Assessing people’s satisfaction with diverse modes of transportation is complex yet crucial. This paper delves into the pivotal outcomes derived from analyzing survey data. The surveys, conducted at Aristotle University of Thessaloniki (AUTH), gauged student satisfaction with the available transport options in the Thessaloniki Metropolitan Area during 2021 and 2022, thus providing insights into trends in satisfaction levels, allowing for a deeper understanding of evolving mobility preferences. Through a comparative analysis of the data, we discerned attributes influencing participants’ transportation choices. Our research contributes to the literature by applying a standardized methodology using an algorithm developed by the European Commission; the satisfaction levels among university students for private car and public transport in those years were determined. Notably, the findings underscore that, despite a marginally higher usage of public transport, university students exhibit a greater satisfaction level with private car transportation. This implies a nuanced preference for private car usage among students, as opposed to a mere emphasis on the higher popularity of public transport within this demographic. The findings aim to provide transport operators and policymakers with valuable information in terms of ways to enhance sustainable urban mobility and improve public transport services.

Urban air pollution remains a critical public health issue, especially in densely populated cities where pedestrians experience direct exposure to traffic-related and environmental emissions. This study develops and tests a pollution-aware pedestrian routing framework for Thessaloniki, Greece, designed to minimize environmental exposure while maintaining route efficiency. The framework combines high-resolution air-quality data and computational techniques to represent pollution patterns at pedestrian scale. Air-quality is expressed as a continuous European Air Quality Index (EAQI) and is embedded in a network-based routing engine (OSRM) that balances exposure and distance through a weighted optimization function. Using 3000 randomly sampled origin-destination pairs, exposure-aware routes are compared with conventional shortest-distance paths across short, medium, and long walking trips. Results show that exposure-aware routes reduce cumulative AQI exposure by an average of 4% with only 3% distance increase, while maintaining stable scaling across all route classes. Exposure benefits exceeding 5% are observed for approximately 8% of medium-length routes and 24% of long routes, while short routes present minimal or no detours, but lower exposure benefits. These findings confirm that integrating high-resolution environmental data into pedestrian navigation systems is both feasible and operationally effective, providing a practical foundation for future real-time, pollution-aware mobility services in smart cities.