In order to overcome the shortcomings of crash data a number of surrogate measures of safety have been developed and proposed by various researchers. One of the most widely used temporal indicators is time-to-collision (TTC) which requires the road users to be on a collision course. Road users that are strictly speaking not on a collision course actually might behave and take evasive actions as if they were, thus indicating that such near-miss situations might also be relevant for safety analysis. Taking that into account, a more flexible indicator T2, which does not require the two vehicles to be on a collision course, describes the expected time for the second road user to arrive at the conflict point. Recently extreme value theory (EVT) offering two approaches, block maxima (BM) and Peak over Threshold (POT), has been applied in combination with surrogate indicators to estimate crash probabilities. Most of this research has focused on testing BM and POT as well as validating various surrogate safety indicators by comparing model estimates to actual crash frequencies. The comparison of collision course indicators with indicators including crossing course interactions and their performance using EVT has not been investigated yet. In this study we are seeking answers to under what conditions these indicators perform better and whether they are transferable. Using data gathered at a signalized intersection focusing on left-turning and straight moving vehicle interactions our analysis concluded that the two indicators are transferable with stricter threshold values for T2 and that POT gives more reasonable results.

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.

One of the major challenges of autonomous vehicles (AV) is their interaction with pedestrians. Unofficial interactions such as gestures, eye contact, waving, and flashing lights are very common behavioral patterns for drivers to express their intent to give priority. In our research we composed a virtual reality experiment for a pedestrian crossing in an urban environment in order to test pedestrians’ reactions on an LED light display mounted on a virtual AV. Our main research interest was to investigate whether communication patterns influence the decision making of pedestrians when crossing the road. In a VR environment, four scenarios were created with a vehicle approaching a pedestrian crossing with different speeds and displaying a special red/green sign to pedestrians. Here, 51 persons participating in the experiment had to decide when crossing is safe. Results show that the majority of people indicated they would cross in the time windows when it was actually safe to cross. Male subjects made their decision to cross slightly faster but no significant differences were found in the decision making by gender. It was found that age is not an influencing factor, either. Overall, a quick learning process was experienced proving that explicit communication patterns are self-explaining.

Limitations of historical crash data and the advantages of surrogate measures of safety have already been stressed by various authors. To describe nearness-to-collision, mostly time-based indicators are applied, and there is a consensus among researchers that speed-related indicators are needed to account for the severity dimension. There have been attempts to fit bivariate Extreme Value models to predict the number of crashes, however modeling crash frequency together with severity has received much less attention. The aim of this paper is to apply Extreme Value models to various pairs of temporal and speed-related indicators in order to investigate the dependence structure between them as well as to construct probability based risk levels and examine them in relation to severity levels. Bivariate threshold excess models were fitted to a dataset of left-turning and straight moving vehicle interactions recorded in a signalized intersection. The dependence structure between variable pairs were thoroughly investigated; it was concluded that temporal and speed related indicators were found independent, which means that road users getting close to each other in time does not necessarily come with high speeds. Therefore these indicators should be combined in order to properly predict severity; a temporal indicator on its own is not enough to make inferences about the severity of events. Using the calculated joint probability of events risk levels were constructed illustrating the events of equal probability.

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 Győr. 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.