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Distracting activities (such as using mobile phones, writing text messages) become increasingly common with the widespread use of telecommunication devices, becoming an increasing problem of road safety. Our research aimed to show the effects of these disruptive factors on driving. To quantify the effects, simulator tests have been performed. To analyse the significance of the changes caused by the disruptive factors, mathematical-statistical methods have been applied and conclusions for all drivers have been drawn. The effects of the disruptive factors have been quantified. On the one hand, the cognitive distraction and the hindrance of movements affects negatively the road safety, and on the other hand results in negative environmental and economic effects. Based on the numerical results of the research, hitting speeds caused by the disruptive factors have been determined as an example. The results of the research can be used as input data for the quantification of economic and environmental effects of road safety caused by disruptive factors and for the establishment of the background of legislation related to the prohibition of these factors.

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Keeping the basic principles of sustainable development, it must be highlighted that decisions about transport safety projects must be made following expert preparation, using reliable, professional methods. A prerequisite for the cost–benefit analysis of investments is to constantly monitor the efficiency of accident forecasting models and to update these continuously. This paper presents an accident forecasting model for urban areas, which handles both the properties of the public road infrastructure and spatial dependency relations. As the aim was to model the urban environment, we focused on the road public transportation modes (bus and trolley) and the vulnerable road users (bicyclist) using shared infrastructure elements. The road accident data from 2016 to 2018 on the whole road network of Budapest, Hungary, is analyzed, focusing on road links (i.e., road segments between junctions) by applying spatial econometric statistical models. As a result of this article, we have developed a model that can be used by decision-makers as well, which is suitable for estimating the expected value of accidents, and thus for the development of the optimal sequence of appropriate road safety interventions.

Abstract. The analysis on the border crossings, as the objects to decrease the separation effect, is really important for our country, to evaluate the connection of the neighbouring and other nearby countries and international economic centres with Hungary. In our country's life the cross-border partnerships have an important role because of historical and cultural causes. In recent article, our task is to analyse changes of the crossborder traffic, with the help of the spatial econometrics. The goal is, to show the effect of five outstanding areas on the crossing traffic, and to analyse the spatial connection between them.

On one hand, better and better databases occur through the world, however for cities a lot of important data is unavailable. On the other hand, the cities are one of the most important units, where transportation interactions ought to be analyzed. In this recent paper the order of the cities (based upon the NUTS – Nomenclature des unités territoriales statistiques classification) will be introduced, and an answer will be found for the question whether this parameter could be used as a substitution of an economic parameter.

Közúti baleseti veszteségek aktualizálásaA közlekedésbiztonság fontos eleme a veszteségértékek teljes körű feltárása és használata. Az elvégzett kutatások eredményeként a teljeskörűség irányába tett jelentős lépéseket mutatják be és tesznek javaslatot a gyakorlati alkalmazásra.

Road infrastructure has an impact on the occurrence of road traffic crashes. The aim of this study was to analyze the impact of road geometric formation on road traffic crashes. Based on the nature, convenience, and availability of data, the study used Budapest city road traffic crash data from 2017 to 2021. For organizing, analysis, and modeling, the study used Microsoft-Excel, the Statistical Package for Social Science, and Quantum Geographic Information System. Relative frequency distribution, Multinomial Logistic Regression, Multilayer Perceptron Artificial Neural Network, and Severity Index were used for the analysis. Both inferential and descriptive statistics are used to describe and summarize the study outcome. Multicollinearity tests, p-value, overdispersion, percent of incorrect error, and other statistical model testes were undertaken to analyze the significance of the data and variable for modeling and analysis. A large number of crashes were observed in straight and one-lane road geometric formationsr890. However, the severity level was high at the horizontal curve and in all three lanes of the road. The regression model indicated that light conditions, collision type, road geometry, and speed had a significant effect on traffic accidents at a p-value of 0.05. A collision between the vehicle (rear end collision), and a vehicle with a pedestrian was the probable cause of the crash. The Multilayer Perceptron Artificial Neural Network indicated that horizontally curved geometry has a positive and strong relationship with road traffic fatalities. The primary reasons for the occurrences of a road traffic crash at an intersection, horizontal curve, and straight road geometric formation were the improper use of road traffic signs, road pavement condition, and stopping sight distance problems, respectively. The hourly distribution showed that from 16:01 to 17:00 time interval was a peak hour for the occurrences of road traffic crashes. Whereas, driver plays vital role and responsible body for the occurrences of crashes at all geometric formations.