Studying the impact of AVs on our road infrastructure offers a lot of potential in the transportation domain; one of these issues is how capacity will be affected. This paper presents a contribution to this research area by investigating the impact of AVs on the capacity of single-lane roundabouts using a microsimulation model. For the development of the model, a roundabout situated in Győr (Hungary) was selected and field data on the roundabout geometric characteristics as well as traffic volumes were used. Simulations using Vissim were run for various scenarios based on varying input traffic volumes and market penetration rates of AVs to assess queue lengths. The highway capacity manual (HCM) roundabout model was used to estimate the capacity of the existing roundabout. Values of follow-up times and critical gaps were set to decreasing as the penetration rate of AVs increases. The results demonstrated that 20% and 40% AVs in the flow would increase leg capacities by about 10% and 20%, respectively. Furthermore, a reduction in excessive queue lengths was estimated and capacities and queue lengths were calculated by legs. It was found that these are highly influenced by the distribution of flows among legs, and the share of flows in various directions.

There are several methods for the analysis of road accidents in a road network. In Hungary from 2011 GPS coordinates are used to identify the location of personal injury accidents. This method significantly improves the display of locations of accidents on the map, which can be then analyzed using GIS tools. Accident black spots are the most dangerous places in road networks identified by the density of the accidents in the network. One of the analysis methods is the accident density searching. The methods and algorithms used in some software may show differences in relation to one another. The aim of this research is comparing two applications by investigating the local road network in GyĘr. The analysis was made using the WEB-BAL accident analysis software using the density-based spatial clustering of applications with noise procedure and the QGIS software using the kernel density estimation method. The former is the official accident database and online software used for accident investigations and the latter is an open source geographic information system. The results are visualized in accident density plots and black spot maps.

Many traffic accidents are caused by unforeseen and unexpected events in a site that was hidden from the driver's eyes. Road design parameters determining required visibility are based on relationships formulated decades ago. It is worth reviewing them from time to time in the light of technological developments. In this paper, sight distances for stopping and crossing situations are studied in relation to the assumed visual abilities of autonomous vehicles. Current sight distance requirements at unsignalized intersections are based among others on speeds on the major road and on ac-cepted gaps by human drivers entering or crossing from the minor road. Since these requirements vary from country to country, regulations and sight terms of a few selected countries are compared in this study. From the comparison it is remarkable that although the two concepts, i.e. gap acceptance on the minor road and stopping on the major road have different backgrounds, but their outcome in terms of required sight distances are similar. Both distances are depending on speed on the major road: gap sight distances show a linear, while stopping sight distances a parabolic function. In general, European SSD values are quite similar to each other. However, the US and Australian guidelines based on gap acceptance criteria recommend higher sight distances. Human capabilities and limitations are considered in sight field requirements. Autonomous vehicles survey their environment with sensors which are different from the human vision in terms of identifying objects, estimating distances or speeds of other vehicles. This paper compares current sight field requirements based on conventional vehicles and those required for autonomous vehicles. Visibility requirements were defined by three vision indicators: distance, angle of view and resolution abilities of autonomous cars and human drivers. These indicators were calculated separately for autonomous vehicles and human drivers for various speeds on the main road and for intersections with 90° and 60° angles. It was shown that the required sight distances are 10 to 40 meters shorter for autonomous vehicles than for conventional ones.

The objective of this paper was to model the evolution of road safety as a function of motorization level. The authors completed a country-level as well as a time-dependent analysis focusing on countries for which data were available for a long period of time (1965 to 2009). For the statistical analysis, a function describing road safety trends (decline, turning, improvement) was proposed. Two coefficients in the model were estimated for each country and for each year, and their change over time is discussed. The results showed that the shape of the curve changed over time. In some countries, the decrease in the mortality rate became slower over time; however, a greater potential to improve road safety existed in other countries. Possible reasons for the general positive trends in road safety are the continuous improvement in engineering solutions (better infrastructure, safer cars) as well as road users who are better trained and skilled because of education and experience. In addition to the factors mentioned previously, the increased speed by which safety-related information and knowledge are disseminated contributes to the decrease in the differences in safety levels among countries.

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