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Autonomously driving shuttles with electric drive are envisioned to play a complementary role in public transportation in the near future. These vehicles may be used in scenarios where the use of full-sized busses is not technically feasible or economically viable. The compact size of automated shuttles offers the opportunity to operate in narrow streets, to connect suburban areas and to supply on-demand first and last mile services. The integration of an on-demand shuttle service into public transportation was tested and evaluated in a real-world trial in Hamburg-Bergedorf (Germany). A survey covering a large variety of aspects of user experience was created and distributed among users. Overall, the passengers were satisfied with the on-demand shuttle service and indicated that they could imagine to use such a service in the future. The field trial provided valuable insights and revealed the development potential of on-demand shuttle services in public transportation, motivating to continue research in this area.

New mobility concepts in public transport will benefit from automated driving systems. Nevertheless, fully automated vehicles are not expected within the next years. For this reason, a remote operating fallback authority might be a promising solution. To cope with highly complex automation tasks, teleoperation with a distinct human-machine interaction could be used. This work describes a task analysis in order to derive requirements for the design of a future control center workplace which deals with the control of driverless shuttles in combination with mobility-on-demand services in public transport. The results will contribute to create an efficient, valid and capable human-machine interaction concept for vehicle teleoperation.

This report synthesises insights from qualitative exploratory research. It is based on two different research approaches a) a social media content analysis and b) in-depth interviews with disabled people. Both focus on mobility needs, challenges, and attitudes toward mobility solutions and criteria for travel and disabled users’ approach to the transport service barriers they face daily. The current COVID-19 pandemic situation has required a rethinking of the original methodology. Instead of shadowing, a social media content analysis was performed to identify public transport use barriers in the cities under consideration. Furthermore, faceto-face interviews were replaced with phone/online interviews. During the qualitative research, the data was analyzed to create user profiles and inform the development of a questionnaire to gauge the broader disability community's attitudes and other vulnerableto-exclusion groups towards future transport systems. For this part, we will develop an accessible online questionnaire and engage transport users with disabilities as respondents in various European cities. We aim to reach a minimum of 500 disabled citizens with different access needs by the end of the project to allow for a cross-country comparison. The results of the qualitative study revealed that in all the pilot cities, disabled passengers are still restricted in many aspects when it comes to choosing public transportation. However, people living in the largest cities still have more options than disabled people living in more remote or rural areas. Based on our qualitative study results, the interviewees in all the cities noted some investment had been made in reducing the barriers for disabled persons who travel in Public Transport. Nevertheless, they also confessed that most investments were made in engineering solutions: ramps to enable people in wheelchairs to change level; audio information to support visually impaired people. When regarding the needs of people with mental health problems, it is clear that much less has been done.

The Human Factors methodological framework was developed to evaluate the effectiveness of selected safety measures in terms of making level crossings (LCs) more self-explaining and forgiving, and hence increasing their safety. The methodological framework includes a practical Human Factors Assessment Tool (HFAT) accompanied by an implementation guide which presents how the HFAT can be used in a real case study. The purpose of this deliverable is to summarise the theoretical background of the Human Factors methodological framework and the development process of the first version of the Human Factors Assessment Tool. In addition, this deliverable aims to explain how the HFAT was adjusted and updated in the second part of the project based on feedback obtained during the HFAT testing phase in four of the project’s pilot tests, covering 14 measures.

