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Human operators play a key role in the safe and successful conduct of maritime and aviation transport operations. Human error is often reported as a contributor to maritime and aviation accidents. Therefore, the implementation of human-informed design considerations is essential to improve safety and operational performance in both sectors, especially in the maritime sector, where there is a lack of an established framework to systematically consider human factors at the design stage. Therefore, the SAFEMODE project brings together key experts from both aviation and maritime sectors to address this important gap. The SAFEMODE project aims to deliver a framework that includes human factors considerations and enables designers to make risk-informed decisions. The methodological approach of SAFEMODE builds upon four areas: the collection and analysis of accident data; the development of a toolkit for human performance assurance, the development of Human Factors-based risk models and the creation of a framework to support risk-informed design. The type of safety events considered in SAFEMODE for both domains includes collision and grounding for the maritime sector, and runway collision, taxiway collision and wake vortex during en-route flight phase for the aviation sector. This paper will provide an insight into the efforts conducted as part of the SAFEMODE project to assess the human contribution to risk and the benefits of applying these models to support risk-informed decisions in design and operations.

The growing diffusion of fashion e-commerce websites shows the appreciation by users, highlighting the importance of offering this service also to users with different disabilities. To this end, e-commerce should be not only accessible - implementing all the technical requirements for accessibility - but also usable, paying attention to the offered user experience.

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic settings.

New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study, we aimed to demonstrate: (i) the higher resolution of neurophysiological measures in comparison to subjective ones; and (ii) how the simultaneous employment of neurophysiological measures and behavioral ones could allow a holistic assessment of operational tools. In this regard, we tested the effectiveness of an electroencephalography (EEG)-based neurophysiological index (WEEG index) in comparing two different solutions (i.e., Normal and Augmented) in terms of experienced workload. In this regard, 16 professional air traffic controllers (ATCOs) have been asked to perform two operational scenarios. Galvanic Skin Response (GSR) has also been recorded to evaluate the level of arousal (i.e., operator involvement) during the two scenarios execution. NASA-TLX questionnaire has been used to evaluate the perceived workload, and an expert was asked to assess performance achieved by the ATCOs. Finally, reaction times on specific operational events relevant for the assessment of the two solutions, have also been collected. Results highlighted that the Augmented solution induced a local increase in subjects performance (Reaction times). At the same time, this solution induced an increase in the workload experienced by the participants (WEEG). Anyhow, this increase is still acceptable, since it did not negatively impact the performance and has to be intended only as a consequence of the higher engagement of the ATCOs. This behavioral effect is totally in line with physiological results obtained in terms of arousal (GSR), that increased during the scenario with augmentation. Subjective measures (NASA-TLX) did not highlight any significant variation in perceived workload. These results suggest that neurophysiological measure provide additional information than behavioral and subjective ones, even at a level of few seconds, and its employment during the pre-operational activities (e.g., design process) could allow a more holistic and accurate evaluation of new solutions.

International audience; We investigated the contribution of specific HCI concepts to provide multimodal information to Air Traffic Controlers in the context of Remote Control Towers (i.e. when an airport is controlled from a distant location). We considered interactive spatial sound, tactile stimulation and body movements to design four different interaction and feedback modalities. Each of these modalities have been designed to provide specific solutions to typical Air Traffic Control identified use cases. Sixteen professional Air Traffic Controllers (ATCos) participated in the experiment, which was structured in four distinct scenarios. ATCos were immersed in an ecological setup, in which they were asked to control (i) one airport without augmentations modalities, (ii) two airports without augmentations, (iii) one airport with augmentations and (iv) two airports with augmentations. These experimental conditions constituted the four distinct experimental scenarios. Behavioral results shown a significant increase in overall participants’ performance when augmentation modalities were activated in remote control tower operations for one airport.

International audience; The present study aims at investigating the contribution of multimodal modalities to the context of Remote Towers. Interactive spatial sound and vibrotactile feedback were used to design 4 different types of interaction and feedback, responding to 4 typical Air Traffic Control use cases. The experiment involved 16 professional Air Traffic Controllers, who have been called to manage 4 different ATC scenarios into ecological experimental conditions. In two of the scenarios, participants had to control only one airport (i.e., Single Remote Tower context), while in the other two scenarios participants had to control simultaneously two airports (i.e., Multiple Remote Tower context). The augmentation modalities were activated or not in a balanced way. Behavioral results highlighted a significant increase in overall participants performance when the augmentation modalities were activated in Single Remote Tower context. This work demonstrates that some types of augmentation modalities can be used into Remote Tower context.

