Visual attention and cognitive workload in tram driving: a multimodal eye-tracking study on the Florence T2 line

Urban public transportation systems are critical components of sustainable mobility, offering efficient and environmentally friendly alternatives to private vehicles. Within these systems, tram drivers play a central role in ensuring passenger safety, operational efficiency, and service reliability. The cognitive demands placed on tram drivers, however, are substantial. Navigating complex urban environments characterized by high pedestrian density, mixed traffic, and dynamic road conditions requires sustained attention, rapid decision-making, and the ability to manage multiple cognitive tasks simultaneously. Despite the significance of these challenges, research specifically addressing the cognitive workload, stress levels, vigilance, and visual behavior of tram drivers in real-world settings remains limited. This thesis aims to contribute to the evolving field of transport neuroergonomics by analyzing tram driver behavior under real operational conditions in Florence, Italy. By employing a multimodal monitoring approach that integrates EEG-based cognitive state assessment (via the Mindtooth System) with eye-tracking analysis (using the Tobii Pro-Glasses 3), the study offers a detailed and ecologically valid evaluation of tram drivers' mental states. The research focuses on Line T2 of the Florence tram network, a route characterized by dense urban complexity, thus providing an ideal case study for examining the interplay between environmental challenges and driver cognitive performance.