This deliverable collects the main results obtained from evaluations of the piloted safety measures selected in earlier phases of the SAFER-LC project. This deliverable reports the descriptions of the piloted measures, method and data to evaluate the safety effects of the selected measures, as well as the results of evaluations together with their discussion. More detailed information about the implementation of the measures and execution of pilots can be found from deliverable D4.3 of the SAFER-LC project (Carrese et al., 2019). In some cases, deliverable D4.3 also reports details on the development of the measure. The main inputs for this deliverable from other SAFER-LC activities originate from Work Package 2 (WP2), Work Package 3 (WP3) and earlier tasks of WP4. The earlier deliverables of WP4 produced implementation guidelines for the pilots (D4.1; SAFER-LC Consortium, 2018a) by providing an overview of the major testing environments that were available for piloting in the SAFER-LC project. The available pilot test environments ranged from simulation environments to real (or close to real) traffic circumstances. Deliverable D4.2 (SAFER-LC Consortium, 2018b) describes the proposed evaluation framework including a list of parameters from which the partners could select the most appropriate ones for the evaluation of their pilot. The identified Key Performance Indicators (KPIs) were arranged into five categories: ‘Safety’, ‘Traffic’, ‘Human behaviour’, ‘Technical’, and ‘Business’. Finally, the deliverable D4.3 (Carrese et al., 2019) describes the pilot activities carried out in WP4 by documenting the implementation and execution of pilots in various level crossing environments in different countries. This deliverable reports the evaluation results of 21 safety measures that were piloted at eight pilot sites during the SAFER-LC project. The number of piloted safety measures varied by pilot site and the pilot test sites varied from simulation studies to controlled conditions and real railway environments. In some cases, the selected measures were not suitable for piloting in a real world experimental context and/or the implementation in real railway environment was not feasible, for example, due to financial resources, timing of our piloting period and/or lack of suitable pilot site(s). Therefore, pilot test sites in the SAFER-LC projects varied from simulation studies to controlled conditions and real railway environments. Some of the measures (‘In-vehicle warnings to driver’, and ‘Additional lights to train front’) were tested in two different environments to collect complementary information on their safety effects via two types of installation. Due to the nature of the conducted pilots (small-scale pilot tests), it was hardly possible to calculate any quantitative estimates for safety effects of the measures in terms of annual reductions in the number of LC fatalities and/or accidents based on the results of the pilots. However, since numerical estimates of safety effects are needed for cost-benefit calculations (WP5 of the SAFER-LC project), the authors made an attempt to draw these estimates based on the applicability of safety measures to different LC types, road users and behaviours leading to LC accidents based on pre-existing information on the effects of LC safety measures. The authors acknowledge that many uncertainties are related to these estimates. However, the assumptions used in the calculations are clearly documented and hence the estimates can be easily updated if more detailed statistics or more information on safety effects become available. Therefore, a detailed documentation of LC accident data (information on additional variables and details) is highly recommended to enable drawing of these estimates. Based on the safety potential calculations presented in chapter 5 the piloted measures that were estimated to have the highest safety benefits are: − Additional lights at the train front, covering measures ‘Additional warning light system at front of the locomotive (6.0–12.0%)’ and ‘Improved train visibility using lights (6.0–30.0%)’. This measure was estimated to have rather high effectiveness (prevention of 15–30% of relevant LC accidents) and target rather large share of LC accidents (19.9−96.3% depending on the approach). − In-vehicle train and LC proximity warning (4.4–15.0%). It is important to be noted that the effectiveness of this measure depends on the usage of the in-vehicle devices. In practice, the car driver needs to install the application on a smart mobile device, and location tracking should be enabled on this device while driving. Furthermore, the driver needs to allow the application to run seamlessly on the background and also notice the visual or auditory warning in order to perform the required action on time (e.g. stop before the LC). However, these latter requirements are valid for all LC safety measures. − Speed bumps and flashing posts (2.0–8.0%). This accident reduction estimate concerns the situation where the measure is implemented to passive LCs (where the highest safety effects were expected in Dressler et al. 2018). − Blinking lights drawing driver attention (Perilight) (2.0–8.0%). This measure is targeted to passive LCs. Some concerns on applicability of piloted safety measures in different railway environments are listed below: − Written letters on ground and coloured road marking: Any road marking can only be applied on a paved road with an even surface. Thus, the message written on the road does not hold for road environments such as gravel roads, cobblestone, tracks etc. Furthermore, these measures are not perfectly suitable to countries with snow and long winter with darkness. − Noise-producing pavement and speed bumps: These measures are not well suited to gravel roads. In addition, these measures are not effective in case of snow. − Blinking amber light with train symbol and blinking lights drawing driver attention (Perilight): It is important to note that these measures are targeted to passive LCs and require power. However, in practice many of passive LCs no mains power is available and thus other alternative power sources need to be investigated. The effectiveness of these measures was estimated somewhat lower than active LCs with sound and/or light warning since the warning in these measures is linked to LC approach and not to actual arrival of train. − In-vehicle train and LC proximity warning: This system may not operate satisfactory for LCs surrounded by roads on which Global Navigation Satellite System (GNSS) reception is poor. Overall, the safety effect results of the piloted measures are promising. Therefore, it is recommended that some of most promising measures will be tested in larger scale real world experiments with well-planned research designs to obtain more information on their effects (also on long term) on road user behaviour and thus on road safety. This would also support the more exact numerical estimation of safety effects of the piloted measures. The results of this deliverable will serve as input for WP5 that deals with cost-benefit analyses. The estimates of safety effects of each measure will be used in cost-benefit or cost-effectiveness calculations and the experiences collected during the piloting will support the drawing of final recommendations for the SAFER-LC project.

