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In this study we conduct a large survey (n=6,405) on urban daily commuters in four European countries (Germany, the United Kingdom, Poland, and the Czech Republic), we add richness to our understanding of who will use Mobility-as-a-Service (MaaS) options and what relationship MaaS has with public transit in terms of choosing how to travel. We explored how sensitive participants were to changes in pricing and travel times, and also examined how likely different kinds of participants, grouped into segments (user groups) with similar characterizes, were to public transportation, MaaS, or private cars. We concluded that the basic mobility factors such as price and travel time can increase the likelihood of using a certain option, as we notably observed in the “Unspecified Users” group. Our complex approach, combining several mathematical modelling tools, provided insight into the choices people make into choosing among different travel options, and our findings may be leveraged by decision makers to create better, more informed approaches towards promoting sustainable alternatives to car use in urban settings.

The aim of the study was to ascertain the incidence, circumstances and causes of sudden cardiac death in persons aged 1-40 years in the Czech Republic.De-identified autopsy reports of all individuals who died suddenly between the ages of 1-40 years during the period 2014-2019 inclusive in a selected area of the Czech Republic were analysed retrospectively. Persons with substantial cardiovascular pathology defined by histopathological criteria and those with a negative autopsy were included in the study. The latter were designated as sudden arrhythmic death syndrome.In total, 245 sudden cardiac death cases were identified resulting in an incidence rate of 2.4/100,000 person-years. Among the deceased, we found an enormous gender gap with men representing 81% of cases. More than 80% of deaths occurred during everyday activities or sleep, whereas only 7% were sports-related. The most common cause of death was coronary artery disease detected in 38%, which was followed by cardiomyopathies in 15%, sudden arrhythmic death syndrome in 12%, left ventricular hypertrophy in 10%, and congenital heart defects in 7%.Coronary artery disease is the predominant cause of sudden cardiac death in the young population of the Czech Republic. Hence, effective preventive measures targeted at the reduction of risk factors associated with early coronary artery disease should be reinforced. The second most prevalent cause in our population are potentially heritable heart conditions such as cardiomyopathies and sudden arrhythmic death syndrome. This fact has already prompted the introduction of molecular autopsy and cardiogenetic care for relatives in the Czech Republic.

The paper deals with the task of modeling discrete questionnaire data with a reduced dimension of the model. The discrete model dimension is reduced using the construction of local models based on independent binomial mixtures estimated with the help of recursive Bayesian algorithms in the combination with the naive Bayes technique. The main contribution of the paper is a three-phase algorithm of the discrete model dimension reduction, which allows to model high-dimensional questionnaire data with high number of explanatory variables and their possible realizations. The proposed general solution is applied to the traffic accident questionnaire analysis, where it takes the form of the classification of the accident circumstances and prediction of the traffic accident severity using the currently measured discrete data. Results of testing the obtained model on real data and comparison with theoretical counterparts are demonstrated.

In the article we present preliminary results of research of a logboat housed in the Žitný Ostrov Museum in Dunajská Streda. According to our research the boat comes from the 3rd century or later part of the last third of 1st millennium cal BCE. It is the earliest chronometrically dated vessel countrywide and the second specimen of Late Iron Age logboats known from Slovak and Czech Republics.

The paper focuses on a problem of comparing clusterings with the same number of clusters obtained as a result of using different clustering algorithms. It proposes a method of the evaluation of the agreement of clusterings based on the combination of the Cohen’s kappa statistic and the normalized mutual information. The main contributions of the proposed approach are: (i) the reliable use in practice in the case of a small fixed number of clusters, (ii) the suitability to comparing clusterings with a higher number of clusters in contrast with the original statistics, (iii) the independence on size of the data set and shape of clusters. Results of the experimental validation of the proposed statistic using both simulations and real data sets as well as the comparison with the theoretical counterparts are demonstrated.

This paper deals with the task of modeling the driving style depending on the driving environment. The model of the driving style is represented as a two-layer mixture of normal components describing data with two pointers: outer and inner. The inner pointer indicates the actual driving environment categorized as “urban”, “rural” and “highway”. The outer pointer through the determined environment estimates the active driving style from a fuel economy point of view as “low consumption”, “middle consumption” and “high consumption”. All of these driving styles are assumed to exist within each driving environment due to the two-layer model. Parameters of the model and the driving style are estimated online, i.e., while driving using a recursive algorithm under the Bayesian methodology. The main contributions of the presented approach are: (i) the driving style recognition within each of urban, rural and highway environments as well as in the case of switching among them; (ii) the two-layer pointer, which allows us to incorporate the information from continuous data into the model; (iii) the potential use of the data-based model for other measurements using corresponding distributions. The approach was tested using real data.

Data analysis is an important method for obtaining information which is useful in many projects such as e.g. Smart Cities. Common data sources are questionnaires, their output mainly purveys discrete data. The most common description of discrete data is through categorical models. These models have several advantages such as flexibility but there are also disadvantages such as a huge dimension of the table expressing this distribution for more variables and values and their overparametrization. The aim of this paper is to replace the categorical distribution by another discrete distribution with a lower number of parameters while maintaining the model quality. Binomial distribution was chosen a suitable one because it is determined only by one parameter and this parameter allows to shape the probability function of binomial distribution well. The output of the paper is the presented model of mixture with binomial components. The suggested estimation algorithms are tested on real traffic data.

