Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl IntroductionOperational quality measures of motor vehicles are used, among the others, to evaluate the performance of transport services. An important group of problems in making such an assessment is selection of the appropriate method. The operational evaluation of an object requires defining the measures (measurements, indicators) and the determining their values. The appropriate value allocation of the vehicles performance measures is one of the key criteria for the proper functioning of the whole transport system [9]. The numerical evaluation of the efficiency of the equipment is based on the values derived from the observation of the equipment during operation [10]. The variety of operational measures depends, of course, on the type of object (process), and usually these measures have different denominations and orders of scale, making them mutually incomparably [6,11].Comparing the measures describing an object (process) is only possible after normalization. Among the groups of technical objects' features relevant for their operational evaluation (determination of their measures and indicators) were distinguished, among the others [8]:technical condition of the object, being a measure of the ability • to use the object over time, reliability in statistical terms, • quality, understood as the ability of an object to meet specific • needs, functionality describing the object in the sphere of human con-• tact, efficiency characterizing the performance of an object, • serviceability characterizing the object's suitability to be serv-ŚWIDERSKI A, JÓŹWIAK A, JACHIMOWSKI R. Operational quality measures of vehicles applied for the transport services evaluation using artificial neural networks.

Article citation info: (*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl

This article uses Markov and semi-Markov models as some of the most popular tools to estimate readiness and reliability. They allow to evaluate of both individual elements as well as entire systems—including production systems—as multi-state structures. To be able to distinguish states with varying degrees of technical readiness in complicated and complex objects (systems) allows to determine their individual impact on the tasks performed, as well as on the total reliability. The application of the Markov process requires, for the process dwell times in the individual states, to be random variables of exponential distribution and the fulfilling Markov’s property of the independence of these states. Omitting these assumptions may lead to erroneous results, which was the authors’ intention to show. The article presents a comparison of the results of the examination of the process of non-parametric distribution with an analysis in which its exponential form was (groundlessly) assumed. Significantly different results were obtained. The aim was to draw attention to the inconsistencies obtained and to the importance of a preliminary assessment of the data collected for examination. The diagnostics of the machine readiness operating in the studied production company was additionally performed. This allowed to evaluate its operational potential, especially in the context of solving process optimization problems.

A special element of road safety research is accidents at the interface of the road and rail system. Due to their low share in the total number of incidents, they are not a popular subject of analyses but rather an element of collective studies, whereas the specificity of the road–rail accidents requires a separate characteristic, allowing, on the one hand, to categorize these types of incidents, and on the other, to specify the factors that affect them, along with an assessment of the strength of this impact. It is important to include in such analyses all potential predictors, both qualitative and quantitative. Moreover, the literature considers most often a number of accidents while, according to the authors, it does not fully reflect the scale of the danger. A better evaluation would be the victim’s degree of injury. Therefore, the purpose of this article is to assess the likelihood of occurrence of various effects of road–rail accidents in the aspect of selected factors. Due to the ordinal form of the dependent variable, the classification trees method was used. The results obtained not only allow the characterization and assessment of the danger but also constitute guidelines for taking preventive actions.

Science and Technology article citation info:(*) Tekst artykułu w polskiej wersji językowej dostępny w elektronicznym wydaniu kwartalnika na stronie www.ein.org.pl Effective fleet management is related to the care for their rational use and proper diagnostics. Early detection of potential irregularities enables to prevent failures and carry out transport processes in an undisturbed way. One of the most important components, from the safety point of view, is the braking system. Laboratory tests can be used to determine the durability characteristics of individual components. Individual indications referring to operating conditions would be most desirable. The article, based on a two-year period of testing of a group of Renault vehicles fitted with disc brakes, presents measurement of wearing and tearing the system components (brake discs and brake friction insert) in a function of selected factors, depending on the time and environment in which the transport was carried out. Nonparametric statistical tests were used to analyze the results. Mann-Whitney and Kruskal-Wallis tests were used to verify the hypothesis on the insignificance of differences. Their results were compared with the results of ANOVA variance analysis. The significance of factors influencing the degree of brakes wear was checked. Possible directions of using the results of brake wear measurement for rationalization of transport processes were also indicated. Presented method may also be applied to the evaluation of other components (assemblies, subassemblies, systems) of motor vehicles.