Search Results (3)

Generation Z, known for its digital fluency and distinct consumer behaviors, is an increasingly influential demographic in the tourism industry. As a sustainability-focused generation, their preferences and behaviors are shaping the future of travel. This study explores the tourism experiences of Romanian Generation Z members, focusing on their travel patterns, motivations, information sources, and service preferences. A bibliometric analysis of the existing literature was conducted to identify research trends and gaps in understanding Generation Z’s tourism behaviors. Using a mixed-method approach, the study integrates survey data from 399 respondents with AI-generated insights from ChatGPT 4o mini to compare traditional research methods with AI-driven analysis. It examines how AI interprets and predicts travel behaviors, highlighting the reliability and biases inherent in AI models. Key discrepancies between the two methods were found: The survey indicated a preference for car travel and commercial accommodation, while AI predictions favored air travel and private accommodation. Additionally, AI emphasized a growing interest in eco-friendly transportation and connections to natural and cultural environments, offering a broader scope than the survey alone. Both methods revealed a trend toward digital platforms for travel planning, moving away from traditional agencies. The findings suggest that AI can complement traditional research by providing actionable insights, though its limitations emphasize the need for a balanced integration of both methods. This study offers new perspectives on Generation Z’s tourism experiences. Full article

In the dynamic and evolving tourism industry, engaging with stakeholders is essential for fostering innovation and improving service quality. However, tourism companies often struggle to meet expectations for customer satisfaction through interactivity and real-time feedback. While new digital technologies can address the challenge of providing personalized travel experiences, they can also increase the workload for travel agencies due to the maintenance and updates required to keep travel details current. Intelligent chatbots and other generative artificial intelligence (GAI) tools can help mitigate these obstacles by transforming tourism and travel-related services, offering interactive guidance for both tourism companies and travelers. In this study, we explore and compare the main characteristics of existing responsive AI instruments applicable in tourism and hospitality scenarios. Then, we propose a new theoretical framework for decision making in the tourism industry, integrating GAI technologies to enable agencies to create and manage itineraries, and tourists to interact online with these innovative instruments. The advantages of the proposed framework are as follows: (1) providing a comprehensive understanding of the transformative potential of new generation AI tools in tourism and facilitating their effective implementation; (2) offering a holistic methodology to enhance the tourist experience; (3) unifying the applications of contemporary AI instruments in tourism activities and paving the way for their further development. The study contributes to the expanding literature on tourism modernization and offers recommendations for industry practitioners, consumers, and local, regional, and national tourism bodies to adopt a more user-centric approach to enhancing travel services. Full article

A real-time, metadata-driven electric vehicle routing optimization to reduce on-road energy requirements is proposed in this work. The proposed strategy employs the state–action–reward–state–action (SARSA) algorithm to learn the EV’s maximum travel policy as an agent. As a function of the received reward signal, the policy model evaluates the optimal behavior of the agent. Markov chain models (MCMs) are used to estimate the agent’s energy requirements on the road, in which a single Markov step represents the average energy consumption based on practical driving conditions, including driving patterns, road conditions, and restrictions that may apply. A real-time simulation in Python with TensorFlow, NumPy, and Pandas library requirements was run, considering real-life driving data for two EVs trips retrieved from Google’s API. The two trips started at 4.30 p.m. on 11 October 2021, in Los Angeles, California, and Miami, Florida, to reach EV charging stations six miles away from the starting locations. According to simulation results, the proposed AI-based energy minimization framework reduces the energy requirement by 11.04% and 5.72%, respectively. The results yield lower energy consumption compared with Google’s suggested routes and previous work reported in the literature using the DDQN algorithm. Full article