Bibliographies thématiques / Traffic analysi

Littérature scientifique sur le sujet « Traffic analysi »

Auteur : Grafiati
Publié le 14 mars 2023

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Articles de revues sur le sujet "Traffic analysi"

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Manvelidze, A. B. « Air lines network modelling algorithm ». Strategic decisions and risk management, no 6 (13 février 2018) : 22–29. http://dx.doi.org/10.17747/2078-8886-2017-6-22-29.

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This analysis is dedicated to find out methods for setting of route networks where new aircraft can be effectively put into service. The conception of this analysis is based on the idea of so called connectivity principle for airports connected by passenger traffic with each other.For the passenger traffic analysis the author took passenger traffic data by federal districts starting from the Far East. Then consequently the data for Siberian, Ural, Wolga, Northwestern, Central, Southern and North Caucasian federal districts were analyzed. Passenger traffic to the Crimea was treated separately. Detailed specifications of passenger traffics were provided in order to determine the connections between airports both within federal districts and beyond them and with neighboring areas in western direction. Query of routes was done based on limitations for non-stop flight range and on minimum and maximum (for significant traffics) flight frequencies.The analysis approach lets us concentrate attention on those airlines which at best fit for putting into service of chosen aircraft. Also this method permits to determine the routes with currently insufficient or low traffics but where there’s a definite growth potential. When analysis data are combined with traffic data and tariffs, then it becomes possible to determine the most profitable routes for introduction of new aircraft. Traffic volume, actual figures and forecast, consolidated characteristics of chosen airlines, list of airlines for further studies of efficiency and competitiveness of introduced aircraft are determined.
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CURPEN, Radu, Mihai-Iulian ILIESCU et Florin SANDU. « AN ANALYSIS OF 3G-4G TRAFFIC GUIDANCE METHODS ». Review of the Air Force Academy 14, no 2 (8 décembre 2016) : 123–32. http://dx.doi.org/10.19062/1842-9238.2016.14.2.15.

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Adje, Erick A., Vinasetan Ratheil Houndji et Michel Dossou. « Features analysis of internet traffic classification using interpretable machine learning models ». IAES International Journal of Artificial Intelligence (IJ-AI) 11, no 3 (1 septembre 2022) : 1175. http://dx.doi.org/10.11591/ijai.v11.i3.pp1175-1183.

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Internet traffic classification is a fundamental task for network services and management. There are good machine learning models to identify the class of traffic. However, finding the most discriminating features to have efficient models remains essential. In this paper, we use interpretable machine learning algorithms such as decision tree, random forest and eXtreme gradient boosting (XGBoost) to find the most discriminating features for internet traffic classification. The dataset used contains 377,526 traffics. Each traffic is described by 248 features. From these features, we propose a 12-feature model with an accuracy of up to 99.76%. We tested it on another dataset with 19626 flows and obtained 98.40% of accuracy. This shows the efficiency and stability of our model. Also, we identify a set of 14 important features for internet traffic classification, including two that are crucial: port number (server) and minimum segment size (client to server).
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G, Kothai, et Poovammal E. « Performance Analysis of Stationary and Deterministic AODV Model ». International Journal of Interactive Mobile Technologies (iJIM) 14, no 17 (13 octobre 2020) : 33. http://dx.doi.org/10.3991/ijim.v14i17.16643.

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Vehicular Adhoc Network (VANET) is an emerging technology that provides a digital communication among vehicles, persons and Road-Side Units (RSU). VANETs are highly vulnerable to cyber-attacks. These cyber-attacks make a wrong illusion on traffic jam, can inject false information regarding traffics and injects large amount of spam messages that disrupts the normal functionalities. The main objective of the research work is to implement and analyze the different models that help in improving the traffic management. The scenarios are simulated, and the performance is analyzed using the OMNET++ Simulator<strong>.</strong>
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Mishra, Shailendra. « Network Traffic Analysis Using Machine Learning Techniques in IoT Networks ». International Journal of Software Innovation 9, no 4 (octobre 2021) : 1–17. http://dx.doi.org/10.4018/ijsi.289172.

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Internet of things devices are not very intelligent and resource-constrained; thus, they are vulnerable to cyber threats. Cyber threats would become potentially harmful and lead to infecting the machines, disrupting the network topologies, and denying services to their legitimate users. Artificial intelligence-driven methods and advanced machine learning-based network investigation prevent the network from malicious traffics. In this research, a support vector machine learning technique was used to classify normal and abnormal traffic. Network traffic analysis has been done to detect and prevent the network from malicious traffic. Static and dynamic analysis of malware has been done. Mininet emulator was selected for network design, VMware fusion for creating a virtual environment, hosting OS was Ubuntu Linux, network topology was a tree topology. Wireshark was used to open an existing pcap file that contains network traffic. The support vector machine classifier demonstrated the best performance with 99% accuracy.
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Jiang, Shi Bao, Chuan Feng Bai et Cui Feng Du. « Traffic Forecast Based on Empirical Mode Decomposition and RBF Neural Network ». Advanced Materials Research 846-847 (novembre 2013) : 1270–73. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1270.