This deliverable describes the methods applied and the results achieved during the evaluation of new human-centred low-cost measures to improve safety at level crossings (LCs). The work presented in this deliverable focuses on enhancing the safety performance of level crossing infrastructures from a human-factors perspective, making them more self-explaining and forgiving. The main inputs used in the evaluation were: the SAFER-LC: the developed human factors methodological framework which was developed within the SAFER-Lc project and the results of the pilot tests of innovative LC safety measures performed also as part of the SAFER-LC project.

This deliverable collects the main results obtained from evaluations of the piloted safety measures selected in earlier phases of the SAFER-LC project. This deliverable reports the descriptions of the piloted measures at railway level crossings, method and data to evaluate the safety effects of the selected measures, as well as the results of evaluations together with their discussion.

The points where roads intersect with railroads, called level-crossings, have traditionally been considered dangerous due to the excessive severity of accidents that have occurred involving all kinds of road vehicles and trains. They are most frequently protected with regular safety systems like barriers of light signals, however numerous level-crossings still remain unprotected. In addition, the maintenance status of level crossings differs strongly across Europe. As a consequence, some level crossings pose serious safety threats to road- and rail users, if the level crossing safety infrastructure does not exist or is not properly maintained and operated. Especially at level crossings in urban areas of municipalities in developing countries in Europe, it is often overly costly or even impossible to protect a high number of level crossings and maintain them on a regular basis. Digitalization, the huge coverage of mobile telecommunications networks and the penetration of smart phones and mobile devices in society offer a starting point for alternative, hi tech safety measures, that promise to be cheap as well as effective. Such a system has been developed and tested in Thessaloniki, where a mobile device elicits an auditory as well as a visual warning whenever the user drives in close proximity and heading to any of the 30 level crossings located in the suburbs of the city. Some trains have also been equipped with Global Navigation Satellite System (GNSS) sensors which record and transmit highly accurate spatiotemporal data, so when they approach the level crossings the mobile users also receive the estimated time of arrival on their smart phones and tablets. Three taxis were equipped with a naturalistic driving platform, consisting of cameras and a GPS sensor, to track the behavior of the drivers whenever they approached level crossings. The equipment was installed taking into consideration both the safety of all passengers and the privacy of customers. Only the drivers and surroundings of the vehicles were recorded, for a prolonged period that lasted more than two months and resulted in more than 1TB of relevant video data for further analysis. During the first month the alert system was not activated, in order to collect baseline data. The safety relevant behavior of the drivers in the context of level crossings before the implementation of the assistance system was compared to their behavior when using the system.

