The aim of this article is to improve road safety. Specifically, it deals with the development of a mathematical model that will more accurately define the severity of a defect in a road restraint system. Currently, that evaluation is based only on the subjective perception of individual safety auditors. The mathematical model was developed based on the principle of Bayesian statistics. The determination of the specific risk was made by comparing the results of the model for two datasets. In the first case, the model was based on accident data correlated with recorded defects in road restraint systems. In the second case, the dataset represented accident events with crash barriers where no defect was identified. Based on the comparison, a total of 64 risk combinations were identified. The mathematical model confirmed 26 combinations (41% of all selected combinations of the defect levels of the crash barriers). Although not even half of the identified combinations were confirmed, more than 90% of all correlated records are found in these exposures of confirmed combinations. The verification was able to clearly define the risk of safety defects and thus brings potential accuracy to subsequent decision-making related to the repair of road restraint systems.

The article focuses on the assessment and evaluation of the passability in densely populated parts of cities with multi-storey housing estates, in terms of the operation of the integrated rescue system (IRS) in the Czech Republic. The aim of the research is to minimize the arrival times to conduct the intervention as efficiently as possible. The presented problem is caused by unsystematic development of housing estates and the emergence of secondary problems in the form of inability to reach the place of intervention by the larger IRS vehicles. The vision presented in this document presents a systematic approach to improve the serviceability of individual blocks of flats. The main aim is to ensure passability, even for the largest equipment, such as fire engine ladders. Detailed mapping of the selected sites by drones, construction of their digital model, and subsequent virtual verification of the passability by specific vehicle models on identified access roads was performed. The results obtained by this procedure can then be implemented in the navigation of the fire safety forces and facilitate their arrival at the site of intervention. At the end, specific ways are presented in which the whole system can be modified to be able to intuitively change and choose individual access routes in real time, based on the current situation in the area.

The primary objective of this article is to determine and subsequently verify the severity of a traffic accident caused by a collision with a fixed obstacle located in rural area sections along international roads in the Czech Republic. The aim is to create a unified assessment tool of fixed obstacles along roads. In this way, it is possible to determine in advance the level of risk of fixed obstacles along roads before traffic accidents could occur at these locations. The work is based on the assumption that the severity level of fixed obstacles in road traffic is determined by the product of the probability of hitting the object and the severity of the consequences. On the basis of these parameters, 54 specific combinations of fixed obstacles and surrounding terrain are determined, to which the specific severity of the combination is assigned in relation to the potential impact on the health of the vehicle occupants. Subsequently, a hypothesis is established that confirms or refutes, in a clear and unified manner, the initial, subjective views on the possible consequences of a traffic accident related to a fixed-obstacle impact. The verification of the hypothesis is carried out using a statistical apparatus based on the principles of conditional probability on 280 traffic accidents related to hitting a fixed obstacle. The proposed procedure verified 36 out of 54 (almost 67%) individual combinations of fixed obstacles and surrounding terrain. It was determined that there were 13 combinations with a high risk, where there is a significant probability of serious injury to occupants; 6 combinations with a moderate severity of injury to occupants; 3 cases with a low severity; and 14 cases where it was shown that the impact with a fixed obstacle was most probably not going to result in personal injury but only in material damage to the vehicle. The results of this research, which applies a unified approach to the risk analysis of fixed obstacles, will systematically improve the level of traffic safety. This primarily means preventing accidents with fixed obstacles, saving lives and reducing economic losses.

Orthophoto imaging of the Earth's surface using unmanned aerial systems have in recent years become a common and efficient method for acquiring highly detailed orthophoto maps. These are widely used in transportation and civil engineering fields. In the context of traffic accidents and technical documentation, such imagery can be applied for accurate reconstruction of the scene. However, this process often requires manual vectorization of selected road infrastructure features. This task is time-consuming and demanding, especially in more complex scenarios. The presented paper introduces a newly proposed method for semi-automatic vectorization of road infrastructure features from raster imagery. The method was implemented in MATLAB and consists of several sequential steps. These include selection of the area of interest, colour identification, noise reduction, clustering, and generation of vector contours. The entire process emphasizes simplicity, computational efficiency, and ease of use without the need for machine learning or extensive training data. Statistical evaluation using a paired t-test (p = 0.0022) confirmed that the automated approach is significantly faster than manual processing. On average, the proposed semi-automatic vectorization process was 2.15 times faster. In realistic scenarios, such as entire intersection areas, a speed increase of up to 3.1 times was achieved. These results confirm the practical benefit of the proposed method for efficient and rapid processing of traffic infrastructure image documentation.