Modelování dopravního toku s využitím moderních stochastických nástrojů

The importance of traffic state prediction steadily increases together with growing volume of traffic. The ability to predict traffic speed and density in short to medium horizon is one of the main tasks of every Intelligent Transportation System. This prediction can be used to manage the traffic both to prevent the traffic congestions and to minimize their impact. This information is also useful for route planning. Traffic state prediction is not an easy task given that the traffic flow is very difficult to describe by numerical equations. Other possible approach to traffic state prediction is to use historical data about the traffic and relate them to the current state by application of some form of statistical approach. This task is, however, complicated by complex nature of the traffic data, which can, due to various reasons, be quite inaccurate. This thesis is focused on finding the algorithms that can exploit valuable information contained in traffic data from Czech Republic highways to make a short-term traffic speed predictions. My proposed algorithms are based on modern stochastic approaches like hidden Markov models, dynamic Bayesian networks, ensemble Kalman filters, Monte Carlo simulation and Markov chains. These models are naturally able to capture all complexities in the traffic and incorporate uncertainty of the traffic dataSpolu s rostoucím objemem provozu narůstá význam předpovědi stavu dopravního provozu. Schopnost předvídat dopravní rychlost a hustotu v krátkém až střednědobém horizontu je jednou z hlavních úkolů každého systému pro řízení dopravy. Tato predikce může být použita k řízení provozu, a to jak k prevenci vzniku dopravních zácp, tak k minimalizaci jejich dopadu. Tyto informace jsou také užitečné pro plánování jízdních tras. Předpověď stavu dopravního provozu není snadným úkolem, protože dopravní tok je velmi obtížné popsat numerickými rovnicemi. Dalším možným přístupem k předpovědi provozního stavu je použití historických údajů o provozu a jejich propojení s aktuálním stavem pomocí vhodného statistického přístupu. Tento úkol však komplikuje složitá povaha dopravních dat, která mohou být z různých důvodů poměrně nepřesná. Tato práce je zaměřena na nalezení algoritmů, které mohou využívat cenné informace obsažené v dopravních údajích z dálnic ČR za účelem vytvoření krátkodobých předpovědí rychlosti provozu. Mnou navrhované algoritmy jsou založeny na moderních stochastických přístupech jako jsou skryté Markovovy modely, dynamické bayesovské sítě a Kalmanovy filtry. Tyto modely dokáží přirozeně zachytit zákonitosti dopravního provozu a vzít v úvahu neznámou nejistotu zatěžující dopravní data.470 - Katedra aplikované matematikyvyhově

Využití dynamických grafů pro vyhledávání nejrychlejší cesty v dopravní síti

Import 04/07/2011This work deals with finding the fastest route in a dynamic graph representing road network. Its goal is to develop algorithms capable of finding such route. There will be three algorithms presented in this work: the first based on Dijkstra algorithm, the second based on A* algorithm and the third based on Ant colony optimization algorithm.Tato práce se zabývá hledáním nejrychlejší cesty v dynamickém grafu reprezentujícím silniční síť. Jejím cílem je navrhnout algoritmy, které by takovou cestu byly schopné najít. V práci budou k předloženy tři algoritmy řešící tento problém: jeden pracující na bázi Dijkstrova algoritmu, druhý pracující na bázi A* algoritmu a třetí pracující na bázi mravenčích algoritmů.470 - Katedra aplikované matematikyvýborn

Real time traffic simulator for self-adaptive navigation system validation

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The data extraction using distributed crawler inside multi-agent system

The paper discusses the use of web crawler technology. We created an application based on standard web crawler. Our application is determined for data extraction. Primarily, the application was designed to extract data using keywords from a social network Twitter. First, we created a standard crawler, which went through a predefined list of URLs and gradually download page content of each of the URLs. Page content was then parsed and important text and metadata were stored in a database. Recently, the application was modified in to the form of the multi-agent system. The system was developed in the C# language, which is used to create web applications and sites etc. Obtained data was evaluated graphically. The system was created within Indect project at the VSB-Technical University of Ostrava