This report describes the execution of pilot tests within the SAFER-LC project. Specifically, this report concerns the implementation and the execution of the tests built in various level crossing environments in different countries. Simulation tools (in-vitro) and prototype systems running in close-to-reality situations under controlled conditions were used for better understanding human reaction to the proposed measures and to optimize the system operation and design, by means of testing and fine-tuning the solutions developed within the project. Additionally, various measures were tested under these environments for cases too dangerous or complex to test in the real-world pilot activities. The integrated tests were implemented in the test-track hosted by the Aachen University, where the whole chain, from detection and communication to awareness increase and barriers operation have been demonstrated. In addition, some functionalities were tested under real-world conditions. The simulation, controlled and field tests were carried based on the use cases defined in the beginning of the project.

This deliverable describes the methods applied and the results achieved during the first phase of Task 2.3 within the SAFER-LC project: the design of new human-centred low-cost measures to improve safety at level crossings (LCs). The European project SAFER-LC – Safer level crossing by integrating and optimizing road-rail infrastructure management and design – aims to improve safety in road and rail transport by minimising the risk of LC accidents, focusing on both technical solutions and human processes. Within the project, the objective of Work Package 2 (WP2) is to enhance the safety performance of level crossing infrastructures from a human factors perspective, making them more self-explaining and forgiving. Task 2.3 specifically aims to design concepts of human-centred low-cost countermeasures to enhance the safety of current LC infrastructures and, in a later step, to evaluate these countermeasure designs from a human factors perspective. A two-stage process, consisting of a collection phase and a selection phase, was adopted to define the countermeasure concepts presented in this report. In the first phase, a large pool of design ideas was collected from three different sources: (1) a comprehensive review of the research literature, (2) an analysis and selection of theoretical models relevant to explaining and predicting road user behaviour at level crossings, and (3) a design workshop with road and rail experts. In the second phase, three steps were undertaken in order to systemize and prioritize the measures collected: (1) an elimination of measures based on redundancy, feasibility, and expert ratings of their effectiveness and cost, (2) a classification of the remaining measures with respect to their applicability to different LCs and road user types, and their effect mechanism, and (3) a ranking of the measures based on their prospects for accident risk reduction and the need for further research. The design process was based on operational descriptions of different types of road user behaviours observed at LCs that challenge safety and hence need to be defined as the target of safety measures. The presence or absence of active controls and barriers at LCs was identified as a particularly significant factor with regard to what types of behaviour need to be supported or prevented. Therefore, the design thinking process and organization of measures drew upon the basic distinction between passive and active LCs. Measures for passive LCs were mainly to address the problems of road users insufficiently scanning the tracks for trains, insufficiently adapting their approach speed to the need of scanning and the potential need to stop, and road users getting stuck on the rails. Measures for active LCs were mainly to prevent road users from circumventing closed barriers (climbing over / below; swerving around half-barriers), passing the LC in spite of active light signals (e.g. flashing red light), passing the LC after pre-signalling has begun or while barriers are closing, and, again, getting stuck on the rails. Apart from the differences, a range of common possibilities to support safe road user behaviour at both active and passive LCs was identified (e.g. by improving LC conspicuity, using common means of conveying behavioural recommendations adapted to the respective LC type, and helping road users not to enter the tracks when they cannot be sure to leave in good time). In all cases, design considerations included vulnerable (VRU) as well as motorized road users (MRU). The process resulted in a list of 89 design solutions that can be applied in LC design. The complete list is given in Annex A of this report. The ten measures achieving the best ranks in each of the aforementioned use cases were: Passive LCs: Active inverted speed bumps, laser illumination of the crossing, image process warning, blinking peripheral lights drawing driver attention, light markings in the road to highlight the waiting line, speed bumps on approach to the LC, on-road flashing markers, road swivelling, LC attention device, and coloured marking of the danger zone. LCs with barriers: Adapting the timing of LC closure to the actual speed of the passing train, camera based enforcement (prosecution of violations), additional display "Two Trains", second chance zone, sound warning indicating an approaching train, lane separation in front of half barriers, increasing the length of the barrier, audible signal while in the danger zone, information countdown to closing the barrier and complete open / close cycle. All types of LCs: Proximity message via connected device (in- vehicle display, satnav, mobile device), improving train visibility using lights, audible warnings about LC, extended "no stop" zone, message on smartphone / -watch to warn on approaching train (VRU), coloured pavement markings to mark the danger zone (MRU), satnav intelligence, countdown to train arrival, LED enhanced traffic signs and warning sign to avoid blocking back. The next steps within the SAFER-LC project will be to conduct empirical tests on selected measures to evaluate their effects on road user behaviour and LC safety, and to integrate the project’s practical results and recommendations in a toolbox to be accessed through a user-friendly interface to support rail and road stakeholders in improving safety at LCs.

