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Experimental comparison of traffic flow models on traffic data
Přikryl, Jan ; Horňák, Ivan
Despite their deficiencies, continuous second-order traffic flow models are still commonly used to derive discrete-time models that help traffic engineers to model and predict traffic oflow behaviour on highways. We brie fly overview the development of traffic flow theory based on continuous flow-density models of Lighthill-Whitham-Richards (LWR) type, that lead to the second-order model of Aw-Rascle. We will then concentrate on widely-adopted discrete approximation to the LWR model by Daganzo's Cell Transmission Model. Behaviour of the discussed models will be demonstrated by comparing the traffic flow prediction based on these models with real traffic data on the southern highway ring of Prague.
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Model for the project area NOMŘÍZ
Přikryl, Jan
This document describes the basic state model of the area used in the updated version of the control algorithm for the project NOMŘÍZ. The document is a shortened version of the internal uncollated nonpublic implementation document for internal use ÚTIA and ELTODO as.
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Statistical analysis and research on the links in the measured traffic data
Brabec, M. ; Konár, O. ; Kasanický, I. ; Pelikán, E. ; Přikryl, Jan
One of the current problems in transport research is the problem of availability of huge amounts of unused data measured telematics systems on motorways and urban roads. This data often contain information that - without the use of appropriate mathematical tools for data analysis - remain hidden and unused for traffic control. Within the project, a new method of traffic congestion in the urban areas, our aim is to propose new algorithms for adaptive control of urban transport networks. The current version of the algorithm is based on a linearized model of the development length of the queue of vehicles waiting at the red light, based on the classic balance of conservation. This model is for cases saturation supplemented alternative model queue length as a function of traffic occupancy detector. The aim of the work described in this report was to investigate possible links and other hidden states of the transport system, hidden in the measured data, and until now not considered by the traffic model used.
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Graphics card as a cheap supercomputer
Přikryl, Jan
The current powerful graphics cards, providing stunning real-time visual effects for computer-based entertainment, have to accommodate powerful hardware components that are able to deliver the photo-realistic simulation to the end-user. Given the vast computing power of the graphics hardware, its producers very often offer a programming interface that makes it possible to use the computational resources of the graphics processors (GPU) to more general purposes. This step gave birth to the so-called GPGPU (general-purpose GPU) processors that – if programmed correctly – are able to achieve astonishing performance in floating point operations. In this paper we will briefly overview nVidia CUDA technology and we will demonstrate a process of developing a simple GPGPU application both in the native GPGPU style and in the add-ons for Matlab (Jacket and Parallel Toolbox).
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Variational Bayes in Distributed Fully Probabilistic Decision Making
Šmídl, Václav ; Tichý, Ondřej
We are concerned with design of decentralized control strategy for stochastic systems with global performance measure. It is possible to design optimal centralized control strategy, which often cannot be used in distributed way. The distributed strategy then has to be suboptimal (imperfect) in some sense. In this paper, we propose to optimize the centralized control strategy under the restriction of conditional independence of control inputs of distinct decision makers. Under this optimization, the main theorem for the Fully Probabilistic Design is closely related to that of the well known Variational Bayes estimation method. The resulting algorithm then requires communication between individual decision makers in the form of functions expressing moments of conditional probability densities. This contrasts to the classical Variational Bayes method where the moments are typically numerical.
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