This study investigates the energy and environmental implications of replacing E10 gasoline with Liquefied Petroleum Gas (LPG) in a Euro 4 passenger car under real-world urban driving conditions. A comparative methodology robust to operating-state distribution was applied, combining portable exhaust gas analysis with on-board diagnostic data to calculate energy-specific emissions per crankshaft revolution and to reconstruct emission surfaces in the load–RPM domain using bilinear interpolation. The study revealed that LPG reduces carbon dioxide emissions by 8.35%, demonstrating a clear climate and energy benefit due to its lower carbon intensity. In comparison, carbon monoxide (+9.5%) and hydrocarbons (+8.3%) increased under low-load and idle conditions. Nitrogen oxides showed only minor differences between the fuels (+1.3%). LPG exhibited a more stable CO₂ emission profile, reflecting improved combustion efficiency from an energy perspective, although its performance in terms of incomplete combustion products requires further optimisation. The methodology highlights how linking energy efficiency with pollutant formation provides a comprehensive framework for evaluating alternative fuels in Real Driving Emissions (RDE) tests. The results confirm LPG’s potential to reduce greenhouse gas emissions in transport systems and identify calibration strategies needed to mitigate trade-offs in local pollutant emissions.

This research examines the risk factors that influence injury severity in individual motorcycle accidents, utilising a dataset of 5253 incidents. Five machine learning algorithms—multinomial logistic regression, classification trees, random forests, XGBoost, and neural networks—were used to classify the results into three groups: Death (13.48%), Injury (80.14%), and No injury (6.38%). In all models, passenger presence was the most important predictor of injury. Motorcycle accidents involving passengers do not always have more serious consequences for several overlapping reasons. On the one hand, a motorcycle with a passenger has a significantly higher mass, which increases the braking distance and kinetic energy at the moment of collision, hindering quick defensive manoeuvres, cornering, and reactions to sudden hazards. Often, the rider also refrains from sudden movements to prevent the passenger from losing their balance. In the case of single-rider motorcycle accidents on roadways, approximately 5% of those involved with a passenger were fatalities, while approximately 48% were uninjured; in the case of those without a passenger, no one was uninjured. It follows from the above that the presence of a passenger increases the rider’s sense of responsibility. Other factors that significantly increased risk were single-lane carriageways, vehicle overturning, contaminated road surfaces, and collisions with complex objects, e.g., like trees. The multinomial logistic regression model had an overall accuracy of 69.2% on the test set. The Recurrent Neural Network achieved the best overall accuracy of 79.56%. Balanced accuracy, as the average between sensitivity and specificity of the RNN model for the “death” class was 68.15%, for the “injury” class—72.6%, and for the “no injury” class—96.61%. The Area Under the ROC Curve of the Recurrent Neural Networks model for “no injury” was 0.97, indicating it was very good at distinguishing between this class and the other classes. Even though it was easy to tell which cases did not involve injuries, it was still hard to tell the difference between fatal and non-fatal injuries in all models. The results support interventions tailored to specific situations, such as improved road lighting and speed control in rural areas, as well as helmet enforcement and safety measures at intersections in cities.