(1) Human factors vision in SAFER-LC - Rationale and approach (2) Analyzing the effect of countermeasures on human behaviour and safety - Application of a Human Factor Methodological Framework (3) Human-centered cost-effective measures – From design to evaluation (4) Discussion

Level Crossings represent vulnerable points in the European land transport system, since collisions at level crossings account for a high number of fatalities and serious injuries among road users and lead to major disruptions of railway operations. This paper presents part of the ongoing work conducted in the SAFER-LC EU project, namely an overview of indicators for level crossing safety solutions, taking into account the road and rail users' perspective. The paper identifies key safety criteria concerning the requirements coming from various types of level crossing users. The analysis focuses on human errors, attentional processes and risk perception, while paying special attention to vulnerable users such as pedestrians. The results are discussed in the context of a Human Factors methodological framework which analyses how technological and non-technological safety measures can be better adapted from a user perspective to make level crossings more self-explanatory and 'forgiving'. The implications for the implementation and evaluation of 'user-friendly' safety measures at level-crossings are also discussed.

Human inattentiveness or risk-taking can easily compromise the safety offered by warning signs and approved safety systems in rail and road traffic. For this reason, level crossings remain one of the major vulnerable spots within the railway system. The degree of safety at level crossings does not solely depend on the level of reliability of technical safety systems. Since the safety standard of technical systems at level crossings is extremely high, the most variable factor and most critical point in the context of the level crossing is road user behavior. Within the joint European research project SAFER-LC, human factors were therefore identified as an important starting point to find and develop effective measures that can enhance safety at level crossings. A huge variety of safety measures has been proposed in the past. Some measures focus on improving the visibility of trains and infrastructural elements, some appeal to road users’ understanding of the situation through informative signs or warnings, others use structural changes in the road surface to induce speed reduction. This article provides an overview of road-user-centered measures proposed in the literature to enhance the safety of traverse at level crossings. A descriptive model of three fundamental steps in human information processing – perception of the level crossing, activation of relevant knowledge and formation of behavioral intention – serves as a framework to systemize and categorize road-user-centered safety measures from the literature. The overview offers a psychological perspective on measures that could be implemented by rail and road infrastructure managers to enhance safety at level crossings.

Im hochsicheren System "Bahn" stellen Kreuzungen mit dem Straßenverkehr nach wie vor Punkte mit erhöhtem Risiko dar. Das europäische Projekt SAFER-LC zielt auf die Identifikation von Unfallursachen und die Ableitung von Maßnahmen zur Erhöhung der Sicherheit an Bahnübergängen ab. Dabei werden das physische Erscheinungsbild von Übergängen, existierende Systeme von Signalen und Barrieren und die technischen Möglichkeiten der Digitalisierung und Vernetzung ebenso unter die Lupe genommen wie die Strukturen und Prozesse, in welche die Abwicklung des Schienen- und Straßenverkehrs eingebettet ist.

