Road traffic accidents remain a critical public health concern worldwide, with Poland consistently experiencing high fatality rates—52 deaths per million inhabitants in 2023, compared to the EU average of 46. To investigate the underlying factors contributing to these accidents, we developed a multifactorial dataset integrating 250,000 accident records from 2015 to 2023 with spatially interpolated weather data and sociodemographic indicators. We employed Kriging interpolation to convert point-based weather station data into continuous surfaces, enabling the attribution of location-specific weather conditions to each accident. Following comprehensive preprocessing and spatial analysis, we generated visualizations—including heatmaps and choropleth maps—that revealed distinct regional patterns at the county level. Our preliminary findings suggest that accident occurrence and severity are driven by different underlying factors: while temperature and vehicle counts strongly correlate with total accident numbers, humidity, precipitation, and road infrastructure quality show stronger associations with fatal outcomes. This integrated dataset provides a robust foundation for Bayesian and time-series modeling, supporting the development of evidence-based road safety strategies.

Road traffic accidents pose a substantial global public health burden, resulting in significant fatalities and economic costs. This study employs Bayesian Poisson regression to model traffic accident rates in Poland, focusing on the intricate relationships between weather conditions and socioeconomic factors. Analyzing both yearly county-level and weekly nationwide data from 2020 to 2023, we created four distinct models examining the relationships between accident occurrence and predictors including temperature, humidity, precipitation, population density, passenger car registrations, and road infrastructure. Model evaluation, based on WAIC and PSIS-LOO criteria, demonstrated that integrating both weather and socioeconomic variables enhanced predictive accuracy. Results showed that socioeconomic variables—especially passenger car registrations—were strong predictors of accident rates over longer timeframes and across localized regions. In contrast, weather variables, particularly temperature and humidity, were more influential in explaining short-term fluctuations in nationwide accident counts. These findings provide a statistical foundation for identifying high-risk conditions and guiding targeted interventions. The study supports Poland’s national road safety goals by offering evidence-based strategies to reduce accident-related fatalities and injuries.

This paper presents a robust framework for brushless DC (BLDC) motor fault diagnosis by comparing two data fusion strategies: decision-level stacking and data-level fusion. The study uses features extracted from current and rotational speed signals to classify four conditions: healthy, mechanical damage, electrical damage, and combined faults. Decision-level fusion employs a meta-learner to integrate predictions from specialized base models, while data-level fusion utilizes a single Random Forest classifier trained on all combined features. This comparative analysis aims to provide insights into the optimal fusion strategy for developing highly accurate BLDC motor diagnostic systems, with particular relevance for predictive maintenance applications, where early and reliable fault identification is essential.