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Traffic analysis and forecast are the classic topics of telecommunication research, as they provide strategic ground to address the issues such as mobile networks traffic jam, network coverage planning design, marketing management etc. On the basis of the empirical mode decomposition theory and methods, this article first implements multi-scale analysis of the time series of traffic, then it goes on to executes RBF neural network based on the different compositions, and finally reach the forecast expectation of voice traffics of selected base stations.
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Gajewski, Piotr, Jerzy Łopatka et Piotr Łubkowski. « Performance Analysis of Public Safety Cognitive Radio MANET for Diversified Traffic ». Sensors 22, no 5 (1 mars 2022) : 1927. http://dx.doi.org/10.3390/s22051927.

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This paper presents properties of a mobile ad hoc network (MANET) with dynamic spectrum management (DSM) and is devoted to the concept and implementation of the new traffic engine that is used in a High-Fidelity simulator of MANET with cognitive nodes for special applications. The communication traffic generated by each node is defined according to its role in the hierarchical structure of the operational scenario, determining its priorities, permission to use particular real time and non-real time services. The service usage is a source based model, defined in the user’s profile containing its statistical properties, describing periodicity, duration and randomness of traffic generation. The overall traffic generated by the node is a combination of traffics related to specific services. Their statistical parameters are based on real exercises results. The model was defined in the Matlab environment and next verified using the MAENA simulator for complex, operational scenarios. The achieved results show that use of both central and distributed DSM provides a better performance of the MANET network with complex traffic.
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Anupriya et Anita Singhrova. « Comparative Analysis of Time Series Forecasting Models for SDMN Traffic ». Journal of Advanced Research in Dynamical and Control Systems 11, no 0009-SPECIAL ISSUE (25 septembre 2019) : 531–40. http://dx.doi.org/10.5373/jardcs/v11/20192602.

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Lu, Jiazhong, Fengmao Lv, Zhongliu Zhuo, Xiaosong Zhang, Xiaolei Liu, Teng Hu et Wei Deng. « Integrating Traffics with Network Device Logs for Anomaly Detection ». Security and Communication Networks 2019 (13 juin 2019) : 1–10. http://dx.doi.org/10.1155/2019/5695021.

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Advanced cyberattacks are often featured by multiple types, layers, and stages, with the goal of cheating the monitors. Existing anomaly detection systems usually search logs or traffics alone for evidence of attacks but ignore further analysis about attack processes. For instance, the traffic detection methods can only detect the attack flows roughly but fail to reconstruct the attack event process and reveal the current network node status. As a result, they cannot fully model the complex multistage attack. To address these problems, we present Traffic-Log Combined Detection (TLCD), which is a multistage intrusion analysis system. Inspired by multiplatform intrusion detection techniques, we integrate traffics with network device logs through association rules. TLCD correlates log data with traffic characteristics to reflect the attack process and construct a federated detection platform. Specifically, TLCD can discover the process steps of a cyberattack attack, reflect the current network status, and reveal the behaviors of normal users. Our experimental results over different cyberattacks demonstrate that TLCD works well with high accuracy and low false positive rate.
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SEMENIKHIN, B. A., L. P. KUZNETSOVA, YU A. MALNEVA et A. YU ALTUKHOV. « THE ANALYSIS OF PASSENGER TRAFFIC ON THE ROUTES OF THE BUS OF KURSK ». World of transport and technological machines 71, no 4 (décembre 2020) : 37–45. http://dx.doi.org/10.33979/2073-7432-2020-71-4-37-45.

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Results of inspection and the analysis of passenger traffics on routes of the bus of Kursk are presented, the main shortcomings of the existing route network are revealed. The analysis of change of daily volume of transportations of passengers made on the basis of data of it and the previous inspections of passenger traffics and also distribution of total power of a passenger traffic on hours of day is provided. Results of development of rational route bus network of Kursk which is almost completely deprived of the shortcomings inherent in the existing route network are presented.
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Thèses sur le sujet "Traffic analysi"

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FINAMORE, ALESSANDRO. « Analysis, characterization and classification of Internet traffic ». Doctoral thesis, Politecnico di Torino, 2012. http://hdl.handle.net/11583/2497191.

