The drivers’ distraction plays a crucial role in road safety as it is one of the main impacting causes of road accidents. The phenomenon of distraction encompasses both psychological and environmental factors and, therefore, addressing the complex interplay contributing to human distraction in automotive is crucial for developing technologies and interventions for improving road safety. In scientific literature, different works were proposed for the distraction characterization in automotive, but there is still the lack of a univocal measure to assess the degree of distraction, nor a gold-standard tool that allows to “detect” eventual events, road traffic, and additional driving tasks that might contribute to the drivers’ distraction. Therefore, the present study aimed at developing an EEG-based “Distraction index” obtained by the combination of the driver’s mental workload and attention neurometrics and investigating and validating its reliability by analyzing together subjective and behavioral measures. A total of 25 licensed drivers were involved in this study, where they had to drive in two different scenarios, i.e., City and Highway, while different secondary tasks were alternatively proposed in addition to the main one to modulate the driver’s attentional demand. The statistical analysis demonstrated the reliability of the proposed EEG-based distraction index in identifying the drivers’ distraction when driving along different roads and traffic conditions (all p < 0.001). More importantly, the proposed index was demonstrated to be reliable in identifying which are the most impacting additional driving tasks on the drivers’ distraction (all p < 0.01).

Background: This paper, in the frame of social acceptance of renewable energies and innovative community-based production and consumption models, aims at supporting a data-driven approach able to deal with climate change and identify and quantify the psycho-sociological dimensions and factors that could support the transition from a technology-driven approach to a consumer-driven approach throughout the emerging “ prosumer business models”. In addition to the already existing Social Acceptance dimensions, this paper tries to identify a purely individual psychological fourth dimension to understand the processes and factors that underlie individual acceptance and adoption of new renewable energy business models, with the realization of a Prosumer Acceptance Index. Methods: Questionnaire data collection has been performed throughout an online survey platform, combining standardized and ad-hoc questions adapted for the research purposes, based on the developed theoretical model. To identify the main factors (individual/social) influencing the relation with renewable energy technology adoption, a Factorial Analysis has been conducted to identify the latent variables that are related to each other. Linear regression has been conducted to identify and quantify the factors that could better predict behavioural intention to become a prosumer. Results: Five latent psychological factors were revealed: concern about environmental issues, interest in energy sharing, concern on climate change, social influence and impact on bill cost. Three variables were found to significantly measure and predict the scores of the “Acceptance in becoming a prosumer” ad hoc scale: attitude, economic incentive and age. Conclusions: This research can facilitate policymakers and stakeholders to better understand which relevant psycho-sociological factors are intervening in the renewable energy technology acceptance processes and what and how specifically target when proposing change towards sustainable energy production and consumption.

Monitoring how attention is distributed across concurrent task demands is fundamental in daily life contexts such as mobility, education and industrial control. However, existing neurophysiological measures, typically based on univariate EEG markers, are sensitive to generic cognitive load, visual complexity, or motor activity, and therefore lack specificity for attentional splitting. Here, we introduce the Attentional Split Index (ASI), a novel EEG-based metric that employ Mutual Information (MI) theory, designed to quantify the coordinated modulation of multiple neurometrics that emerges when attention is divided across tasks. Twenty-five participants completed a realistic driving protocol combining a main driving task in two different environments (Urban, Highway) with four types of attentional-split demands, i.e., Focused, Auditory Continuous Performance Test (ACPT), Matrix, Surrogate Reference Task (SURT). Traditional neurometrics (parietal alpha, inverse frontal beta, frontal theta/beta ratio) exhibited partial sensitivity to task demands but failed to selectively reflect attentional splitting. In contrast, the ASI showed a robust and systematic increase across conditions, distinguishing not only explicit multitasking segments but also subtler differences between Urban and Highway focused driving. Eye-tracking and subjective distraction rating showed a strong and significant correlation with the ASI (Urban: r _ET = 0.515 and r _SUB = 0.744; Highway: r _ET = 0.357 and r _SUB = 0.673; all p < 10 ⁻² ), confirming high behavioural and phenomenological coherence. Surrogate analyses demonstrated that ASI effects were absent when temporal coordination across neurometrics was artificially disrupted, and that real ASI exceeded surrogate values in the vast majority of participants during multitasking but not during eyes-open baseline. Together, these findings establish the ASI as a specific, robust, and ecologically coherent neural marker of attentional splitting, with promising implications for neuroergonomics and next-generation adaptive human–machine systems.

Background: Although motorcycle helmets can save lives in case of a crash, no helmet use data are available for many countries. When data is available, it is often only analysed as a global average, preventing targeted road safety education and legislative action. In this study, we conducted a detailed analysis of motorcycle helmet use in the capital of Madagascar. Methods: Using a cross-sectional observational field survey framework, we observed 17 230 individual motorcycles. We systematically recorded motorcycle riders’ helmet use, position on the motorcycle, rider numbers and gender. Results: We found a general helmet use of 76.1%. Observed drivers had a significantly higher helmet use (84.6%) than passengers (47.7%), and subsequently helmet use per motorcycle decreased significantly when the number of riders per motorcycle increased. Female drivers had significantly higher helmet use than male drivers, and female passengers had significantly higher helmet use than male passengers. That is, on the same position of the motorcycle, female riders behaved safer than male riders. However, since female riders were more often passengers than drivers, their average helmet use was lower than that of male riders overall. Contrary to findings from other countries, motorcycle helmet use did not differ significantly throughout the day but was relatively constant. Conclusion: Our results show the potential for injury and fatality prevention in Madagascar through increased passenger helmet use. This increase would also proportionally benefit female riders more than male riders. Findings regarding road safety legislation’s applied impact, education, enforcement and future research needs are discussed.

The air traffic growth expected for future years will likely cause an imbalance between traffic demand and available capacity. This could lead to increased airspace congestion, heightened complexity, and a higher workload for controllers attempting to manage the situation. Nowadays, available tools can identify 4D Area of Relatively High Air Traffic Control Complexity (4DARHAC) events up to 20 min before they occur. Nonetheless, state-of-the-art Artificial Intelligence applications can significantly increase this prediction horizon. Powered by a combination of different Machine Learning models, the ASTRA solution aims to both detect and provide resolution strategies for 4DARHACs up to 1 h before onset. To validate ASTRA’s operational concept, a series of workshops and interviews with Flow Management Position operators were conducted, focusing on assessing the initial concept and identifying end user needs. The feedback collected was validated by a board of Subject Matter Experts (SMEs) and transformed into a concrete set of functional and non-functional requirements. Overall, ASTRA’s operational concept was endorsed as a promising solution for reducing airspace complexity while alleviating operator workload during the tactical phase of operations. Experts further highlighted the importance of integrating ASTRA with existing Flow Management Position software tools to maximize its operational impact and facilitate adoption.

Methods for the transferability of know-how, best practices and technologies across transport modes constitute an advantage for the mobility ecosystem. Transferability is an added value for practical and efficient solution design, development and implementation, as well as for solution scaling up to other transport modes. However, transferability methods oftentimes lack systematic planning and consideration for stakeholders’ and users’ needs and requirements. For that reason, the present contribution offers a User-centred transferability validation methodology to assess the transferability impact of solutions, best practices, lessons learnt, and technologies across transport modes. The following methodology is the outcome of a process (ongoing) carried out in several European projects, showing promising results for cross-modal transferability between Aviation, Road, and Maritime modes. The contribution presents the User-centred transferability validation methodology rationale that led to its design, the methodological implications for its use, the results obtained in the course of its application, and the lessons learnt and future research directions for the methodology.