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.

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, NOx, 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.