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The Internet is a global interconnection of networks representing nowadays one of the most important telecommunication technologies. Born as an U.S. military project, it has evolved in a worldwide communication system used by people every day. This success is based on its ``freedom'' since no single organization or administration entity governs or maintains it. This freedom also motivates the huge heterogeneity of Internet services available today ranging from working activities (e.g., VoIP, e-mail, etc.) to entertainment (e.g., video games, streaming, peer-to-peer, etc.) and commerce (e.g., Amazon, eBay, etc.) just to name a few. The Internet is a fertile and in constant evolution system. Every year new services and software platforms are launched affecting not only the users' activities (e.g. social networks) but also the internal architecture of the networks (e.g., Content Delivery Network vs peer-to-peer) or the devices used to access to the services (e.g., PC vs smartphones and Internet tablets). The richness of the Internet scenario is paid at the cost of its internal complexity. Eric Schmidt, the CEO of Google, said: \emph{``The Internet is the first thing that humanity has built that humanity doesn't understand, the largest experiment in anarchy that we have ever had.''}\footnote{\url{http://www.brainyquote.com/quotes/authors/e/eric_schmidt.html}}. At the origins, the Internet has been designed to operate on few standardized services. None could have i) foreseen the success of this media and ii) designed the network to cope with the plethora of nowadays services. If on the one hand this diversity provides the Internet with a certain level of resiliency and has driven innovation, on the other hand understanding its internal mechanisms is a daunting task, made worse by the fast and constant deployment of new services and applications. However, behind what it could seem a chaotic scenario, the Internet is composed by well defined markets in which big players participate having precise interests: \begin{description} \item \textbf{Users}, representing the majority of the people which assess to the network. They are interested in \emph{Quality of Experience} - QoE, i.e., having good performance when accessing to the network, avoiding for example long delay related to the initial buffering when streaming a video. They are also interested in the \emph{Network Neutrality}, preserving their freedom to use the Internet independently from which service they are accessing; \item \textbf{Internet Service Providers - ISP}, corresponding to organizations which provide Internet access to the customers. They are interested in incrementing the revenues through i) \emph{network engineering} as to optimize the offered services and ii) studying the users' activity as to find new \emph{billing policies}; \item \textbf{Content providers}, corresponding to organizations which sell a specific Internet service, e.g., video streaming, file hosting, etc. As for ISPs, they are interested in finding new way to make revenues. At the same time, they have to cope also with illegal activities as \emph{content piracy}, a common flaw since the early days of peer-to-peer systems; \item \textbf{Government regulation agencies}, corresponding to organizations which regulate some aspects of the Internet activities. For example, they study \emph{Service Level Agreements} - SLA between users and ISPs, comparing the quality of the Internet access offered to the users with respect to the specifications written in the contract signed. \end{description} Other activities as \emph{security} are important for more than one player. Consider for example \emph{malware} and \emph{Denial of Service} - DoS attacks. These can violate the users' privacy, damaging the network and violate some laws. Overall then, there are several motivations to be interested in studying the Internet. Since the early days, the scientific community has made giant steps toward understanding the Internet. We can generalize that two requirements have to be satisfied. First of all, we need \emph{tools and methodologies} as to inspect and characterize the traffic at different granularities, i.e., per-packet, per-flow, per-port, per-user, etc. In particular, \emph{traffic classification} is one of most important activities performed by network operators. It allows to identify which application has generated a given communication and to study not only the whole network traffic aggregate but also how different applications participate in the composition of the total traffic. Leveraging on these tools and methodologies, we can further drill into performing \emph{users and network characterization}. For example, monitoring the traffic over long-term periods, we can study the applications' popularity trends and identify the rise of new technologies. We can perform \emph{anomaly detection}, i.e., study unexpected network condition that might be related to either security issues of malfunctioning hardware. We can optimize routing policies, study inter-ISP traffic, investigate the energy consumption of the network elements or work on caching schemes related social network content, just to name a few of the huge amount of research studies recently conducted in the literature. In this thesis, we present our contributions in studying the Internet discussing the tools and methodologies developed to characterize the network traffic. The thesis is divided in two parts. In the first part we focus on traffic classification methodologies starting from the problem definition and the available solutions in the literature as reported in Chapter~\ref{chapter:traff_class}. In the remaining of the first part we focus on KISS, a novel traffic classification technique we propose based on \emph{Stochastic Packet Inspection} (SPI) analysis. In particular, in Chapter~\ref{chapter:kiss} we describe the framework used by the classifier which is then validated in Chapter~\ref{sec:kiss_udp} and~\ref{sec:kiss_tcp} for UDP and TCP traffic respectively. Chapter~\ref{chapter:compare} is about the comparison of KISS with other state of the art traffic classifier while in Chapter~\ref{sec:clustering} we extend the KISS framework with some clustering techniques. Overall, KISS allows to reach a high level of accuracy in traffic classification which is comparable or even better with respect to other traffic classifiers. It presents a flexible structure which is able to identify a rich set of applications with a limited amount of resource requirements. In the second part of the thesis we study YouTube, the famous video streaming system. Leveraging on Tstat, a passive traffic analyzer, we developed a methodology to identify the YouTube video downloads and we conduct an in depth analysis of many aspects of YouTube. In Chapter~\ref{sec:yt-overview} we start presenting an overview of the system and its components showing the internal mechanisms adopted. Chapter~\ref{sec:yt-methodology} reports an analysis of the available methodologies in the literature to study YouTube and presents our methodology based on monitoring the real users' activities considering different location, access technologies and devices. In the remaining chapters we present the results of our analysis grouped in four different areas of interest: video content properties (Chapter~\ref{sec:yt-content}), internal load balancing and caching policies (Chapter~\ref{sec:yt-cdn}), users' habits and behaviours (Chapter~\ref{sec:user}), and download performance (Chapter~\ref{sec:yt-performance}). Results show that YouTube is a complex system where several components interact with precise policies used to control the communications. Besides its great success, the system is far from being perfect and there is space for further optimization. For example, mobile devices suffer more impairments during the download with respect to PCs. Users stick to the default video resolution and are not interested in changing the quality during the playback. Instead, it is common the abruptly abort of the download. This behaviour is particularly critical because, coupled with aggressive buffering policies used to ensure continuity in the playback, it leads to waste a non negligible amount of traffic, i.e., the users download a portion of the video which it is never played.
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ROLLO, FEDERICA. « Verso soluzioni di sostenibilità e sicurezza per una città intelligente ». Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2022. http://hdl.handle.net/11380/1271183.

