-based models are compared with the existing port-based first-fit wavelength assignment algorithm. Delay values are reduced by 29% in the class-based model than in the port-based model. Furthermore, packet transmission rate in class-based model is higher by 15.4% than port-based model.
Journal Article
Traffic Patterns and Emergency Medical Services Prenotification Transport Estimates in Trauma Activations Response to Letter
Sophia Gorgens,1 Eric N Klein,2,3 Matthew A Bank,2,3 Daniel Jafari1,3 1Department of Emergency Medicine, Northwell Health, Long Island, NY, USA; 2Department of Surgery, Northwell Health, Long Island, NY, USA; 3Zucker School of Medicine at Hofstra, Long Island, NY, USACorrespondence: Sophia Gorgens, Department of Emergency Medicine, Northwell Health, 300 Community Drive, Manhasset, NY, 11030, USA, Email sophia.gorgens@gmail.com
Journal Article
Traffic Patterns and Emergency Medical Services Prenotification Transport Estimates in Trauma Activations Letter
Sari Luthfiyah,1 Triwiyanto Triwiyanto,2 Mohammed Ismath3 1Department of Nursing, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia; 2Department of Electromedical Technology, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia; 3Inamdar Multi-Specialty Hospital, Pune, Maharashtra, IndiaCorrespondence: Sari Luthfiyah, Email sarilut@poltekkesdepkes-sby.ac.id
Journal Article
Unlocking the Full Potential of Deep Learning in Traffic Forecasting Through Road Network Representations: A Critical Review
Research in short-term traffic forecasting has been blooming in recent years due to its significant implications in traffic management and intelligent transportation systems. The unprecedented advancements in deep learning have provided immense opportunities to leverage traffic data sensed from various locations of the road network, yet significantly increased the models’ complexity and data and computational requirements, limiting the actionability of the models. Consequently, the meaningful representation of traffic flow data and the road network has been highlighted as a key challenge in improving the efficiency, as well as the accuracy and reliability of forecasting models. This paper provides a systematic review of literature dedicated to spatiotemporal traffic forecasting. Three main representation approaches are identified, namely the stacked vector, image/grid, and graph, and are critically analyzed and compared in relation to their efficiency, accuracy and associated modeling techniques. Based on the findings, future research directions in traffic forecasting are proposed, aiming to increase the adoption of the developed models in real-world applications.
Journal Article
Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network
This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns’ dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods.
Journal Article
Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data
Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically.
Journal Article
Modeling and Classification of Random Traffic Patterns for Fatigue Analysis of Highway Bridges
With the increasing severity of traffic congestion, the impact of random traffic patterns has emerged as an indispensable factor in the fatigue design and assessment of highway bridges. In this study, an analytical approach has been proposed for modeling the effects of random traffic patterns on fatigue damage. A fatigue damage ratio, referred to as RPEF, is introduced to establish the correlation between damage and traffic characteristics. Two quantitative parameters representing two characteristics of traffic loads, namely the average loading occurrence number (scale parameter) and the vehicle headway COVs (shape parameter), have been found to be excellent indices for clustering the different dimensional randomness of RPEFs. Based on a comprehensive case study, the realization of using equivalent RPEFs to evaluate bridge fatigue damage under mixed traffic conditions was explored. The results indicate that the actual fatigue damage of bridges can be evaluated through the superposition of different traffic pattern effects, provided that the pattern classification number fits the fluctuations in traffic flow. It is necessary to ensure the rationality of traffic pattern classification for structures with spans greater than 50 m, as an overly simplistic traffic pattern classification may lead to an underestimation of fatigue damage.
Journal Article
An empirical analysis of airport capacity evaluation: insights regarding air traffic design hours
An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Airport capacity is rather difficult to describe due to its multi-faceted and dynamic nature, and it depends both on the available infrastructure, on external factors and on operating procedures. Moreover, annual capacity is used for long term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. By reviewing the relationships between hourly and annual air traffic volumes at 50 European airports during the period 2004-2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. Additionally, we appraise different daily traffic distribution patterns and their variation by hour of the day. The clustering of airports with respect to their capacity, operational, and traffic characteristics allows us to discover functional relationships between design hours and annual volumes. These relationships help us to propose empirical methods to derive expected traffic in design hours from annual volumes. This could be used to properly assess airport expansion projects or to optimise resource allocation tasks. Finally, we provide new evidence on the nature of airport capacity and the dynamics of air traffic design hours.
Journal Article