The target area of the survey is the rehabilitated flat area behind the capital cities of Vienna and Bratislava, which lies in the tourist area of Győr. Wetlands provide a backdrop for tourism products such as kite flying, cycling and walking. The city centre offers tourists an easy sightseeing tour behind the natural scenery of the Danube tributary (Szigetköz). Objective: The demographic characteristics of demand and preferences for active tourism product types and the extent of the scope of supply were analyzed. The present research also analyses the cycling routes in the region with regard to the EUROVELO 6 road network. The primary research was a quantitative (questionnaire) survey conducted between 10 September 2023 and 30 October 2023. The survey sample of 666 respondents is not representative and was selected by random sampling. The results of the research include an analysis of the demand for participation in cycling tourism and tour programs as activities requiring activity. The findings of the research provide a basis for demand-supply segmentation of sustainable active tourism product development based on physical experience according to demographic characteristics (e.g. age, education). The landscape of the wetland can be positioned for the bicycle tourists. Especially for the target group of people over 40 and for people with higher education. The scope of the guided tours, linked to the central offer, extends over an area of more than 50 km. Activating the target group helps the rehabilitated natural scenery to connect to sustainable tourism.

Introduction: New energy vehicles (NEVs) refer to automobiles powered by alternative energy sources to reduce reliance on fossil fuels and mitigate environmental impacts. They represent a sustainable transportation solution, aligning with global efforts to promote energy efficiency in the automotive sector. Aim: The purpose of this research is to investigate the influence of social demand on the business model of NEVs. Through a comprehensive analysis of consumer preferences and market dynamics, the research aims to identify strategies for driving the sustainable growth of the NEV industry in respond to societal demands. Research methodology: We conduct a questionnaire survey on 2415 individuals and evaluated that questionnaire data by multifactor analysis of variance to examine individual consumer characteristics. We employed NOVA to evaluate the differences in market penetration factors. Additionally, a regression analysis model is utilized to examine accessibility element’s effects on the consumer’s intensions to buy, addressing categorical and ordered data requirements effectively. Research findings: This research demonstrates that middle-aged and adolescent demographics show the highest willingness to purchase NEV’s, particularly emphasizing technological advancements. Consumer preferences vary based on focus like NEV type, model and brand, necessitating tailored marketing strategies. Conclusion: Improving perception levels and addressing charging convenience and innovative features are vital for enhancing market penetration and sustainable business growth in the NEV industry.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.