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Una città intelligente è un luogo in cui la tecnologia viene sfruttata per aiutare le amministrazioni pubbliche a prendere decisioni. La tecnologia può contribuire alla gestione di numerosi aspetti della vita quotidiana, offrendo ai cittadini servizi più affidabili e migliorando la qualità della vita. Tuttavia, la tecnologia da sola non è sufficiente per rendere una città intelligente; sono necessari metodi adeguati per analizzare i dati raccolti e gestirli in modo da generare informazioni utili. Alcuni esempi di servizi intelligenti sono le app che permettono di raggiungere una destinazione attraverso il percorso più breve oppure di trovare il parcheggio libero più vicino, o le app che suggeriscono i percorsi migliori per una passeggiata in base alla qualità dell'aria. Questa tesi si concentra su due aspetti delle smart city: sostenibilità e sicurezza. Il primo aspetto riguarda lo studio dell'impatto del traffico sulla qualità dell'aria attraverso lo sviluppo di una rete di sensori di traffico e qualità dell'aria e l'implementazione di una catena di modelli di simulazione. Questo lavoro fa parte del progetto TRAFAIR, cofinanziato dall'Unione Europea, il primo progetto che monitora la qualità dell'aria in tempo reale e fa previsioni su scala urbana in 6 città europee, tra cui Modena. Il progetto ha richiesto la gestione di una grande quantità di dati eterogenei e la loro integrazione su una piattaforma dati complessa e scalabile condivisa da tutti i partner del progetto. La piattaforma è un database PostgreSQL, adatto a gestire dati spazio-temporali, che contiene più di 60 tabelle e 435 GB di dati (solo per Modena). Tutti i processi della pipeline di TRAFAIR, le dashboard e le app sfruttano il database per ottenere i dati di input ed eventualmente memorizzare l'output. I modelli di simulazione, eseguiti su risorse di HPC, utilizzano i dati dei sensori e devono fornire risultati in tempo reale. Pertanto le tecniche di identificazione delle anomalie applicate ai dati dei sensori devono eseguire in tempo reale e in breve tempo. Dopo un attento studio della distribuzione dei dati dei sensori e della correlazione tra le misure, sono state implementate e applicate alcune tecniche di identificazione delle anomalie. Per i dati di traffico è stato sviluppato un nuovo approccio che utilizza un filtro di correlazione flusso-velocità, la decomposizione STL e l'analisi IQR. Per i dati di qualità dell'aria è stato creato un framework innovativo che implementa 3 algoritmi. I risultati degli esperimenti sono stati confrontati con quelli dell'Autoencoder LSTM. L'aspetto relativo alla sicurezza nella città intelligente è legato a un progetto di analisi dei crimini, i processi analitici volti a fornire informazioni tempestive e pertinenti per aiutare la polizia nella riduzione, prevenzione e valutazione del crimine. A causa della mancanza di dati ufficiali, questo progetto sfrutta le notizie pubblicate sui giornali online. L'obiettivo è quello di classificare le notizie in base alla categoria di crimine, geolocalizzare i crimini, identificare la data dell'evento, e individuare alcune caratteristiche. È stata sviluppata un'applicazione per l'analisi delle notizie, l'estrazione di informazioni semantiche attraverso l'uso di tecniche di NLP e la connessione delle entità a risorse Linked Data. La tecnologia dei Word Embedding è stata utilizzata per la categorizzazione del testo, mentre il Question Answering tramite BERT è stato utilizzato per estrarre le 5W+1H. Le notizie che si riferiscono allo stesso evento sono state identificate attraverso la cosine similarity sul testo delle notizie. Infine, è stata implementata un'interfaccia per mostrare su mappa i crimini geolocalizzati e fornire statistiche e rapporti annuali. Questo è l'unico progetto presente in Italia che partendo da notizie online cerca di fornire un'analisi sui crimini e la mette a disposizione attraverso uno strumento di visualizzazione.
