Keywords

1 Simulators in Railway Driver Training

Using simulators for vehicle operator training is a well-established procedure among transportation providers. They are employed for educational purposes by airplane pilots [1,2,3], train drivers [4, 5] and road vehicle drivers (i.e. trucks, buses and cars) [6, 7]. The air transportation industry in particular requires pilots to go through numerous rounds of simulator training before they start flying actual planes and to keep retraining during their career. Some countries have also introduced mandatory simulator training for railway drivers (e.g. Poland) [8]. The popularity of these schemes stems from two distinct factors. One of them is the high financial and social cost of a potential mistake made while steering a large passenger airship or railway vehicle. The other one concerns the difficulty of recreating potentially useful training settings in the real world. Both pilots and railway drivers need to practice their actions in emergency situations in order to ensure the safety of their passengers and their own.

Several companies have developed simulators for railway purposes in the last few decades, the most notable among them are CORYS (France) and Lander (Spain). Most devices delivered during this period were the so-called full-scale simulators which means a complete recreation of the driver’s cab of the simulated train with one or multiple TV screens or projection mats mounted outside of the cab in the place of the windshield (and sometimes rearview mirrors). In order to achieve additional immersion many customers order these simulators to be mounted on moving platforms with six degrees of freedom enabling them to simulate the jerks and forces related to acceleration, braking and other events that may happen during training.

These solutions, while reliable and realistic, are, however, quite costly, which has limited their proliferation. In order to increase their market appeal producers have developed desktop simulators, which usually consist of a simplified desk panel with some controllers and a TV screen. These devices come at a vastly reduced cost but suffer from significantly reduced realism.

The concept described in this paper of a VR-based railway simulator has been developed primarily in order to support energy efficient driving. The set of techniques used for that purpose is called eco-driving and is a well-researched concept in the context of both rail and road transportation [9,10,11]. Industry experience and scientific papers both show that implementing these energy-efficient driving techniques can significantly reduce the energy consumption of a railway operator. Results that were published point to savings of up to 10% [12, 13]. These savings are achievable through the efficient usage of a time reserve called schedule padding, which is added into timetables to avoid delay propagation between trains [14, 15]. Eco-driving projects vary in implementation and may be simply based on providing drivers with feedback about their energy consumption (this information is actually available only to a limited group of railway drivers at this time). It has been decided that a multi-faceted approach might prove to be a better way to convey information to drivers which is the main reason why the VR-based eco-driving simulator was created. This enables instructors and their trainees to try out differing driving strategies in a realistic environment while obtaining information about the current and total energy consumption. This helps develop and propagate efficient driving techniques leading to potential energy savings.

The use of virtual reality for railway training purposes has already been proposed by several companies and organizations [16,17,18]. Most uses suggested and offered so far were, however, not concerned with the steering and control of the train, but rather revolved around supplementary training for railway workers other than drivers. Trainees using these devices learned how to repair, switch or exchange certain railway elements and how to move safely in railway environments, which differ strongly from the use proposed for the device described in this paper.

2 Virtual Reality

2.1 History

The concept of virtual reality was born in the 1950s when Morton Heilig proposed to simulate the whole environment of the simulation participant in a manner indiscernible from reality. Heilig built such a device in 1962 and named it Sensorama [19]. Due to technological constraints Sensorama was completely mechanical. Within the next decade the first digital VR projects were born, pioneered by Thomas Furness, whose main achievement was the creation of the first flight simulator for airplane pilots, named Visually-Coupled Airborne Systems Simulator (VCASS) [20]. The main problem of these early VR approaches was caused by technological constraints: both the computational and graphical capabilities available during that time were insufficient to create real immersion. This delayed the true onset of VR devices until the 21st century. In the last few years the pace of technological progress has finally reached the stage where realistic immersion became feasible. At first this was achieved through the use of completely surrounding TV screens. This technique was, however, quickly overtaken by VR-capable goggles displaying simulation computed by personal computers (Oculus Rift, HTC Vice) or mobile phones (Samsung Gear). At this time virtual reality devices are quickly gaining in popularity with some sources claiming that their growth is similar to that observed for mobile phones, personal computers and color TVs before reaching market saturation. That would suggest that within the next decade VR devices could be as widespread as these other everyday use devices are right now.

