Driving experiences provided by the introduction of new vehicle technologies are directly impacting the criteria for road network design. New criteria should be taken into consideration by designers, researchers and car owners in order to assure traffic safety in changed conditions that will appear with, for example, introduction of Autonomous Vehicles (AVs) in everyday traffic. In this paper, roundabout safety level is analysed on the originally developed microsimulation model in circumstances where different numbers of AVs vehicles are mixed with Conventional Vehicles (CVs). Field data about speed and traffic volumes from existing roundabouts in Croatia were used for development of the model. The simulations done with the Surrogate Safety Assessment Model (SSAM) give some relevant highlights on how the introduction of AVs could change both operational and safety parameters at roundabouts. To further explore the effects on safety of roundabouts with the introduction of different shares of AVs, hypothetical safety treatments could be tested to explore whether their effects may change, leading to the estimation of a new set of Crash Modification Factors.

Child pedestrians make up 30% of the total number of children injured in road traffic in the EU. They are a particularly vulnerable subgroup because they exhibit specific traffic behavior related to cognitive and physical development, sociodemographic characteristics, and environmental conditions. This paper provides an overview of research of parameters that affect the safety of children in the conflict zones of the intersection—crosswalks. The overview was undertaken targeting available research mostly conducted in the last 10 years all over the world, related to the identification of parameters that affect the safety of child-pedestrians, and models developed for the prediction of pedestrian and child-pedestrian behavior. Research conducted on various urban networks provides insight into locally and more widely applicable impact parameters connected to child characteristics and infrastructural and traffic elements, but also distractors (e.g., electronic devices) as new phenomena influencing children’s road safety. A review of pedestrian behavior-prediction models suggests that models are being developed for the general population, and models for children’s behavior, with specific parameters, are missing. For further research, more detailed analysis of the impact of distractors and of COVID–19 pandemic non-mobility, as well as an analysis of possible infrastructural solutions to increase children’s road traffic safety, is suggested.

Pavement surface texture is one of the prevailing factors for friction realization on pavement surfaces. In this paper, an overview of pavement texture properties related to the pavement frictional response is given. Image analysis methods used for pavement texture characterization are thoroughly analyzed together with their potential for the establishment of a pavement texture–friction relationship. Digital pavement surface models derived from photogrammetry or laser scanning methods enable the extraction of texture parameters comparable to the ones acquired by common pavement surface measuring techniques. This paper shows the results of a preliminary small-scale research study of the pavement texture–friction relationship. This research was performed in a laboratory which produced asphalt samples, primarily to analyze the potential of developing a methodology for the digital pavement texture model setup. Furthermore, the relationship between selected 2D texture parameters calculated from the digital texture model and measured friction coefficient expressed as SRT value was analyzed. A significant correlation was established for standard texture indicator mean profile depth (MPD) and SRT values (R = 0.81). Other texture parameters showed moderate correlation with the frictional response of the surface, with absolute values of correlation coefficients varying from 0.7 to 0.75. A further analysis of this relationship will be performed by inclusion of other texture parameters that can be determined from the digital texture model acquired by the established methodology.

Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.