A smart city is a place where technology is exploited to help public administrations make decisions. The technology can contribute to the management of multiple aspects of everyday life, offering more reliable services to citizens and improving the quality of life. However, technology alone is not enough to make a smart city; suitable methods are needed to analyze the data collected by technology and manage them in such a way as to generate useful information. Some examples of smart services are the apps that allow to reach a destination through the least busy road route or to find the nearest parking slot, or the apps that suggest better paths for a walk based on air quality. This thesis focuses on two aspects of smart cities: sustainability and safety. The first aspect concerns studying the impact of vehicular traffic on air quality through the development of a network of traffic and air quality sensors, and the implementation of a chain of simulation models. This work is part of the TRAFAIR project, co-financed by the European Union, which is the first project with the scope of monitoring in real-time and predicting air quality on an urban scale in 6 European cities, including Modena. The project required the management of a large amount of heterogeneous data and their integration on a complex and scalable data platform shared by all the partners of the project. The data platform is a PostgreSQL database, suitable for dealing with spatio-temporal data, and contains more than 60 tables and 435 GB of data (only for Modena). All the processes of the TRAFAIR pipeline, the dashboards and the mobile apps exploit the database to get the input data and, eventually, store the output, generating big data streams. The simulation models, executed on HPC resources, use the sensor data and provide results in real-time (as soon as the sensor data are stored in the database). Therefore, the anomaly detection techniques applied to sensor data need to perform in real-time in a short time. After a careful study of the distribution of the sensor data and the correlation among the measurements, several anomaly detection techniques have been implemented and applied to sensor data. A novel approach for traffic data that employs a flow-speed correlation filter, STL decomposition and IQR analysis has been developed. In addition, an innovative framework that implements 3 algorithms for anomaly detection in air quality sensor data has been created. The results of the experiments have been compared to the ones of the LSTM autoencoder, and the performances have been evaluated after the calibration process. The safety aspect in the smart city is related to a crime analysis project, the analytical processes directed at providing timely and pertinent information to assist the police in crime reduction, prevention, and evaluation. Due to the lack of official data to produce the analysis, this project exploits the news articles published in online newspapers. The goal is to categorize the news articles based on the crime category, geolocate the crime events, detect the date of the event, and identify some features (e.g. what has been stolen during the theft). A Java application has been developed for the analysis of news articles, the extraction of semantic information through the use of NLP techniques, and the connection of entities to Linked Data. The emerging technology of Word Embeddings has been employed for the text categorization, while the Question Answering through BERT has been used for extracting the 5W+1H. The news articles referring to the same event have been identified through the application of cosine similarity to the shingles of the news articles' text. Finally, a tool has been developed to show the geolocalized events and provide some statistics and annual reports. This is the only project in Italy that starting from news articles tries to provide analyses on crimes and makes them available through a visualization tool.
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Wonta, Yonatan. « Performance analysis and modeling of FCIP san traffic in mixed priority IP traffics ». Thesis, Wichita State University, 2012. http://hdl.handle.net/10057/5544.