Virtual reality has been particularly popular since its inception in industries, where providing real-life training is very expensive or even impossible. One of the best examples of that is the space industry [21]. Astronauts require extensive training before leaving the Earth. This training should be provided in conditions as close to reality as possible. Although weightlessness on Earth may only be achieved by using special diving airplanes, all the other space flight conditions may be recreated using immersive simulators. A similar reasoning has led to the immense popularity of simulators in military and medical training allowing future and current doctors and soldiers to practice their skills without endangering lives. Additionally, industries using costly and complicated machines (e.g. mining) which are not easily available for training purposes also use simulators in order to train their personnel [21]. In 2017, 63% of surveyed business personnel answered that the main purpose they were planning to use VR in their company was training, which was by far the most popular response among other uses of VR such as product design, data visualization or marketing [22]. This suggests an increasingly attractive future for VR in the professional and educational aspect. At the same time Mixed Reality (MR) and Augmented Reality (AR) devices are also gaining popularity in a manner that might make them serious competitors to standard VR within the next few years [23, 24].

2.2 Reality-Virtuality Continuum

The concept of a reality-virtuality continuum was introduced by Milgram and Kishino [25] in 1994. The idea is based on the premise that all technologies which involve some joining of the real and virtual worlds belong to a continuum between a completely virtual reality where immersion is total and a completely real environment, where there are no virtual elements whatsoever and all the elements exist objectively. All the intermediate solutions fall within the realm of Mixed Reality as shown in Fig. 1. Mixed reality is therefore defined as an environment where real world and virtual world objects are presented together using one method of display. Millgram also proposed an additional partition between Augmented Reality (AR) and Augmented Virtuality (AV). Augmented reality is, within the scope of this framework, information technology and media nested in real-world environments whereas augmented virtuality is the realtime representation of the current state of the real world and its elements in media and information technology environments.

Fig. 1.
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Reality-virtuality continuum [25].

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A somewhat different definition of Augmented Reality was given by Azuma in 1997 [26], who distinguishes it from Virtual Reality through the access to the real world- in VR computer generated content replaces all other experience, whereas in AR it is rather added onto the real-world experience.

In the described version of physically extended virtual reality simulator for train drivers, there are some elements of virtual environment as well as parts from real environment used so the simulator falls between the two edges of the presented continuum.

3 User Experience in Railway Simulators

3.1 Definition of User Experience

User experience is defined in the literature as an evaluation of the qualitative experience that a user has during his interaction with a product [27]. The most up-to-date International Organization for Standardization definition of user experience is concentrated on the perceptions and responses of people using a product, system or service [28]. According to this definition, user experience includes all the emotions, beliefs, preferences, perceptions, physical and psychological responses, behaviors and accomplishments that occur before, during and after the use.

3.2 First Version of the PEVR Simulator

The first pilot version of the VR simulator was designed for Innotrans 2016 - the world’s largest trade fair focused on the rail transport industry. The concept of a physically extended virtual reality railway simulator was based on two main components: hardware and software.

The hardware component consisted of virtual reality goggles, computer and the driver’s desk and chair. In order to increase immersion an additional LeapMotion hands detector was mounted on the table to enable users to see their hands and help them correctly use the train cab devices. One of the most important parts of the simulator is the driver’s desk which had a few real controls added in order to make the haptic part of simulation more realistic (for acceleration, electrodynamical and electropneumatic braking, the deadman’s switch).

The software component consisted of three main parts: simulation logic and physics, 3D environment and driver cab visualization. The first part is needed to provide training scenarios and realistic train behaviour. This is particularly important for experienced train drivers, who control the vehicle based on their perceptions of its movements and know its physical characteristics very well. The 3D environment needs to be realistic enough to provide immersion while still being manageable for the computer to calculate and display. This is particularly important for virtual reality goggles, where much higher rates of frame per second than for regular displays are required to avoid nausea. The last part of software is the virtual driver cab, which needs to correspond well to its physical extensions in order to provide immersion. This has forced the developers to modify the location of the components that are available also in the real world in order to avoid confusion for users. That is caused by the physical limitations of a desktop simulator, which was somewhat smaller that the real driver’s desk.

In order to measure user experience of the described simulator each person taking part in the simulation during Innotrans was asked to fill a survey about their experience and attitude towards virtual reality in the railway context. In 2016 the questionnaire consisted of 4 closed questions and 1 open one assessing: overall user experience, the quality of simulation, similarity to real world and the idea to supplement train driver training with a VR simulator.

3.3 New Version of the PEVR Simulator

In 2016, generally the proposed VR-based simulator solution was very well received. Most visitors accepted it and even those who pointed out certain limitations to its current design and technology usually supported the idea of using it as a training tool for train drivers. Many of them pointed, however, to the main obstacle - lack of enough immersion, which was lowering the user experience. In order to answer this complaint the new version for Innotrans 2018 had significant new physical controllers added. In addition to the already present main train controller and deadman’s switch, a separate pneumatic brake lever as well as sanding, horn, reverser, pantograph and door controls were included. The LeapMotion detector, which was formerly located directly above the trainee’s hands on the driver’s desk has been mounted directly on the goggles increasing the quality of hand detection. The comparison between two versions of the simulator is the main subject of this analysis.

