Tibor Vadvári

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Publications - 4

Mathematical Description of the Universal IDM - some Comments and Application

Tibor Vadvári István Lakatos Krisztián Szabó András Háry Tamas Péter Ferenc Szauter

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2023-01-01

Volume: 20

Issue: 7

Page Range: 99-115

Description:

The aim of the study is to define and mathematically describe the universal IDM. An important result of this research is that the model uses a single system of differential equations. It is able to simultaneously describe the dynamic operations of the IDM systems for all different vehicle sequences. The aim of the study is to support the driving of autonomous vehicles by taking into account the dynamic variations in the state characteristics of traffic processes. The approach used is motivated by important issues in current modelling techniques that address significant economic problems in the application of large-scale ITS network models. This also points to a new opportunity in the key area of vehicle traffic management, in the related targeted fundamental research, particularly in the analysis of traffic processes in large-scale dynamic networks.

Open Access: Yes

DOI: 10.12700/APH.20.7.2023.7.6

Analysis of network traversal and qualification of the testing values of trajectories

Tibor Vadvári István Lakatos Krisztián Szabó András Háry Tamás Péter Ferenc Szauter

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2021-01-01

Volume: 18

Issue: 10

Page Range: 151-171

Description:

This research work is aimed directly at the study of network traversal, for the design of vehicle dynamics and driving test programs. The work can be applied more widely to the qualification of test tracks. In addition to general modelling, this method can also be used to investigate the formation of loops in order to take into account, the sub-routes, multiple times. An important area of its application is the more comprehensive analysis of complex loads, as well as, the development of learning algorithms, which can be achieved by repeating multiple traversals of certain track sections within a series of measurements, and can be used for the development of on-board vehicle systems. The mathematical modelling is presented through the application of the geometric graph and subgraphs of the track. The properties of the Markov model extracted from the connection matrix of the large-scale network model are also presented. In this way, the modelling is extended to the application of the connection matrix of the large-scale network model as well. The modelling and computational details are demonstrated by means of a computer based, algebraic program. This modelling and the results of the calculations, will allow the further development of a test program design and related evaluation methods.

Open Access: Yes

DOI: DOI not available

Identification of supply chains based on input-output data

Tibor Vadvári P. Várlaki

Publication Name: Periodica Polytechnica Transportation Engineering

Publication Date: 2015-01-01

Volume: 43

Issue: 3

Page Range: 162-167

Description:

The paper focuses on supply chain modeling issues, namely how subspace identification techniques can be used to characterize the strength of relations between certain system parameters. This might be useful when no knowledge about the internal workings or inner structure of the system is available, thus only blackbox like approaches can be utilized. Here let us show how supply chains can be identified and modeled by deterministic linear state space models and how the accuracy of the identified model reflects the relation between certain system parameters.

Open Access: Yes

DOI: 10.3311/PPtr.7931

Neural-network based modelling approach for loading systems

Gabriella Orbán Tibor Vadvári András Rövid P. Várlaki

Publication Name: International Conference on Emerging Trends in Engineering and Technology Icetet

Publication Date: 2011-12-01

Volume: Unknown

Issue: Unknown

Page Range: 72-76

Description:

As in many fields in modern logistics the system modelling and identification play an important role. By the modelling of complex non-linear systems different model approximation approaches are utilized. The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a higher order singular value decomposition (HOSVD) based approximation approach for neural network (NN) model approximation is introduced. The approach will be detailed from the point of view of logistic systems but it may be applicable for other fields, as well. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the HOSVD. © 2011 IEEE.

Open Access: Yes

DOI: 10.1109/ICETET.2011.69