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Fibre Channel (FC) is a high-speed data transfer technology used in communication between storage subsystems and computing devices commonly called Storage Area Networks (SAN). A Fibre Channel over TCP/IP enables the connection of FC-SANs isolated by IP networks. This thesis examines the FCIP protocol which is used as FC SAN extensions for SANs geographically separated by Wide Area IP networks. The performance of synchronous mirroring is examined between two SAN islands when operating though an IP network. A mathematical model is developed based on a series of experiments which denotes the characteristics of the FCIP protocol under the presence of varying load mixed IP traffics such as voice and video. The author also investigates performance improvement mechanisms using Multi-Protocol Label Switching (MPLS) and IP Quality of Service (QoS). The application of IP QoS to classify data based on priority is used to differentiate mission-critical applications and to apply the appropriate priority schemes. The use of MPLS enables core networks to function with a packet exchange speed closer to that offered by layer 2 switching. This thesis presents a comparison of the real time values against the modeled values. The performance boost observed using IP QoS and MPLS is also presented in this study.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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GRIMAUDO, LUIGI. « Data Mining Algorithms for Internet Data : from Transport to Application Layer ». Doctoral thesis, Politecnico di Torino, 2014. http://hdl.handle.net/11583/2537089.

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Nowadays we live in a data-driven world. Advances in data generation, collection and storage technology have enabled organizations to gather data sets of massive size. Data mining is a discipline that blends traditional data analysis methods with sophisticated algorithms to handle the challenges posed by these new types of data sets. The Internet is a complex and dynamic system with new protocols and applications that arise at a constant pace. All these characteristics designate the Internet a valuable and challenging data source and application domain for a research activity, both looking at Transport layer, analyzing network tra c flows, and going up to Application layer, focusing on the ever-growing next generation web services: blogs, micro-blogs, on-line social networks, photo sharing services and many other applications (e.g., Twitter, Facebook, Flickr, etc.). In this thesis work we focus on the study, design and development of novel algorithms and frameworks to support large scale data mining activities over huge and heterogeneous data volumes, with a particular focus on Internet data as data source and targeting network tra c classification, on-line social network analysis, recommendation systems and cloud services and Big data.
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Lindberg, Gunnar. « Valuation and pricing of traffic safety / ». Örebro : Universitetsbiblioteket : Örebro University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-787.

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Caldwell, Sean W. « On Traffic Analysis of 4G/LTE Traffic ». Cleveland State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=csu1632179249187618.

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Ruffing, Nicholas Luke. « Analysis of Smartphone Traffic ». Cleveland State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=csu1430150923.

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Wegner, Douglas Michael. « AMCROSS message traffic analysis ». Master's thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-12232009-020318/.

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De, Albuquerque Filho Emilio Alverne Falcão. « Analysis of airspace traffic structure and air traffic control techniques ». Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76097.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 157-163).
Air traffic controller cognitive processes are a limiting factor in providing safe and efficient flow of traffic. Therefore, there has been work in understanding the factors that drive controllers decision-making processes. Prior work has identified that the airspace structure, defined by the reference elements, procedural elements and pattern elements of the traffic, is important for abstraction and management of the traffic. This work explores in more detail this relationship between airspace structure and air traffic controller management techniques. This work looks at the current National Airspace System (NAS) and identifies different types of high altitude sectors, based on metrics that are likely to correlate with tasks that controllers have to perform. Variations of structural patterns, such as flows and critical points were also observed. These patterns were then related to groupings by origins and destinations of the traffic. Deeper pilot-controller voice communication analysis indicated that groupings by flight plan received consistent and repeatable sequences of commands, which were identified as techniques. These repeated modifications generated patterns in the traffic, which were naturally associated with the standard flight plan groupings and their techniques. The identified relationship between flight plan groupings and management techniques helps to validate the grouping structure-base abstraction introduced by Histon and Hansman (2008). This motivates the adoption of a grouping-focused analysis of traffic structures on the investigation of how new technologies, procedures and concepts of operations will impact the way controllers manage the traffic. Consideration of such mutual effects between structure and controllers' cognitive processes should provide a better foundation for training and for engineering decisions that include a human-centered perspective.
by Emilio Alverne Falcão de Albuquerque Filho.
S.M.
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Shi, Ping. « WWW traffic analysis and simulation ». Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ35527.pdf.

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Livres sur le sujet "Traffic analysi"

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American Telephone and Telegraph Company., dir. Basic traffic analysis. Englewood Cliffs, N.J : Prentice Hall, 1994.

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United States. Federal Highway Administration. Office of Research, Development, and Technology. Traffic analysis toolbox. McLean, VA : U.S. Dept. of Transportation, Federal Highway Administration, Research, Development, and Technology, 2004.

