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Multi-objective genetic algorithms in road traffic prediction
Petrlík, Jiří ; Brandejský, Tomáš (oponent) ; Snášel,, Václav (oponent) ; Sekanina, Lukáš (vedoucí práce)
The understanding of the road traffic behavior is a key to effective traffic control, management and organization. This task is becoming more and more important with increasing traffic demands and the number of registered vehicles. The information about the current and future traffic situation is very important for drivers and traffic operators. Fortunately, there was a huge progress in technologies for traffic data acquisition in the last few decades. Stationary sensors, such as loop detectors, radars, cameras and infrared sensors can be installed on important locations of the roads and measure various microscopic and macroscopic traffic variables. However, some measurements can lead to an incorrect data which cannot further be used in the subsequent processing tasks such as traffic prediction or intelligent control. For example, this can be caused by equipment failures or data transmission problems. It is highly desirable to have a framework, which is capable of estimating the missing values in traffic data. It is also very important to provide a reliable short-time prediction of the traffic state. In this thesis, we focus on selected problems from this domain - the imputation of missing traffic data, short time traffic forecasting and travel times estimation. The proposed solution is based on combining the state-of-the art machine learning methods such as support vector regression (SVR) with the multi-objective evolutionary optimization. SVR has various meta-parameters which should be properly set in order to achieve the best performance. The performance also strongly depends on the selection of the input variables for SVR. We used the multi-objective optimization to find the proper settings of SVR meta-parameters and input variables. Using the multi-objective optimization, we obtained many different non-dominated solutions from Pareto front. These solutions can dynamically be switched according to the traffic data which are currently available, in order to maximize the quality of prediction. The proposed methods are specially designed for environments with many missing values in traffic data. We evaluated the proposed methods using real world data and compared them with the state of the art methods for the traffic data imputation and short term prediction such as the probabilistic principal component analysis and support vector regression optimized by a single objective optimization. The proposed methods provide better results than these state of the art methods especially in the cases where there are many missing values in the traffic data.
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Paralelní evoluční algoritmus EDA využívající teorii kopulí
Hyrš, Martin ; Brandejský, Tomáš (oponent) ; Matoušek, Radomil (oponent) ; Schwarz, Josef (vedoucí práce)
Ve své disertační práci se zabývám návrhem, implementací a~testováním pokročilého paralelního algoritmu EDA ( Estimation of Distribution Algorithm ) využívajícího teorii kopulí pro tvorbu pravděpodobnostního modelu. Nová populace se vytváří v~procesu vzorkování sdružené distribuční funkce, která modeluje aktuální rozložení subpopulace slibných jedinců. Použití kopulí umožňuje zefektivnit proces učení a~vzorkování pravděpodobnostního modelu. Lze jej separovat na vzájemně nezávislá marginální rozdělení a~kopuli, která reprezentuje korelace mezi proměnnými řešeného problému. Tato koncepce iniciovala použití paralelní ostrovní struktury, v~níž bylo použito místo migrace jedinců migrace pravděpodobnostních modelů příslušejících jednotlivým ostrovním subpopulacím. Statistické testy použité při komparaci navrženého algoritmu ( mCEDA = migrating Copula - based Estimation of Distribution Algorithm ) a~algoritmů jiných autorů potvrdily efektivnost navržené koncepce.
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Paralelní evoluční algoritmus EDA využívající teorii kopulí
Hyrš, Martin ; Brandejský, Tomáš (oponent) ; Matoušek, Radomil (oponent) ; Schwarz, Josef (vedoucí práce)
Ve své disertační práci se zabývám návrhem, implementací a~testováním pokročilého paralelního algoritmu EDA ( Estimation of Distribution Algorithm ) využívajícího teorii kopulí pro tvorbu pravděpodobnostního modelu. Nová populace se vytváří v~procesu vzorkování sdružené distribuční funkce, která modeluje aktuální rozložení subpopulace slibných jedinců. Použití kopulí umožňuje zefektivnit proces učení a~vzorkování pravděpodobnostního modelu. Lze jej separovat na vzájemně nezávislá marginální rozdělení a~kopuli, která reprezentuje korelace mezi proměnnými řešeného problému. Tato koncepce iniciovala použití paralelní ostrovní struktury, v~níž bylo použito místo migrace jedinců migrace pravděpodobnostních modelů příslušejících jednotlivým ostrovním subpopulacím. Statistické testy použité při komparaci navrženého algoritmu ( mCEDA = migrating Copula - based Estimation of Distribution Algorithm ) a~algoritmů jiných autorů potvrdily efektivnost navržené koncepce.
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Multi-objective genetic algorithms in road traffic prediction
Petrlík, Jiří ; Brandejský, Tomáš (oponent) ; Snášel,, Václav (oponent) ; Sekanina, Lukáš (vedoucí práce)
The understanding of the road traffic behavior is a key to effective traffic control, management and organization. This task is becoming more and more important with increasing traffic demands and the number of registered vehicles. The information about the current and future traffic situation is very important for drivers and traffic operators. Fortunately, there was a huge progress in technologies for traffic data acquisition in the last few decades. Stationary sensors, such as loop detectors, radars, cameras and infrared sensors can be installed on important locations of the roads and measure various microscopic and macroscopic traffic variables. However, some measurements can lead to an incorrect data which cannot further be used in the subsequent processing tasks such as traffic prediction or intelligent control. For example, this can be caused by equipment failures or data transmission problems. It is highly desirable to have a framework, which is capable of estimating the missing values in traffic data. It is also very important to provide a reliable short-time prediction of the traffic state. In this thesis, we focus on selected problems from this domain - the imputation of missing traffic data, short time traffic forecasting and travel times estimation. The proposed solution is based on combining the state-of-the art machine learning methods such as support vector regression (SVR) with the multi-objective evolutionary optimization. SVR has various meta-parameters which should be properly set in order to achieve the best performance. The performance also strongly depends on the selection of the input variables for SVR. We used the multi-objective optimization to find the proper settings of SVR meta-parameters and input variables. Using the multi-objective optimization, we obtained many different non-dominated solutions from Pareto front. These solutions can dynamically be switched according to the traffic data which are currently available, in order to maximize the quality of prediction. The proposed methods are specially designed for environments with many missing values in traffic data. We evaluated the proposed methods using real world data and compared them with the state of the art methods for the traffic data imputation and short term prediction such as the probabilistic principal component analysis and support vector regression optimized by a single objective optimization. The proposed methods provide better results than these state of the art methods especially in the cases where there are many missing values in the traffic data.
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