Müdigkeitswarnsysteme (MWS) sind eine relativ neue Form der Fahrerassistenz, in deren Entwicklung der Forschungsschwerpunkt bisher deutlich auf der Detektionsseite lag. Wenig ist darüber bekannt, wie wirksam diese Systeme tatsächlich das Risiko müdigkeitsbedingter Unfälle mindern und welche Faktoren der Warnungsgestaltung für die angestrebte Sicherheitswirkung ausschlaggebend sind. Mit Blick auf den problematischen Fahrerzustand kann grundsätzlich nur eine Reduktion der Müdigkeit das Sicherheitsrisiko effektiv verringern. Ziel einer Warnung ist daher, den Fahrer zu einer Pause zu bewegen, in der er Koffein zu sich nehmen und / oder einen Kurzschlaf halten sollte. Allerdings wirken auf Ebene der Überzeugungen, Einstellungen und Motivation des Fahrers eine Reihe psychologischer Prozesse diesem Zielverhalten entgegen. Zwei besondere Herausforderungen für die Warnungswirkung sind die Tendenz zu unrealistischem Optimismus in der Einschätzung des persönlichen Risikos und die Tendenz zur Vermeidung unmittelbarer subjektiver Handlungskosten der Pause. Vor diesem theoretischen Hintergrund wurde die Wirkung verschiedener Warnungsvarianten auf Kognitionen, Motivation und Verhalten untersucht. Zu diesem Zweck wurde ein neues experimentelles Paradigma entwickelt, mit Hilfe dessen im Fahrsimulator die Anreizsituation einer realen Warnungssituation nachgestellt werden kann. Zunächst wurde durch teilweise Schlafdeprivation und die Ausnutzung circadianer Leistungstiefs ein hohes Grundniveau von Müdigkeit bei den Teilnehmern erzeugt. Ergänzend wurde eine Konstellation aus möglichen finanziellen Gewinnen und Verlusten geschaffen, die die grundlegenden Elemente der Motivationslage des Fahrers in einer realen Warnungssituation nachbildet. Während simulierter Fahrten auf einer monotonen Strecke wurde der Fahrerzustand überwacht. Bei Erreichung des definierten Müdigkeitskriteriums erhielten die Fahrer eine von mehreren möglichen Rückmeldungen. In Experiment 1 wurden zwei Warnungsvarianten verglichen: eine Standardwarnung (Kaffeetassensymbol) und eine Warnung mit einem Bild des Fahrers aus der aktuellen Situation, die mit Fokus auf eine hohe Überzeugungswirkung neu entwickelt wurde. Auf der Ebene verhaltensrelevanter Überzeugungen wies die Gruppe der mit eigenem Bild gewarnten Fahrer hierbei höhere Ausprägungen hinsichtlich der Gefährdungswahrnehmung und hinsichtlich der wahrgenommenen Effektivität einer Pause auf. Die Inzidenz von Pausen war jedoch insgesamt sehr gering. In Experiment 2 wurden die Anreizbedingungen verschärft und die Wirkung beider Warnungsvarianten mit einer Situation ohne Warnsystem verglichen. Hierbei zeigte sich, dass die Standardwarnung die Gefährdungswahrnehmung und Pausenwahrscheinlichkeit zwar in erwarteter Richtung, aber nur in sehr geringem Umfang beeinflusste. Die Warnung mit eigenem Bild führte konsistent zu größeren Effekten in verhaltensrelevanten Überzeugungen und beobachteten Pausen. In beiden Experimenten wurde zudem der situative Kontext der Warnung variiert, indem die Warnung entweder relativ nah dem Ziel oder in höherer Zielentfernung gegeben wurde. Erwartungsgemäß wirkte sich eine größere Nähe zum Ziel erschwerend auf Pausenmotivation und –verhalten aus. Die Ergebnisse geben erste Anhaltspunkte darauf, wie durch eine stärkere Berücksichtigung der Überzeugungs- und Motivationsfunktion in der Warnungsgestaltung die Sicherheitswirksamkeit von Müdigkeitswarnsystemen erhöht werden kann. Neben der Entwicklung von Detektionssystemen sollte sich empirische Forschung in Zukunft verstärkt auf die messbaren Sicherheitseffekte von Warnungen sowie die vermittelnden psychologischen Mechanismen richten, damit auch die bisher wenig berücksichtigte Outputseite von Müdigkeitswarnern optimiert werden kann. Drowsiness warning systems are a relatively new kind of driver assistance. In their development so far, there was an articulate research focus on the problem of drowsiness detection. Little is known about how effective these systems actually are in reducing the risk of drowsiness-related accidents and about what aspects of warning design are crucial in achieving the desired effect on safety. As to the critical driver state, only a reduction