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United States. Federal Highway Administration. Office of Research, Development, and Technology., dir. Traffic analysis toolbox. McLean, VA : U.S. Department of Transportation, Federal Highway Administration, Research, Development, and Technology, 2004.

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Fletcher, Peter, Alex Poon, Ben Pearce et Peter Comber. Practical Web Traffic Analysis. Berkeley, CA : Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-5366-2.

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Steiner, Moritz, Pere Barlet-Ros et Olivier Bonaventure, dir. Traffic Monitoring and Analysis. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17172-2.

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Dainotti, Alberto, Anirban Mahanti et Steve Uhlig, dir. Traffic Monitoring and Analysis. Berlin, Heidelberg : Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54999-1.

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Domingo-Pascual, Jordi, Yuval Shavitt et Steve Uhlig, dir. Traffic Monitoring and Analysis. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20305-3.

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Pescapè, Antonio, Luca Salgarelli et Xenofontas Dimitropoulos, dir. Traffic Monitoring and Analysis. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28534-9.

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Papadopouli, Maria, Philippe Owezarski et Aiko Pras, dir. Traffic Monitoring and Analysis. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01645-5.

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Ricciato, Fabio, Marco Mellia et Ernst Biersack, dir. Traffic Monitoring and Analysis. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12365-8.

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Chapitres de livres sur le sujet "Traffic analysi"

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Gazis, Denos C. « Traffic Analysis ». Dans Encyclopedia of Operations Research and Management Science, 1564–70. Boston, MA : Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1054.

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Weik, Martin H. « traffic analysis ». Dans Computer Science and Communications Dictionary, 1803. Boston, MA : Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_19817.

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Coull, Scott E. « Traffic Analysis ». Dans Encyclopedia of Cryptography and Security, 1311–13. Boston, MA : Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_120.

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Treiber, Martin, et Arne Kesting. « Stability Analysis ». Dans Traffic Flow Dynamics, 257–301. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32460-4_15.

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Muhlrad, Nicole, Gilles Vallet, Ilona Butler, Victoria Gitelman, Etti Doveh, Emmanuelle Dupont, Heike Martensen et al. « Analysis of Road Safety Management Systems in Europe ». Dans Traffic Safety, 1–17. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307853.ch1.

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Salter, R. J. « Traffic assignment ». Dans Highway Traffic Analysis and Design, 63–70. London : Macmillan Education UK, 1996. http://dx.doi.org/10.1007/978-1-349-13423-6_9.

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Salter, R. J. « Traffic assignment ». Dans Highway Traffic Analysis and Design, 64–69. London : Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-20014-6_9.

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Foley, Alan, et Bob Regan. « Web Traffic Analysis ». Dans The Web Professional’s Handbook, 372–93. Berkeley, CA : Apress, 2003. http://dx.doi.org/10.1007/978-1-4302-5362-4_12.

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Fernandez, Rodrigo, Muhammad Haroon Yousaf, Timothy J. Ellis, Zezhi Chen et Sergio A. Velastin. « Traffic Flow Analysis ». Dans Computer Vision and Imaging in Intelligent Transportation Systems, 131–62. Chichester, UK : John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118971666.ch6.

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Marczak, Florian, Winnie Daamen et Christine Buisson. « Empirical Analysis of Lane Changing Behavior at a Freeway Weaving Section ». Dans Traffic Management, 139–51. Hoboken, NJ, USA : John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119307822.ch10.

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Actes de conférences sur le sujet "Traffic analysi"

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Babic, Dario, Darko Babic et Andelko Šcukanec. « The Impact of Road Familiarity on the Perception of Traffic Signs – Eye Tracking Case Study ». Dans Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.131.

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Traffic sign visual information provides road users with the basic instructions regarding route selection, safety at intersections, warnings on physical obstacles on the road and safe route marking. The use of sophisticated eye tracking systems is an efficient way to analyse the influence of traffic signs on drivers’ behaviour. In this paper, the drivers’ perception of traffics signs has been analysed using such a system. The aim of this paper is to determine how the perception of traffic signs changes according to the frequency of driving on a specific route or according to the route familiarity. The results show that the drivers’ perception of traffic signs declines as they get familiar with the route and road conditions. In addition, older drivers having more driving experience perceive fewer signs and elements from the environment because they are often led by their own experience and knowledge, so they do not need the same amount of information as compared to younger drivers.
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« [Front matter] ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002895.

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Gonzalez, Roberto, Lili Jiang, Mohamed Ahmed, Miriam Marciel, Ruben Cuevas, Hassan Metwalley et Saverio Niccolini. « The cookie recipe : Untangling the use of cookies in the wild ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002896.

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Grailet, Jean-Francois, et Benoit Donnet. « Towards a renewed alias resolution with space search reduction and IP fingerprinting ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002907.

