Artur Budzyński

Road safety is a prerequisite for sustainable mobility, yet cyclists remain disproportionately exposed to severe outcomes in mixed traffic. Using police-reported bicycle–vehicle crashes from the national SEWIK registry in Poland (152,567 cyclist-involved records; 2015–2024), this study modeled five ordered injury-severity classes with a CatBoost gradient-boosting classifier, evaluated performance with quadratic weighted kappa and complementary class-sensitive metrics under extreme imbalance (including benchmark comparisons and calendar-based walk-forward stress tests), and interpreted predictions with SHAP to summarize transparent, feature-level association patterns. The results indicate modest overall ordinal discrimination (hold-out QWK ≈ 0.20), while highlighting elevated recall for rare fatal outcomes together with low precision, implying a substantial false-positive trade-off if outputs were used as deterministic classifiers. Global and local explanations point to stronger associations for cyclist age, shorter offender licensure tenure (a registry proxy for experience-related factors), regional context, and built-up versus non-built-up settings consistent with higher kinetic-energy environments; these variables should be interpreted cautiously because registry data are observational and omit key exposures (e.g., measured impact speed and cycling volume). Overall, the study contributes a nationwide, explainable severity-profiling workflow for prioritizing cyclist protection: combining benchmarked ML, multi-metric reporting, and XAI diagnostics can support monitoring and evaluation of speed management, infrastructure, and licensing-system improvements—without overstating causal effects from administrative records alone.

The rapid growth of e-commerce has significantly increased direct-to-consumer deliveries, putting competitive and environmental pressure on urban last-mile logistics. Out-of-home (OOH) delivery options, particularly parcel lockers, are increasingly integrated into city mobility strategies to reduce congestion and emissions. However, the role of mobile applications front-ending these networks remains under-researched. This study aims to evaluate the user experience (UX) and functional adequacy across three major parcel-locker apps in Poland: InPost Mobile, DPD Mobile, and ORLEN Paczka. A cross-sectional, mixed-methods approach combining in situ corridor testing and structured post-task questionnaires was employed with 30 users at real locker locations in Katowice. The results indicate that interface simplicity, predictable information flow, and technical stability are the dimensions most consistently associated with higher user ratings. InPost Mobile consistently achieved the highest ratings due to its focus on core workflows, whereas applications emphasizing broader functional coverage (ORLEN Paczka) exhibited usability trade-offs, and DPD Mobile underperformed in speed and stability. Because the study relied on a small convenience sample (n = 30) in a single city and was skewed toward younger adults (18–24), the findings should be interpreted as exploratory and primarily reflective of a digitally proficient demographic rather than the broader user population.

The study proposes an interpretable machine-learning approach for predicting bus arrival delays based on the Automatic Vehicle Location (AVL) system. The data describe vehicle movements and schedule adherence at successive stops, enabling delay estimation both along the route and across different time periods. Delays are defined as deviations between observed arrival times and scheduled times at individual stops, allowing both spatial and temporal delay patterns to be examined. The approach integrates ensemble machine-learning models with nested, temporally aware cross-validation and hyperparameter optimization. This validation strategy preserves the temporal order of observations, ensuring that model evaluation reflects realistic forecasting conditions and avoids information leakage. In particular, training and test sets are separated along the time axis, preventing future observations from influencing model training. Three ensemble models—Random Forest, XGBoost, and CatBoost—achieved comparable accuracy (RMSE ≈ 5.4 min; MAE ≈ 3.6 min; R² ≈ 0.44). Model performance was evaluated at the level of individual stop arrivals, reflecting short-term delay prediction under operational conditions. Comparable performance across models indicates that the observed patterns are not specific to a single modelling technique. This consistency suggests that the identified delay patterns are robust with respect to model choice rather than driven by algorithm-specific assumptions. The results show that the proposed approach enables the identification of combined temporal and spatial feature interactions associated with bus delay magnitudes within the analysed case study. These interactions mainly involve time-of-day effects and stop sequence, illustrating how delays accumulate as vehicles progress along the route. This pattern reflects the cumulative nature of delays in mixed-traffic operations, where early disturbances propagate toward downstream stops. SHAP values were used to provide a quantitative interpretation of these interactions and to identify the most influential predictors in the analysed models. The analytical structure, designed around weekly data partitions, supports delay monitoring and operational analysis. The results indicate that the proposed approach can support the analysis and interpretation of bus delay patterns within similar operational contexts.

