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.

Abstract

Reducing the number of road accident victims is a declared purpose of the European Union. Intelligent Transport Systems (ITS) are able to contribute to this by warning and supporting the drivers, therefore improving road safety. The aim of our research was to analyze the safety aspects of ITS systems, structuring the solutions, analyzing and exploring the opportunities for development. Strategic objectives have been evaluated and relevant processes for achieving them have been summarized. The research efficiently contributes to the utilization of development potentials of ITS systems.

No abstract

The paper introduces a framework to perform the demand management and route planning tasks of a highly developed transport system managing scheme, assuming an autonomous transport system. Two types of autonomous transport system managing models have been introduced. In case of the first model, the assigned number of trips is assumed to be the modified variable related to the optimization problem. In case of the second model, the decision process is directly influenced by the travel prices defined by the optimization method. These approaches represent different demand management strategies. The first model aims to directly assign the incoming user demands to the system, while the second procedure lets the users make the decision. However, in the second case the system can strongly influence the users’ choices through the values of the travel prices. Accordingly, it seems to be a reasonable assumption that the firstly presented model has significantly higher efficiency in distributing the load on the network. On the other hand, the method of the second model would be much more tolerable and acceptable from a social point of view. Therefore, the aim of the paper is to introduce the developed models and to compare their efficiencies.