Smoothing of the curves in the plain

Tato práce se zabývá vyhlazováním dat získaných z měření nitroděložního tlaku. Jejím cílem je vyhladit data, která jsou narušená neočekávanými událostmi. Použijeme k tomu čtyři metody: Interpolaci pomocí Spline funkce, Bézierovu křivku, Klouzavý průměr a Vyhlazování těžišti.This work deals with smoothing data from measuring of uterine pressure. Its goal is to smoothen the data which are disrupted by some unexpected events. We are using four methods for smoothing: Spline interpolation, Bézier curve, Moving average and Smoothing by gravity centres.Prezenční457 - Katedra aplikované matematikyvýborn

Traffic speed prediction using ensemble kalman filter and differential evolution

Importance of traffic state prediction steadily increases with growing volume of traffic. Ability to predict traffic speed in short to medium horizon (i.e. up to one hour) is one of the main tasks of every newly developed Intelligent Transportation System. There are two possible approaches to this prediction. The first is to utilize physical properties of the traffic flow to construct an exact or approximate numerical model. This approach is, however, almost impossible to implement on a larger scale given the difficulty to obtain enough traffic data to describe the starting and boundary conditions of the model. The other option is to use historical traffic data and relate information and patterns they contain to the current traffic state by application of some form of statistical or machine learning approach. We propose to use combination of Ensemble Kalman filter and Cell Transmission Model for this task. These models combine properties of physical model with ability to incorporate uncertainty of the traffic data

New GNSS tomography of the atmosphere method – proposal and testing

Paper is focused on GNSS meteorology which is generally used for the determination of water vapour distribution in the atmosphere from GNSS measurements. Water vapour in the atmosphere is an important parameter which influences the state and development of the weather. At first, the paper presents basics of the GNSS meteorology and tomography of the atmosphere and subsequently introduces a new GNSS tomography method which doesn't require an extensive network of GNSS receivers, but uses only a few receivers situated in a line. After a theoretical concept describing this method and used mathematical background, the results from a real experiment are shown and discussed. Unfortunately the results indicate that presented method is not able to provide credible outputs. Possibly the main problem lies in an insufficient number of available signals from current global navigation satellite systems (GPS and GLONASS) where the improvement could be expected after the start of Galileo and Compass. Potential ways how to improve the results without increasing the number of satellites are outlined in the last section

Use of the bio-inspired algorithms to find global minimum in force directed layout algorithms

We present bio-inspired approach in a process of finding global minimum of an energetic function that is used in force directed layout algorithms. We have been faced with the issue of displaying large graphs. These graphs arise in the analysis of social networks with the need to view social relationships between entities. In order to find global minimum of an energetic function we employ two bio-inspired algorithms: Differential Evolution and Self- Organizing Migration Algorithm (SOMA). Differential evolution is inspired by crossbreeding of population whereas SOMA is inspired by migration of some species. In this article we will present basics of these algorithms, their results and comparison

Performance Evaluation of Probabilistic Time-Dependent Travel Time Computation

Part 6: AlgorithmsInternational audienceComputational performance of route planning algorithms has become increasingly important in recent real navigation applications with many simultaneous route requests. Navigation applications should recommend routes as quickly as possible and preferably with some added value. This paper presents a performance evaluation of the main part of probabilistic time-dependent route planning algorithm. The main part of the algorithm computes the full probability distribution of travel time on routes with Monte Carlo simulation. Experiments show the performance of the algorithm and suggest real possibilities of use in modern navigation applications

Comparison of ASIM Traffic Profile Detectors and Floating Car Data During Traffic Incidents

Part 2: AlgorithmsInternational audienceIntelligent Transportation Systems are highly dependent on the quality and quantity of road traffic data. The complexity of input data is often crucial for effectiveness and sufficient reliability of such systems. Recent days, the fusion of various data sources is the topic which attracts attention of several researchers. The algorithms for data fusion take benefit of the advantages and disadvantages of each technology, resulting in an optimal solution for traffic management problems. The paper is focused on finding relations between two main data sources, floating car data and ASIM traffic profile detectors. Time series of speed and other information obtained from these data sources were analysed by Granger causality with intention to use both data sources efficiently for traffic monitoring and control during traffic incidents