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Wachs, Matthias, Quirin Scheitle et Georg Carle. « Push away your privacy : Precise user tracking based on TLS client certificate authentication ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002897.

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Traverso, Stefano, Martino Trevisan, Leonardo Giannantoni, Marco Mellia et Hassan Metwalley. « Benchmark and comparison of tracker-blockers : Should you trust them ? » Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002898.

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Manzoor, Jawad, Idilio Drago et Ramin Sadre. « How HTTP/2 is changing web traffic and how to detect it ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002899.

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Yeganeh, Bahador, Reza Rejaie et Walter Willinger. « A view from the edge : A stub-AS perspective of traffic localization and its implications ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002900.

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Scheitle, Quirin, Oliver Gasser, Minoo Rouhi et Georg Carle. « Large-scale classification of IPv6-IPv4 siblings with variable clock skew ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002901.

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Shah, Anant, Romain Fontugne, Emile Aben, Cristel Pelsser et Randy Bush. « Disco : Fast, good, and cheap outage detection ». Dans 2017 Network Traffic Measurement and Analysis Conference (TMA). IEEE, 2017. http://dx.doi.org/10.23919/tma.2017.8002902.

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Rapports d'organisations sur le sujet "Traffic analysi"

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Newman, Richard E., Ira S. Moskowitz, Paul Syverson et Andrei Serjantov. Metrics for Traffic Analysis Prevention. Fort Belvoir, VA : Defense Technical Information Center, janvier 2003. http://dx.doi.org/10.21236/ada465471.

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Lakhina, Anukool, Konstantina Papagiannaki, Mark Crovella, Christophe Diot, Eric D. Kolaczyk et Nina Taft. Structural Analysis of Network Traffic Flows. Fort Belvoir, VA : Defense Technical Information Center, novembre 2003. http://dx.doi.org/10.21236/ada439086.

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Crovella, Mark, et Eric Kolaczyk. Graph Wavelets for Spatial Traffic Analysis. Fort Belvoir, VA : Defense Technical Information Center, juillet 2002. http://dx.doi.org/10.21236/ada442573.

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Parker, Jr, Mosca Raymond et Carl. Preliminary First Destination Guaranteed Traffic Cost Analysis. Fort Belvoir, VA : Defense Technical Information Center, mars 1986. http://dx.doi.org/10.21236/ada170706.

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Lee, Daniel C. Reliability Analysis of Networks Carrying Critical Mission Traffic. Fort Belvoir, VA : Defense Technical Information Center, janvier 1998. http://dx.doi.org/10.21236/ada336880.

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Liu, Hongrui, et Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, novembre 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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Benke, G., J. Brandt, H. Chen, S. Dastangoo et G. J. Miller. Performance analysis of ATM ABR service under self-similar traffic in the presence of background VBR traffic. Office of Scientific and Technical Information (OSTI), mai 1996. http://dx.doi.org/10.2172/373938.

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Lam, Warren M., et Gregory W. Wornell. Multiscale Analysis and Control of Networks with Fractal Traffic. Fort Belvoir, VA : Defense Technical Information Center, octobre 1998. http://dx.doi.org/10.21236/ada457839.

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Mears, Brad. Advanced Traffic Management Systems (ATMS) Research Analysis Database System. Fort Belvoir, VA : Defense Technical Information Center, juin 2001. http://dx.doi.org/10.21236/ada388177.

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Ringhand, Madlen, Maximilian Bäumler, Christian Siebke, Marcus Mai et Felix Elrod. Report on validation of the stochastic traffic simulation (Part A). Technische Universität Dresden, 2021. http://dx.doi.org/10.26128/2021.242.

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This document is intended to give an overview of the human subject study in a driving simulator that was conducted by the Chair of Traffic and Transportation Psychology (Verkehrspsychologie – VPSY) of the Technische Universität Dresden (TUD) to provide the Chair of Automotive Engineering (Lehrstuhl Kraftfahrzeugtechnik – LKT) of TUD with the necessary input for the validation of a stochastic traffic simulation, especially for the parameterization, consolidation, and validation of driver behaviour models. VPSY planned, conducted, and analysed a driving simulator study. The main purpose of the study was to analyse driving behaviour and gaze data at intersections in urban areas. Based on relevant literature, a simulated driving environment was created, in which a sample of drivers passed a variety of intersections. Considering different driver states, driving tasks, and traffic situations, the collected data provide detailed information about human gaze and driving behaviour when approaching and crossing intersections. The collected data was transferred to LKT for the development of the stochastic traffic simulation.
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