Since high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes an experiment to develop and test machine learning models, i.e., long short-term memory, gated recurrent unit recurrent networks, and stacked autoencoder networks. It compares their effectiveness with traffic prediction results generated using a widely recognized traffic simulator that analyzes traffic at the level of individual vehicles.

Variable speed limit (VSL) is an intelligent transportation system (ITS) solution for traffic management. The speed limits can be changed dynamically to adapt to traffic conditions such as visibility and traffic volume, curvature, and grip coefficient of the road surface. The VSL traffic sign location problem and attempts to solve it using computer simulation are presented in this paper. Experiments on a selected road segment, carried out using the traffic simulator, have shown that the proposed method allows the driver’s habits to be taken into account so that the location of road signs can be optimized. The observable effect was a reduction in vehicle speeds and speed variance on critical road segments, translating directly into increased safety and harmonized traffic.

An approach to a new kind of recommendation system design that suggests safe speed on the road is presented. Real data obtained on roads were used for the simulations. As part of a project related to autonomous road sign development, a number of measurements were carried out on both local roads and expressways. A speed recommendation model was created based on gathered traffic data employing the traffic simulator. Depending on the traffic volume and atmospheric conditions prevailing on the road, as well as the surface conditions, the proposed system recommends the safe speed for passing vehicles by influencing the distance from the preceding vehicle to prevent collisions. The observed effect of the system application was an increase in the minimal distance between vehicles in most simulations.

Auditory brainstem response (ABR) testing is widely used in hearing screening and clinical diagnostics. In routine practice, ABR recordings are often interpreted from graphical reports rather than raw digital signals, making wave V identification and hearing-threshold estimation challenging, particularly near threshold. This study presents a computer-vision framework that estimates hearing thresholds and wave V latencies directly from ABR report images without reconstructing one-dimensional signals. YOLO and RF-DETR object detectors were evaluated in combination with physiologically constrained post-processing that selects consistent detections across stimulus intensity levels. The best-performing configuration, based on an optimized YOLO26M detector, achieved 92.76% accuracy in identifying wave V presence, a mean absolute wave V latency error of 0.28 ms, and 94.3% agreement with expert-defined hearing thresholds within ±10 dB. Post-processing improved threshold agreement by enforcing consistency across intensity levels. These findings demonstrate the feasibility of extracting clinically relevant information directly from graphical ABR reports and suggest potential utility as decision support for audiologists. Further evaluation across clinical centers, recording devices, and report formats is required to establish generalizability.