This simulator in its new, enhanced version was presented at the 2018 edition of the InnoTrans railway transportation fairs. User experience was measured using questionnaires – the same questions as in 2016 as well as added question from User Experience Questionnaire [29] - and has been analysed quantitatively, as well as comparatively using the results from the 2016 survey. The results are presented in the following paragraphs.

4 Methods

In order to measure user experience in both versions of the PEVR simulator (in 2016 and 2018) a short questionnaire was used. It started with 4 closed-ended questions:

  • Q1: How would you rate your experience on the PEVR train simulator?

  • Q2: How would you rate the quality of simulation?

  • Q3: How similar is this simulation to the real world in your opinion?

  • Q4: How would you rate the idea to supplement train driver training with PEVR simulation?

The answers to these questions were marked on a symmetrical five-point Likert scale. In 2018 additionally a short version (due to the time constraints of the fairs) of User Experience Questionnaire [29] was performed enabling the authors to compare UX between simulator versions and the UEQ of the new version with similar products. At the end of each questionnaire there was an open-ended question in which people could give more detailed information about their opinions and reactions.

5 Results

Firstly the summary results from both editions were analysed. This is presented in Table 1.

Table 1. Number of people included in both parts of the research (2016 and 2018).
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A significantly lower number of surveys was collected during the second edition (2018). This was caused partially by technical issues with the new version of the simulator which limited the hours of availability for Innotrans participants, and partially by the fact, that only one fully functional device was available in 2018, whereas in 2016 two identical simulators have been presented. Table 2 presents the statistical information regarding the age of survey participants.

Table 2. Age of the participants.
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Within the 93.7% of surveys which contained information about age, the dominant age groups are distinguishable - over 80% of participants were 20–49 years old which is standard in a usual fair public. If the survey was extended for the open public days, they would have probably contained a lot more responses from people under 20.

The geographical dispersion was significantly different for both surveys, probably due to the sample sizes. In the 2016 edition 32 different countries from 6 continents were named by users, whereas in 2018 people from only 18 countries and 3 continents took part in the survey. In both of the cases the dominant nations were Germany and Poland, which is explained by the fairs’ location (Berlin) and the nationality of the company presenting the devices (Poland). Professionally participants were very diverse – although most of them worked in the railway business, they ranged from train drivers, through mid- and high-level managers to CEOs.

Table 3 presents the general descriptive results of the four questions from the closed part of the questionnaire.

Table 3. Descriptive statistics for closed-ended questions
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Generally positive answers may be observed, particularly for questions Q1 and Q4. There were no completely negative answers (i.e. ones) recorded. A larger standard deviation has been observed for questions Q2 and Q3, where the average answer was worse than for the others. Altogether 185 valid surveys were collected.

In order to compare the responses to different versions of the proposed PEVR simulator the responses were analysed for both editions separately as well. Table 4 presents the results of this analysis.

Table 4. Descriptive statistics for closed-ended questions separately for both editions
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As can be observed in Table 4, the mean for the question 1 (concerning overall user experience) is higher for the second edition of the research, while for all of the other questions (concerning quality of simulation, similarity to real world or the idea to supplement training with PEVR simulator) the average obtained score was higher in 2016 in comparison to 2018.

In order to determine whether any other significant differences were found between these two groups a non-parametric U-Mann-Whitney test has been performed. The results are presented in Table 5.

Table 5. Results of U-Mann-Whitney test comparing both editions
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According to the results presented in Table 5, the only significant difference was observed for question Q2, which concerns the quality of simulation. It was judged worse in the newer version, which may have been caused by the technical problems during the first days of the fairs.

In order to determine whether any significant correlations exist between answers obtained in the survey, an analysis was performed on the data, separately for 2016 and 2018. The results of Spearman’s rho correlation tests are presented in Table 6.