Given global efforts to promote sustainable energy transitions, this study investigates how the deployment of renewable energy technologies (RETs) relates to multidimensional societal welfare and provides empirical evidence on these linkages in Lithuania. The purpose of the study is to provide an integrated, Lithuania-specific assessment of how economic, social, and environmental determinants associated with RET deployment are related to multiple dimensions of societal welfare. Drawing on scientific literature, an integrated indicator framework is developed that links the economic, social, and environmental determinants of renewable energy technology (RET) deployment to six societal welfare dimensions, as defined by the Lithuanian Quality of Life Index. Using official Lithuanian statistics for 2020–2024, a standardized panel dataset is constructed and Pearson correlation analysis and multiple linear regression are applied using aggregated determinant categories, with model assumptions verified using the Breusch–Pagan and Durbin–Watson tests. Correlation results show very strong positive links between RET intensity indicators and key economic welfare measures (for example, wages, GDP per capita, foreign direct investment, disposable income), with absolute correlation coefficients typically between 0.90 and 0.99 (p < 0.05), and strong negative correlations between air-pollution indicators and GDP, income, FDI, and education (correlation coefficients between −0.96 and −0.90; p < 0.05). The results indicate that RET-related economic determinants have a statistically significant positive effect on the societal welfare dimensions of material living conditions; entrepreneurship/business competitiveness; and public infrastructure, living-environment quality/safety. Social factors also significantly support the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. In the retained regression models, explanatory power is very high (R² between 0.91 and 0.999), with positive and statistically significant coefficients for the economic determinant (regression coefficients between 0.43 and 0.96; p < 0.05) and negative, statistically significant coefficients for the environmental determinant in the entrepreneurship and public-infrastructure dimensions (regression coefficients between −1.13 and −1.51; p < 0.05). Environmental determinants are associated with lower air pollution but show negative effects on the societal welfare dimensions of entrepreneurship/business competitiveness and public infrastructure, living-environment quality/safety. Overall, the findings suggest that RET deployment is an important correlate of the economic aspects of societal welfare, while environmental and social dimensions display more complex, domain-specific impacts.

The deployment of solar and other renewable energy technologies (RETs) plays a central role in the global energy transition and the pursuit of sustainable development. Beyond reducing greenhouse gas emissions, these technologies generate far-reaching societal co-benefits that shape environmental quality, social equity, and economic growth. This study systematically reviews peer-reviewed literature published between 2009 and 2025 to identify, integrate, and assess empirical evidence on how RET deployment contributes to societal welfare. Following the SALSA framework and PRISMA guidelines, 147 studies were selected from Scopus and Web of Science. The evidence reveals a consistent welfare triad: environmental gains (emission and pollution reduction, climate mitigation), social gains (improved health, affordability, energy security, and inclusion), and economic gains (employment and income growth, local development). These benefits are, however, heterogeneous and depend on enabling conditions such as policy stability, financial development, grid integration, innovation capacity, and social acceptance. The review highlights that solar energy, in particular, acts as both an environmental and social catalyst in advancing sustainable welfare outcomes. The findings provide a comprehensive basis for policymakers and researchers seeking to design equitable and welfare-enhancing renewable energy transitions.

For effective logistics planning and pricing strategies, it is essential to predict road freight transportation costs accurately. Using a real-world dataset with 45,569 freight offers and 52 different variables, including financial, logistical, geographical, and temporal characteristics, this study presents a data-driven method for forecasting transport prices. To create a strong predictive model, the approach combines hyperparameter optimization, evolutionary feature selection, and extensive feature engineering. Because gradient boosting works well for modelling intricate, nonlinear relationships, it was used as the main algorithm. Temporal dependencies were maintained through a nested cross-validation framework with a time-series split, which improved the generalizability of the model. With a mean absolute percentage error (MAPE) of 6.27%, the model showed excellent predictive accuracy. Key predictive factors included total transport distance, load and delivery quantities, temperature constraints, and aggregated categorical features such as route and vehicle type. The results confirm that evolutionary algorithms are capable of efficiently optimizing model parameters, as well as feature subsets, greatly enhancing interpretability and performance. In the freight logistics industry, this method offers useful insights for operational and dynamic pricing decision-making. This model may be expanded in future research to include external data sources and investigate its suitability for use in various geographic locations and modes of transportation.

This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development.