The aim of this thesis is to observe behavior of traffic flow and to verify or refute traditional so called fundamental relationships. It analyze data based on older measurements, personal measurements executed on the I/43 road, and mathematical models.There were three tools used provided by "VUT" in Brno as well as own developed tool based on cellular automat..

Emerging sensors and intelligent traffic technologies provide extensive data sets in a traffic network. However, realizing the full potential of such data sets for a unique representation of real-world states is challenging due to data accuracy, noise, and temporal-spatial resolution. Data assimilation is a known group of methodological approaches that exploit physics-informed traffic models and data observations to perform short-term predictions of the traffic state in freeway environments. At the same time, neural networks capture high non-linearities, similar to those presented in traffic networks. Despite numerous works applying different variants of Kalman filters, the possibility of traffic state estimation with deep-learning-based methodologies is only partially explored in the literature. We present a deep-learning modeling approach to perform traffic state estimation on large freeway networks. The proposed framework is trained on local observations from static and moving sensors and identifies differences between well-trusted data and model outputs. The detected patterns are then used throughout the network, even where there are no available observations to estimate fundamental traffic quantities. The preliminary results of the work highlight the potential of deep learning for traffic state estimation.

Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have / Ministry of Higher Education in Egypt and the Egyptian Cultural Centre and Educational Bureau in London

Neste trabalho, redes neurais dinÃmicas sÃo avaliadas como modelos nÃo-lineares eficientes para prediÃÃo de sÃries temporais complexas. Entre as arquiteturas avaliadas estÃo as redes FTDNN, Elman e NARX. A capacidade preditiva destas redes sÃo testadas em tarefas de prediÃÃo de um-passo-adiante e mÃltiplos-passos-adiante. Para este fim, sÃo usadas as seguintes sÃries temporais: sÃrie laser caÃtico, sÃrie caÃtica Mackey-Glass, alÃm de sÃries de trÃfego de rede de computadores com caracterÃsticas auto-similares. O uso da rede NARX em prediÃÃo de sÃries temporais à uma contribuiÃÃo desta dissertaÃÃo. Esta rede possui uma arquitetura neural recorrente usada originalmente para identificaÃÃo entrada-saÃda de sistemas nÃo-lineares. A entrada da rede NARX à formada por duas janelas deslizantes (sliding time window), uma que desliza sobre o sinal de entrada e outra que desliza sobre sinal de saÃda. Quando aplicada para prediÃÃo caÃtica de sÃries temporais, a rede NARX à projetada geralmente como um modelo autoregressivo nÃolinear (NAR), eliminando a janela de atraso da saÃda. Neste trabalho, à proposta uma estratÃgia simples, porÃm eficiente, para permitir que a rede NARX explore inteiramente as janelas de tempo da entrada e da saÃda, a fim de melhorar sua capacidade preditiva. Os resultados obtidos mostram que a abordagem proposta tem desempenho superior ao desempenho apresentado por preditores baseados nas redes FTDNN e Elman.

This Master's Thesis deals with the possibilities for the practical usage of traffic simulations as tools for seeking solutions to various kinds of problems such as road traffic flow, the environmental impact of traffic, etc. It contains a description of the basic terms in the area of traffic simulation models, advantages of their practical usage, and furthermore, it describes the basic general methods of and attitudes to the most commonly used traffic simulations. In the second part, the analysis and the design of an original micro traffic simulator are included. Finally, the traffic simulator is implemented and tested on a real traffic situation.

[pt] Com o crescimento da utilização das redes celulares para a transmissão de dados, as operadoras de telefonia móvel estão enfrentando um aumento dramático na utilização do recurso rádio para suprir esta nova necessidade. Com isto, estão sendo obrigadas a migrarem para novas tecnologias que propiciem uma alta eficiência espectral, permitindo assim uma melhor utilização do espectro disponível. Este trabalho descreve uma ferramenta de simulação desenvolvida para analisar o gerenciamento dos recursos de rádio, além de propor um novo algoritmo de alocação de canais de dados para sistemas móveis pessoais, permitindo a transmissão de voz e dados sobre a mesma interface aérea. É sabido que é extremamente difícil levar em conta todos os parâmetros que devem ser considerados em um sistema móvel real. Procurou-se então adotar os mais conhecidos modelos de propagação, de mobilidade e de tráfego de voz e de dados disponíveis na literatura, de forma a tentar aproximar a ferramenta de simulação o máximo possível do caso real. A contribuição deste trabalho é oferecer uma ferramenta de simulação para as redes GSM/HSCSD que permita a otimização da eficiência no uso do espectro disponível para transmissão. Esta ferramenta possibilita ao usuário, além de analisar a capacidade do sistema, o controle de potência e estratégias de handover, avaliar novos algoritmos de alocação de canal para o tráfego de voz e dados em conjunto. Ao final desta dissertação serão apresentados os resultados obtidos com a utilização de um novo algoritmo de alocação de canais para o tráfego de dados, além de realizar algumas outras simulações buscando maximizar a eficiência espectral do sistema utilizando deste novo algoritmo. / [en] Due to the increasing use of cellular networks for data transmission, mobile telephone operators are facing a dramatic raise in using radio resources to supply their demand. As a result, they are being forced to migrate to new technologies that provide high spectral efficiency, allowing an optimized use of available spectrum. This work describes the development of an advanced tool to analyze the operator management of radio transmission for personal mobile systems. The model was built considering well-known propagation, mobility, voice and data traffic for transmission assumptions, in order to approximate the developed tool to the real world mobile systems. The simulation tool is suited for GSM/HSCSD networks allowing engineers to optimize the efficient use of the available spectrum. It also permits the evaluation of system capacity as well as the analysis of power control and handover strategies. Another application exploited in this work is the investigation of resource allocation algorithms. A new algorithm for resource allocation in integrated voice and data environments was proposed and its performance was evaluated using the simulation tool.