Table 6. Results of Spearman’s rho test for both editions
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Almost all of the questions in the survey were observed to be positively correlated to each other, with especially high correlation between Q2 and Q3 in both of the editions of the research. The correlations can be interpreted in the following manner:

  • Correlation between Q1 and Q2: the higher people rate the quality of simulation, the better their experience on the simulator is

  • Correlation between Q1 and Q3: the more similar the simulation is to the real world, the higher people rate their experience on the simulator

  • Correlation between Q1 and Q4: the higher people rate their experience on the simulator, the more likely they are to support the idea to supplement train driver training with VR simulation

  • Correlation between Q2 and Q3: the more similar the simulation is to the real world, the higher people rate the quality of simulation

  • Correlation between Q2 and Q4 (only in 1st edition): the higher people rate the quality of simulation, the more likely they are to support the idea to supplement train driver training with VR simulation

  • Correlation between Q3 and Q4 (only in 2nd edition): the more similar the experience is to the real world, the more likely people are to support the idea to supplement train driver training with VR simulation

The lack of correlation observed for questions 3 and 4 in the research conducted in 2016 and for questions 2 and 4 in 2018 means that even when the quality of the simulation is not perfect and the experience is quite different from real world, people still think that it can be useful in train driver training. Meanwhile the particularly strong correlation between questions 2 and 3 can be explained by the fact that most people judge the quality of a simulation by comparing it to the real world.

Table 7 presents the results of the analysis of the correlation between the survey participants’ age and their answers.

Table 7. Correlation between the age and answers given to closed-ended questions
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A significant correlation may only be observed for question 2 - the older the participant, the more positive his or her view of the simulation quality. This may be partially explained by the relative lack of experience with computer simulation technology of the older participants compared to their younger counterparts.

Table 8 presents the descriptive results of the second part of the survey (User Experience Questionnarie), which was performed in 2018 only. In order to compare the results to other research papers they have been scaled to the range of -3 (horribly bad) to +3 (extremely good).

Table 8. Results of User Experience Questionnaire
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Items belonging to the same scale (pragmatic or hedonic quality) should show a high correlation. The consistence of a scale is measured by the Cronbach Alpha Coefficient which equals 0.75 for pragmatic quality and 0.76 for hedonic quality and that means both scales are consistent.

The results from the UEQ questionnaire have also been compared to other systems evaluated using this method. The total sample in this comparison consisted of 14056 people across 280 studies concerning different products, including business software, web pages, web shops and social networks. Figure 2 shows the results of this comparison. Both the pragmatic and hedonic qualities were judged as excellent by the participants.

Fig. 2.
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Scores of UEQ.

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In order to obtain more qualitative feedback on the proposed PEVR simulator solution an open-ended question was added to the questionnaire in both its editions. In 2016, 32 people out of 144 decided to leave supplementary comments. The negative ones concerned mainly the resolution of the Oculus goggles, which may be problematic when simulating some of the conditions that train drivers meet. Train drivers complained about the lack of some of the real controls, which was one of the main reasons for their addition in the 2018 version. Positive feedback was usually an enthusiastic addition the evaluation in the close-ended part of the survey. In 2018, 13 people out of 47 decided to add some comments. They were once again related to the resolution, but also to the LeapMotion solution for hand detection, which was not working perfectly at the fairs. For example on of the PEVR simulator users observed that “Tracking sensors for hand tracking could be better”, whereas another suggested “maybe higher resolution”. The positive feedback was related to the quality of the immersion and the whole experience and included opinions like “I had a feeling that the chair was moving!”, “Need one at home!” or “I love it, thank you for the experience!”.

6 Limitations and Future Research

The main limitations of this research are related to its specific location and target group. Firstly, the number of participants in the second edition was considerably smaller than in the first one making it harder to compare the results. Secondly, the participants of the survey represented a wide array of professionals in the railway industry. It might be beneficial to perform a similar survey on train drivers only, with the possible inclusion of people related to railway safety and training. An additional interesting path of research could concern the comparison of training using a full-scale simulator with motion simulation with the use of PEVR solutions like the one described in this article.

7 Conclusions

The surveys from 2016 and 2018 analysed in this paper show that the new concept of a Physically Extended Virtual Reality railway simulator is a very attractive solution that could potentially be used for train driver training. The feedback during the 2016 and 2018 editions of Innotrans railway fairs has been positive and the UEQ questionnaire from the second edition has shown clearly that the PEVR perception by the general railway business public is excellent. According to the survey participants, amongst the things needing improvement the foremost are the goggles’ resolution and hand detection software, as these two factors affect immersion very strongly. Interestingly, the second, improved version on the PEVR simulator was actually evaluated as worse than its predecessor. This might have been caused by the fact, that the significant increase in the number of simulator physical controllers has made the task of driving the train more difficult. That would be in line with the comments made by the train drivers using the first version, who insisted on adding more physical controllers in order to make the PEVR simulator more realistic. In the near future PEVR simulators may become very widespread in railway training processes due to their comparatively low price and high immersion levels.