The paper focuses on a task of stochastic modeling the driving style and its online estimation while driving. The driving style is modeled by means of a mixture model with normal and categorical components as well as a data-dependent pointer. The mixture parameters and the actual driving style are estimated with the help of a recursive algorithm under the Bayesian methodology. The main contributions of the presented approach are: (i) the online estimation of the driving style while driving, taking into account data up to the current time instant; (ii) the joint model for continuous and discrete data measured on a vehicle; (iii) the data-dependent model of the driving style conditioned by the values of fuel consumption; (iv) the use of the model both for detection of clusters according to the driving style and prediction of the fuel consumption along with other variables; and (v) the universal modeling with the help of mixtures, which allows us to use different combinations of components and pointer models as well as to specify the initialization approach suitable for the considered problem. Results of the driving style detection in real measurements and comparison with the theoretical counterparts are demonstrated.

The paper provides a practical guide on initialization of the recursive mixture-based clustering of non-negative data. For modeling the non-negative data, mixtures of uniform, exponential, gamma and other distributions can be used. Initialization is known to be an important task for a start of the mixture estimation algorithm. Within the considered recursive approach, the key point of initialization is a choice of initial statistics of the involved prior distributions. The paper describes several initialization techniques for the mentioned types of components that can be beneficial primarily from a practical point of view.

The initialization is known to be a critical task for running a mixture estimation algorithm. A majority of approaches existing in the literature are related to initialization of the expectation-maximization algorithm widely used in this area. This study focuses on the initialization of the recursive mixture estimation for the case of normal components, where the mentioned methods are not applicable. Its key part is a choice of the initial statistics of normal components. Several initialization techniques based on visual analysis of prior data are discussed. Validation experiments are presented.

In this paper we perceive data analysis with empirical probability functions as a data mining method. We propose a way to carry out this type of analysis by employing the LISp-Miner system, namely the CF-Miner procedure and pattern difference quantifiers. In order to confirm that LISp-Miner is a suitable tool for this purpose, we briefly present both methods and then show their equivalence. We do this by providing theoretical description which we then support by analysing a small set of data concerning traffic accidents with methods and comparing results. Afterwards we provide an example of analysis of a full data set concerning rail tickets sold at selected stations in 2014. We show that by considering “difference histograms” it is possible to identify remarkable dissimilarities in histograms of time of ticket sale that would not be found otherwise. Both analyses confirms that the method we propose can provide new and interesting results even if the data has been already analysed.

This paper deals with safety performance predictions in the aviation, which address the long-term global efforts to achieve predictive risk management by the year 2028. Predictive risk management regards timely and accurate detection of risk, well before some incident or accident takes place so that effective control actions can be provided. To assure achieving such diagnosis, it is necessary that mathematically well-founded predictions will become part of existing safety management systems with the capability to predict key performance indicators. From current safety metrics and with respect to the data available in the aviation, overall safety performance was selected as suitable candidate for predictions. To obtain the performance signal, Aerospace Performance Factor methodology was utilized. Due to confidentiality restrictions with regard to aviation safety data, this study relies on public data sets from the domain of European Air Traffic Management. Dedicated resampling method was used to fill in the gaps of real data sets by transforming expert knowledge into mathematical functions. This enabled the possibility to build and test mathematical models for predicting safety performance. Because the identified data sources included some data, which are not necessary for computing safety performance but relevant in its context, conditional forecasts were made possible. With respect to this, the goal of this paper was to research and evaluate possibilities for both conditional and unconditional forecasts in the context of future risk management. Time-series analysis of the computed safety performance was conducted using ordinary least squares and maximum likelihood estimation. Each of the methodology led to different mathematical model and different predictions. Specific aspects of each methodology were identified. Among others, the conclusions confirm possibility of predicting safety performance for establishing predictive risk management, highlighting great potential of conditional forecast and favouring systemic models of safety.

This chapter considers the problem of clustering non-Gaussian data with fixed bounds via recursive mixture estimation under the Bayesian methodology. Here a mixture of uniform distributions is taken, where individual clusters are described by mixture components. For the on-line detection of data clusters, the paper proposes a mixture estimation algorithm based on (i) the update of reproducible statistics of uniform components; (ii) the heuristic initialization via the method of moments; (iii) the non-trivial adaptive forgetting technique; (iv) the data-dependent dynamic pointer model. Results of validation experiments are presented.

The paper deals with the mixture-based clustering of anonymized data of patients with leukemia. The presented clustering algorithm is based on the recursive Bayesian mixture estimation for the case of exponential components and the data-dependent dynamic pointer model. The main contribution of the paper is the online performance of clustering, which allows us to actualize the statistics of components and the pointer model with each new measurement. Results of the application of the algorithm to the clustering of hematological data are demonstrated and compared with theoretical counterparts.

This paper deals with the data analysis of traffic accidents. Traffic accidents can be caused by different reasons, e.g., by watchfulness of a driver, failure of a vehicle, bad structural arrangements, etc. The aim of this paper is to investigate seriousness of incidents in dependence on different circumstances of an accident. Description of these circumstances leads to the use of a high number of different variables (about 50 variables), which are mostly discrete. The majority of statistical methods dealing with discrete variables use a frequency table. This is not suitable for traffic data because of a huge dimension. In this paper, several methods are proposed for solution to the problem with high-dimensional traffic data.