in drowsiness can effectively mitigate the safety risk. Therefore the objective of a warning is to convince the driver to take a break during which he should ingest caffeine and / or take a short nap. However, on the level of beliefs, attitudes and motivation, a number of psychological processes counteract this desired behavior. There are two tendencies that represent a particular challenge to warning effectiveness: the optimism bias in the appraisal of personal risk and the tendency to avoid immediate subjective response costs associated with a break. Against this background, the current studies examined the effect of different warning alternatives on cognitive, motivational and behavioral variables. For this purpose, a new experimental paradigm was developed for reproducing, in the driving simulator, the constellation of incentives typical of a real warning situation. A high initial level of drowsiness was induced among participants by partial sleep deprivation and by exploitation of circadian lows in performance. In addition, a constellation of potential gains and losses was created to emulate the most important elements of driver motivation in a real warning situation. Driver state was monitored during simulated drives on a monotonous road. At meeting a defined drowsiness criterion, drivers were given one of several feedback variants. Experiment 1 compared two warning alternatives: a standard warning (coffee cup symbol) and a warning involving an image of the driver derived from the current situation, which was newly developed with focus on high persuasiveness. On the level of behaviorally relevant beliefs, the group of drivers warned by means of their own image scored higher on subjective vulnerability as well as perceived effectiveness of a break. Overall, the incidence of breaks was extremely low however. In Experiment 2, the incentive conditions were exacerbated, and the effects of either warning alternative were compared to a situation without any warning system. It turned out that the standard warning affected subjective vulnerability and break frequency in the expected direction, but only to a very small extent. The warning involving the driver image consistently entailed larger effects on behaviorally relevant beliefs and observable breaks. Moreover, in both experiments, the situational context of the warning was varied by displaying it either in close proximity to the destination or at a larger distance to go. As expected, close proximity to one’s destination had adverse effects on motivation to take a break and observable breaks. The results provide initial evidence as to how the safety effect of drowsiness warning systems can be enhanced by greater consideration of the persuasive and motivational functions in warning design. Alongside the development of detection technology, future empirical research should be directed to a greater extent at the measurable effectiveness of warnings and the mediating psychological processes in order to optimize not only the input, but also the output of drowsiness warning systems.

Level Crossings represent vulnerable points in the European land transport system, since collisions at level crossings account for a high number of fatalities and serious injuries among road users and lead to major disruptions of railway operations. This paper presents part of the ongoing work conducted in the SAFER-LC EU project, namely an overview of indicators for level crossing safety solutions, taking into account the road and rail users’ perspective. The paper identifies key safety criteria concerning the requirements coming from various types of level crossing users. The analysis focuses on human errors, attentional processes and risk perception, while paying special attention to vulnerable users such as pedestrians. The results are discussed in the context of a Human Factors methodological framework which analyses how technological and non-technological safety measures can be better adapted from a user perspective to make level crossings more self-explanatory and ‘forgiving’. The implications for the implementation and evaluation of ‘user-friendly’ safety measures at level-crossings are also discussed.