Sport tourism is a fast-growing segment of tourism offering new perspectives and supporting travelers’ behavior shift towards active living that is a boost for sustainable destinations. These interrelations between active living, active travelling, and sport tourism have a powerful environmental, economic, and social impact. Based on the recognized contribution of sport tourism in sustaining destinations, the current paper aims to (a) explore the state of sport tourism in the Adriatic–Ionian Region by identifying existing sport tourism initiatives, (b) analyze the current and future potentials of sport tourism in the area’s sustainable growth, and (c) draw policy recommendations for sport tourism development in the region with a view to support the wider vision of sustainability. The review of existing sport tourism cases, revealing an existing investment towards this tourism form, was followed by a qualitative survey of the area’s tourism stakeholders (Greece, Italy, Slovenia, Croatia, Albania, Montenegro, Serbia). Results reveal that sport tourism is estimated to help in building a unique identity closely linked to sustainability goals—the area represents a great natural and cultural beauty that can be emphasized by sport initiatives, while, once such efforts are incorporated in wider sustainability plans, the destinations’ profiles can be significantly upgraded.

Mobility patterns and travel behavior aspects of students traveling to universities, are gaining attention, as the completion of such every day trips are rather complicated. University students form a social group, essentially autonomous in terms of mode choice decision making. Understanding the mode choice decision process, may reveal the most appropriate interventions for achieving sustainable transport goals. The current study aims to examine various aspects of university students’ travel bahaviour such as travel distance, travel time, comfort and safety in the city of Xanthi, Greece. Moving a step forward, the paper provides a better understanding of students’ travel patterns in two different environments due to the University relocation from an urban environment to a more isolated one. For this purpose, a questionnaire survey is conducted where students describe their travel habits before and after the relocation. In addition, students are given a choice set comprised of eight different factors hypothesized to internalize the effect of the relocation on mode choice for the trips to and from the University and they are asked to prioritize them ranking them from the most significant one to the least important. For the analysis two Multinomial Logit models are developed. The results verify initial considerations; distance and time are the most important factors for both cases while the use of public transport instead of walking increases the importance of economy and safety.

This study investigates how lifestyle factors influence mobility choices within the metropolitan area of Thessaloniki, which is the second largest city in Greece. The primary objective is to understand the interplay between lifestyle attitudes and preferences for sustainable mobility options. This research utilizes data from a comprehensive survey conducted among Thessaloniki’s inhabitants, identifying four distinct attitudinal clusters shaped by lifestyle characteristics and general attitudes toward recycling, energy conservation, social distancing, and other factors. A two-step clustering approach combining hierarchical and k-means methods as well as Chi-square and Kruskal–Wallis tests were employed to investigate whether life attitudes are correlated with revealed and stated mode choices as well as beliefs, perceptions, and feelings related to the current and future public transport systems and micromobility options in the metropolitan area of Thessaloniki. The findings reveal significant variations in mobility choices across the identified lifestyle segments with environmentally conscious segments showing a higher propensity for sustainable modes of transport. Additionally, differences are observed in perceptions and intentions regarding micromobility and the future mass transit system within the region, suggesting that when introducing a new metro system that is expected to promote a shift toward public transport, segment-specific approaches are necessary for raising awareness and increasing trust to the new mode.

The analysis and modeling of parameters influencing parents’ decisions regarding school travel mode choice have perennially been a subject of interest. Concurrently, the evolution of artificial intelligence (AI) can effectively contribute to generating reliable predictions across various topics. This paper begins with a comprehensive literature review on classical models for predicting school travel mode choice, as well as the diverse applications of AI methods, with a particular focus on transportation. Building upon a published questionnaire survey in the city of Thessaloniki (Greece) and the conducted analysis and exploration of factors shaping the parental framework for school travel mode choice, this study takes a step further: the authors evaluate and propose a machine learning (ML) classification model, utilizing the pre-recorded parental perceptions, beliefs, and attitudes as inputs to predict the choice between motorized or non-motorized school travel. The impact of potential changes in the input values of the ML classification model is also assessed. Therefore, the enhancement of the sense of safety and security in the school route, the adoption of a more active lifestyle by parents, the widening of acceptance of public transportation, etc., are simulated and the impact on the parental choice ratio between non-motorized and motorized school commuting is quantified.

Human behavior pattern recognition exploited using Artificial Neural Networks (ANN) is at the core of Artificial Intelligence (AI) classification applications. School trip transport forecasting mode selection based upon ANN forecasting ability provided an easy-to-use scientific toolkit for sustainable urban policies designing and implementation. The paper capitalized acknowledged forecasting ability of ANN to recognize and classify behavior patterns leading to parental decisions of school mode choice. Main stages of this research included the conduction of an extended questionnaire survey in 512 parents of school students in Thessaloniki (northern Greece) and the investigation whether ANN forecasting model could classify school mode choice. Research provided promising results (forecasting ability ranging from 76% to 93%) on school mode parental selection forecasting models based on ANN classifier, providing a solid proof of concept for further investigation.