The traffic flow parameters of the road network are most often evaluated through volumes, which are compared with its maximum volume (capacity) or speed and density. Capacity assessment was performed, considering horizontal and vertical orientation and characteristics of the traffic stream. This article presents the results of research on the identification of different states of creating congestion groups and their relationship to road capacity or decrease in speed. The following hypothesis was verified: when the capacity of the road is exceeded or almost reached, there is “always” a significant drop in the flow of traffic compared to when the capacity is not exceeded. The analysis showed that the average travel speed drops by 30% for the condition where groups of 25 or more vehicles are formed with a time interval of up to 4 s. The results make it possible to set traffic models in short time intervals according to real spatial conditions and to use them in the analysis of the environmental and safety impacts of road transport.

Traffic volume depends on several indicators. The most important are the degree of motorization, mobility, and especially the travel time and travel cost factor. The article presents an analysis of the possibility of using mobility surveys and traffic counts to achieve a balance between supply and demand. The frequency of congested traffic and over-capacity volumes are determined. By analyzing the trip information captured in the mobility surveys, we detected the strata causing the highest demand—economically active people with “job” as the purpose of their trip. The study area includes the Kysuce region and the city of Žilina in Slovakia. Three scenarios are processed in the article. Scenario 0 defines the current state, scenario 1 defines the situation with free-of-charge public transport, and scenario 2 represents a targeted modal split where saturation is not exceeded. The aim is to shift passengers to public transport and increase the share of public transport in the modal split. Scenario 2 is also presented in terms of saved emissions as an additional factor of relocating passengers from private to public transport. In terms of fare, we found a small change in the modal split with free-of-charge public transport.

Traffic flow at intersections is influenced by spatial design, control methods, technical equipment, and traffic volume. This article focuses on detecting traffic flows at intersections using video recordings, employing a YOLO-based framework for automated analysis. We compare manual evaluation with machine processing to demonstrate the efficiency improvements in traffic engineering tasks through automated traffic data analysis. The output data include traditionally immeasurable parameters, such as speed and vehicle gaps within the observed intersection area. The traffic analysis incorporates findings from monitoring groups of vehicles, focusing on their formation and speed as they traverse the intersection. Our proposed system for monitoring and classifying traffic flow was implemented at a selected intersection in the city of Zilina, Slovak Republic, as part of a pilot study for this research initiative. Based on evaluations using local data, the YOLOv9c detection model achieved an mAP50 of 98.2% for vehicle localization and classification across three basic classes: passenger cars, trucks, and buses. Despite the high detection accuracy of the model, the automated annotations for vehicle entry and exit at the intersection showed varying levels of accuracy compared to manual evaluation. On average, the mean absolute error between annotations by traffic specialists and the automated framework for the most frequent class, passenger cars, was 2.73 across all directions at 15 min intervals. This indicates that approximately three passenger cars per 15 min interval were either undetected or misclassified.

In recent decades, road transport has become one of the dominant factors shaping environmental conditions, with both beneficial and adverse consequences. While transport infrastructure facilitates access to essential services and supports societal well-being, vehicular emissions remain a major source of air quality degradation. Among the pollutants released, nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) are of particular concern due to their adverse health effects, especially in densely trafficked urban areas. Pollutant levels are determined not only by traffic intensity but also by external influences such as meteorological conditions and roadway design. This study examines how different intersection configurations affect ambient concentrations of PM₁₀, PM_2.5, and NO₂. Field monitoring and dispersion modeling were carried out for a three-arm intersection and a roundabout. NO₂ concentrations were quantified using a reference chemiluminescence method, while PM₁₀ and PM_2.5 were measured with an optical aerosol spectrometer. Traffic flow characteristics associated with each intersection geometry were simulated in PTV Vissim, and pollutant dispersion patterns were subsequently analyzed using the CadnaA modeling environment. Field measurements revealed lower PM concentrations (reduction in PM₁₀, PM_2.5–10 and PM_2.5 concentration—30.1%, 45.1% and 22.8%) and higher NO₂ concentrations (increase in NO₂ concentration—143.3%) at the roundabout.

The behavioral features of the population are addressed in transport models by different levels of territorial disaggregation and the creation of demand strata in a territory. The need for input data grows exponentially with the demand for a detailed zonal system of the territory. The basic source is the mobility survey. This article deals with the comparison of the calculation of the probability of choosing a transport mode for trips using the classic multinominal logit model and the best-worst method. We used data from a mobility survey in the Žilina region as a basic sample. The analysis covered 11 districts and their gravity areas. The individual transport relations are evaluated in detail in the analysis. The results confirm the high degree of accuracy of the best-worst method in the calculation of mode choice on a regional scale. Despite the promising results of the agreement in the confrontation with the mobility survey, it is necessary to verify the modeled data with a more detailed area with disaggregation on-demand strata.