Car driving is considered a very complex activity, consisting of different main tasks and subtasks. For this reason, in particular situations the cognitive demand on the driver can be very high, and this large mental workload decreases performance and increases the probability of error commission. In this preliminary study a workload index based on electroencephalography (EEG), i.e., brain activity of eight drivers in real traffic conditions, is validated. In particular, by means of this objective workload index it has been possible to classify correctly, with accuracy higher than 75%, two driving conditions that differ in terms of difficulty, i.e., easy and hard. Eye-tracking technology was employed to validate EEG-based results. This EEG-based workload index could allow researchers to assess objectively, and even online, the mental workload experienced by drivers, and it thus forms a powerful tool for neuroergonomics research.

Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57% of road accidents and a contributing factor in most of them. In this study, 20 young subjects have been involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, have been acted. A Workload Index based on the Electroencephalographic (EEG), i.e., brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking (ET) technology and subjective measures have also been employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies. The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to ET and subjective ones. In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.

International audience; This study aims at investigating the possibility to employ neurophysiological measures to assess the human - machine interaction effectiveness . Such a measure can be used to compare new technologies or solutions, with the final purpose to enhance operator’s experience and increase safety. I n the present work, two different interaction modalities (Normal and Augmented) related to Air Traffic Management field have been compared , by involving 10 professional air traffic controllers in a control tower simulated environment . Experimental task consisted in locating aircrafts in different airspace positions by using the sense of hearing. In one modality (i.e. “Normal”), all the sound sources (aircraft s ) had the same amplification factor . I n the “Augmented” modality , the amplification factor of the sound sources located al ong the participant head sagittal axis was increased, while the intensity of sound sources located outside this axis decreased . In other words, when the user oriented his head toward the aircraft position, the related sound was amplified. Performance data , subjective questionnaires (i.e. NASA - TLX) and neurophysiological measures (i.e. EEG - based) related to the experienced workload have been collected. Results showed higher significant performance achieved by the users during the “Augmented” modality with respect to the “Normal” one, supported by a significant decreasing in experienced workload, evaluated by using EEG - based index. In addition, Performance and EEG - based workload index showed a significant negative correlation. On the contrary , subjective workload analysis did not show any significant trend. This result is a demonstration of the higher effectiveness of neurophysiological measures with respect to subjective ones for Human - Computer Interaction assessment.

This contribution offers a critical reflection on standard reactive incident reporting systems and provides an outlook towards proactive methods for monitoring risk. Incident reporting systems are often regarded as a prerequisite for effective resilience engineering, but sometimes they fail to achieve much of the expected benefits. There is now a growing body of research that criticizes incident reporting on the basis of its inability to provide an accurate representation of harm compared to other methods as well as the fact that there is still widespread underreporting of incidents. In this chapter we take a different angle by arguing that the problems encountered with incident reporting are at least to some extent to be found in the structural characteristics of the respective domains rather than within either the principle of incident reporting as such or its implementation. We identify a number of such structural characteristics that are necessary for successful incident reporting through reflection on the success and the (partial) failure of two major incident reporting systems from aviation and healthcare. Where those structural characteristics are not present, incident reporting systems are bound to encounter difficulties. In such environments, a complementary proactive risk monitoring approach may be required to maximize learning from operator and frontline feedback.

The knowledge of operational experts plays a fundamental role in performing safety assessments in safety critical organizations. The complexity and socio-technical nature of such systems produce hazardous situations which require a thorough understanding of concrete operational scenarios and cannot be anticipated by simply analysing single failures of specific functions. This paper addresses some limitations regarding state-of-the-art safety assessment techniques, with special reference to the adoption of “chain of event” models in accident causation (widely criticised by many authors), to the use of severity classes and to the adoption of the worst credible effect criterion. Such methods tend to assume a linear link between single hazards considered in isolation and corresponding consequences for safety, thus neglecting the intrinsic complexity of the systems under analysis and reducing the opportunities for an effective involvement of operational experts. An alternative approach is proposed to overcome these limitations, by distinguishing different typologies of hazards and integrating the analysis of single functions with the study of concrete operational scenarios.

