Testing a digital level crossing warning system for road traffic users with a naturalistic driving study

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

Evaluation of new human-centred low-cost measures. Deliverable D2.4 of the SAFER-LC project

This deliverable describes the methods applied and the results achieved during the second phase of Task 2.3 in the SAFER-LC project: the evaluation 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 – aimed 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) was 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 aimed to design 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. This objective was driven by the insights of the major role that road user behavior plays in accidents at level crossings and the need for safety measures to be affordable to enable their application to a large number of crossings and the achievement of tangible safety effects. The activities in the design of countermeasures were performed in the first phase of task 2.3 from May 2017 to October 2018. They resulted in a list of 89 reviewed LC safety measures, of which 36 measures were for use at passive LCs, 29 for LCs with barriers, and 24 for use at all kinds of LCs. For the purpose of evaluation, Task 2.3 referred to two main inputs from other tasks within SAFER-LC: the human factors methodological framework developed in Task 2.2 and the pilot tests of innovative LC safety measures performed in Work Package 4 (WP4). The human factors methodological framework was developed to define what aspects of human behavior should be considered when trying to assess the suitability of a LC safety measure. It also defined important context variables that influence this suitability, including environmental factors such as LC type, layout, weather, traffic etc. as well as the issue of acceptance by different stakeholders. The methodological framework is based on sociotechnical systems theory, relevant models of human cognition and behavior, and analytical tools and empirical approaches from related research projects. Its development resulted in the definition of three sets of criteria important to the human factors assessment of a given LC safety measure. To facilitate and structure the application of the framework, a human factors assessment tool (HFAT) was developed. Its core is a survey comprising checklists and forms to assess the three sets of criteria defined. The tool helps to collect and systemize relevant information on a given LC safety measure in order to enable a reasoned estimation of its effects in road user behavior, user experience and social perception. The pilot tests in WP4 involved two kinds of tests. One kind focused on demonstrating the feasibility of technical solutions to improve LC safety. The other one was concerned with the effects of LC safety measures on road user behavior. This included two simulator studies of infrastructural safety measures, an online survey based on videos of a train-mounted countermeasure in a real rail environment, a field test of an in-vehicle LC proximity warning, and a field test of two infrastructural measures. Based on the results of these tests, the pilot site leaders used the HFAT to assess the piloted measures from a human-factors perspective. Using the HFAT enabled the presentation of the results in a common format, although the input studies used different methods and measured different indicators. The LC safety measures evaluated in this way were: blinking lights for the locomotive front, coloured road markings on approach to the LC, in-vehicle proximity warning, rings upstream of the LC, traffic light, blinking amber light with train symbol, funnel effect pylons, message , “” written on road, peripheral blinking lights, rumble strips, sign “”, and speed bump and flashing posts. The four measures assessed to most facilitate safe road user behavior in the HFAT evaluation were the blinking lights for the locomotive front, the two in-vehicle proximity warnings, and the peripheral blinking lights. Minding the evidence collected in the HFAT, this assessment is rather certain for the two measures involving blinking lights, and more tentative for the in-vehicle proximity warnings. Stakeholder acceptance and user trust are expected to be sufficient to allow for successful implementation of these measures, minding the principles of stakeholder participation and user-friendly design. Two measures scored particularly low on the assessment of behavioral safety effects: the funnel effect pylons and the message “” on the road. Both assessments are tentative, as the findings from the pilot are the only evidence available by now. Due to the low expected efficacy, acceptance and trust values were not considered in these cases. The seven remaining measures were attested a medium effectivity on the facilitation of safe behavior. These assessments are more certain for the rumble strips and the sign “”, and remain tentative for the coloured road markings on approach to LC, the rings upstream of the LC, the traffic light, the blinking amber light with a train symbol, and the speed bumps and flashing posts due to the limited availability of evidence. Based on the acceptance and trust values obtained with the HFAT, successful implementation appears possible for most of these measures. Some difficulty in implementation is expected based on the acceptance assessment for the coloured road markings on approach to LC, the rings upstream of the LC, and the funnel effect pylons. Beyond its use as a tool to guide and evaluate empirical research on LC safety, the HFAT can also be used by road and railway transport stakeholders as a checklist to support the consideration of human factors aspects in the evaluation of LC safety measures. Using the HFAT in this function can help to assess the suitability of a LC safety measure to different railway environments and user requirements and to avoid efficacy barriers, by considering the important issues of acceptance and social perception of road users and other stakeholders. The results obtained in SAFER-LC Task 2.3, the design and evaluation of human-centered low-cost measures to improve LC safety, will be used as one main input in the implementation of the SAFER-LC toolbox, a web-based tool for anyone concerned with LC safety. The toolbox is conceived to be a guide to best practice that integrates all the recommendations, promising interventions, and specifications developed during the project with the empirical evidence collected from the scientific literature and the pilot tests. The toolbox will be accessible free of charge at the end of the project and will continue to be maintained, updated and improved by the International Union of Railways (UIC) for the benefit of the road- and railway-safety community

Human factor methodological framework. Deliverable D2.5 of the SAFER-LC Project

This deliverable presents the revised version of the Human Factors (HF) methodological framework which has been developed in the SAFER-LC project as part of Work Package 2 (WP2). The objective of Task 2.2 of WP2 is to develop a Human Factors methodological framework 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. The overall objectives and structure of this deliverable is described in Chapter 1. Chapter 2 reviews and summarises the most important theoretical aspects of the Human Factors methodological framework in the LC context. The framework was developed in line with the principles self-explaining and forgiving infrastructure and by considering LCs as socio-technical systems, where individual road users and the technical infrastructure interact. Models on human information processing and human behaviour in terms of errors and violations at LCs have also been considered. These theoretical aspects represent the theoretical backbone of the HFAT, and were presented in detail in deliverable D2.2 (Havârneanu et al., 2018). Further, Chapter 3 shows how the HFAT was applied in the SAFER-LC pilot tests and presents the feedback received from the pilot test leaders. The two-step evaluation of the HFAT by the pilot test leaders was a useful and productive exercise. It allowed collecting valuable inputs, suggestions and ideas on how to improve specific parts of the tool. While most of the evaluation feedback was taken into account during the HFAT revision process, not all received suggestions could be implemented within the SAFER-LC timeframe and resources. Other suggestions were subject to group discussion during the project meetings and were implemented only partially, following the collective decision. Chapter 4 explains the differences between the first version of the tool and the revised version. Based on the received feedback, changes concerned only the classification criteria (orange form) and the criteria to assess the behavioural safety effects (green forms). Major changes involved the revision of effect mechanism list in the classification criteria table and the regrouping of areas of psychological function in assessment of behavioural safety effects. Chapter 5 provides an overall discussion of the HFAT, its strengths and limitations, its current utility as a stand-alone methodology, and possible directions in its further development. For example, the HFAT could be used in the future as a checklist to support the consideration of human factors perspective in the evaluation of LC safety measures. The HFAT will also be included in the SAFERLC toolbox, accessible through a user-friendly interface

Results of the evaluation of pilot tests. Deliverable D4.4 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, 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