Sustainable transportation systems are becoming an increasingly important issue around the world, especially with the advancement of urbanisation. Autonomous vehicles and ride-sharing services represent innovative mobility solutions that can improve not only the efficiency of transportation but also its environmental sustainability. The aim of this study is to examine Hungarian consumers’ attitudes toward ride-sharing and their acceptance of AVs, with a focus on whether there is a link between the two phenomena. The research is based on a nationally representative sample of 2000 respondents. Correlation analyses were performed based on the dimensions of technology acceptance models. Based on the results, a significant positive correlation can be demonstrated between the willingness to use ride-sharing services in the future and the openness towards AVs. Perceived usefulness and social influence showed the strongest relationship with intention of usage. The results contribute to the social acceptance of autonomous technologies and sustainable transport in Hungary.

The use of alternative fuels in spark ignition internal combustion engines (ICEs) has attracted growing interest in recent years due to their potential to reduce greenhouse gas emissions and decrease dependence on fossil fuels. However, differences in measurement conditions across previous studies make the consistent evaluation of available data challenging. The aim of this study is to establish and analyze a normalized database that enables the comparison of ethanol and selected synthetic fuels in terms of engine performance and regulated emissions. Based on this database, a meta-analysis is conducted to identify the optimal ethanol blending ratio using a Multi-Criteria Decision Analysis (MCDA) approach. In addition, the roles of exhaust gas recirculation (EGR) and exhaust aftertreatment systems are examined to support the interpretation of the results. The study also reviews potential feedstocks for alternative fuels, outlining their advantages, disadvantages, and the technological and environmental challenges associated with their production, which may hinder widespread adoption. Particular emphasis is placed on exhaust emissions from alternative fuels, including ethanol and synthetic (E-Fuel) blends, with a focus on key regulated species such as CO, CO₂, and NO_x. Experimental engine parameters reported in the reviewed studies are also considered, including engine displacement, fuel injection systems, and exhaust aftertreatment technologies. An MCDA-based analysis applied to the normalized cross-study dataset identifies an optimal ethanol blending ratio relative to RON95E10. The results indicate that E25 ethanol blends achieve substantial reductions in emission levels, particularly for CO (− 29%), HC (− 4%), and NO_x (− 11%), with average trends showing consistent reductions across the reviewed studies. At the same time, emission responses remain comparatively balanced within the E20–E40 range, without a systematic deterioration that would offset these benefits. Overall, the results demonstrate that standardized data normalization combined with MCDA improves the comparability of experimental studies and provides a structured basis for future research on ethanol and synthetic fuel blends, taking into account the influence of modern exhaust aftertreatment systems such as EGR, three-way catalysts (TWC), and gasoline particulate filters (GPF). Graphical abstract

This paper presents a one-dimensional (1-D) thermodynamic engine simulation validated through testbench measurements. The objective was to evaluate the accuracy of the 1-D model by comparing simulated results with experimental data from a modern 2-L turbocharged gasoline direct injection (DI) internal combustion engine featuring variable valve timing. Key parameters such as engine speed, air–fuel ratio, temperature, and pressure were measured under controlled conditions. Using AVL BOOST, simulation modeled combustion, valve timing, and thermodynamic processes across intake and exhaust systems. Simulation results were compared with experimental data across various steady-state operating points. The model demonstrated strong agreement with experimental results in steady-state operation. A few differences highlight the need for further refinement of the model. The study confirms the effectiveness of 1-D simulations as a reliable and cost-efficient tool for engine analysis and optimization. Future work will focus on enhancing the accuracy of the simulation.

The transport sector plays a significant role in air pollution, and real-world emissions measurements are becoming increasingly important. In this study, emissions from a turbocharged, direct-injection gasoline internal combustion engine (ICE) vehicle and a port fuel injection (PFI) hybrid electric vehicle (HEV) were compared using a portable emissions measurement system (PEMS) under real-world driving conditions. The CO₂, CO, NO_x, and PN emissions of the two vehicles were measured in urban, rural, and motorway sections. HEV CO₂ emissions were ~20% lower than ICE emissions in the entire Real Driving Emissions (RDE) cycle, while in urban operation, they were almost 50% lower. PN emissions were lower for HEV in rural and motorway sections than for ICE, but significant PN peaks occurred during the early urban phase, attributable to the slower engine warm-up of the HEV. Machine learning analysis (Random Forest and Extra Trees Regressor) indicated that coolant temperature was the dominant driver of HEV PN emissions. The results indicate that powertrain characteristics and thermal management strongly influence real-world driving emissions, highlighting their importance for the further development of hybrid vehicles.