In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states. The objective of the proposed study was to provide an example of p-BCIs used to evaluate the users' mental workload in a real operational environment. For this purpose, through the facilities provided by the Ecole Nationale de l'Aviation Civile of Toulouse (France), the cerebral activity of 12 professional air traffic control officers (ATCOs) has been recorded while performing high realistic air traffic management scenarios. By the analysis of the ATCOs' brain activity (electroencephalographic signal-EEG) and the subjective workload perception (instantaneous self-assessment) provided by both the examined ATCOs and external air traffic control experts, it has been possible to estimate and evaluate the variation of the mental workload under which the controllers were operating. The results showed (i) a high significant correlation between the neurophysiological and the subjective workload assessment, and (ii) a high reliability over time (up to a month) of the proposed algorithm that was also able to maintain high discrimination accuracies by using a low number of EEG electrodes (~3 EEG channels). In conclusion, the proposed methodology demonstrated the suitability of p-BCI systems in operational environments and the advantages of the neurophysiological measures with respect to the subjective ones.

This paper addresses the issue of redesigning a train list. The train list is a paper document of few pages that provides train operators of the Italian railway company with basic information about the features of the railway line they are moving along, the means of traction and engine characteristics. Methodologies borrowed from the fields of document design and human–computer interaction were applied to the analysis, redesign, and evaluation of a new train list following a first prototype developed by the railway company. In all phases (analysis, redesign, evaluation) the study was guided by the theoretical framework of cultural-historical psychology, according to which artifacts historically shaped by human practice always mediate human cognition. The results of the evaluation provide empirical evidence to support the added value of combining document design techniques with the heuristic role of the historical analysis of an artifact, especially in a safety-critical context, so as to properly improve tools and instruments used in human activity and thus reduce the risk of breakdowns.

This paper presents and discusses the application of safety assessment methodologies to a pre-operational project in the Air Traffic Control field. In the case analysed in the present paper a peculiar aspect was the necessity to effectively assess new operational procedures and tools. In particular we exploited an integrated methodology to evaluate computer-based applications and their interactions with the operational environment. Current ATC safety practices, methodologies, guidelines and standards were critically revised, in order to identify how they could be applied to the project under consideration. Thus specific problematic areas for the safety assessment in a pre-operational experimental project are highlighted and, on the basis of theoretical principles, some possible solutions taken into consideration. The latter are described highlighting the rationale of most relevant decisions, in order to provide guidance for generalisation or re-use.

The design of future cities is a challenging endeavour, to which interaction design can contribute by applying its well-established principles and methods. Interaction design methods aim at optimising interactions between humans, tools and environments, considering material tools, immaterial aspects like the interaction modalities, contextual aspects. This contribution is structured like a literature review, discussing the applicability of design methods into urban settings. The first part reviews the key features and scope of three different design methods (nudges, persuasive technologies, and design thinking) and proposes a categorisation of city layers to identify possible intervention focuses. The following of this contribution discusses one major issue that an interaction design approach may encounter while intervening in a city, i.e. how to structure a user-centred process when users are in the order of a multitude, hence hard to be segmented. In the last section, the concept of breakdown, derived from the human-computer interaction literature, is proposed as a way to partially overcome this issue.

International audience; Adaptive Automation (AA) is a promising approach to keep the task workload demand within appropriate levels in order to avoid both the under- and over-load conditions, hence enhancing the overall performance and safety of the human-machine system. The main issue on the use of AA is how to trigger the AA solutions without affecting the operative task. In this regard, passive Brain-Computer Interface (pBCI) systems are a good candidate to activate automation, since they are able to gather information about the covert behavior (e.g., mental workload) of a subject by analyzing its neurophysiological signals (i.e., brain activity), and without interfering with the ongoing operational activity. We proposed a pBCI system able to trigger AA solutions integrated in a realistic Air Traffic Management (ATM) research simulator developed and hosted at ENAC (École Nationale de l'Aviation Civile of Toulouse, France). Twelve Air Traffic Controller (ATCO) students have been involved in the experiment and they have been asked to perform ATM scenarios with and without the support of the AA solutions. Results demonstrated the effectiveness of the proposed pBCI system, since it enabled the AA mostly during the high-demanding conditions (i.e., overload situations) inducing a reduction of the mental workload under which the ATCOs were operating. On the contrary, as desired, the AA was not activated when workload level was under the threshold, to prevent too low demanding conditions that could bring the operator's workload level toward potentially dangerous conditions of underload.