Road safety research has commonly focused on a specific set of risk factors, aiming to achieve a better understanding of the factors influencing risky situations, to develop a set of guidelines and policy recommendations that could help mitigate such cases. While existing studies cover different modes of transport, most focus on passenger cars, with fewer studies (in comparison) dedicated to professional driving modes (such as trucks, trams, etc.). In recent years, advances in technology have enabled the spread of advanced driving assistance systems (ADAS), to help drivers mitigate unsafe driving boundaries. Accordingly, it has also become crucial to better understand drivers‘ perceptions of such systems, to help improving their design and operation. One opportunity to test ADAS would be to install them in driving simulator environments, as the latter provide relatively safe boundaries within which they can be tested, thereby allowing the assessment of drivers’ perceptions of them. While this was done in previous research, it was mostly limited to passenger cars [1
], except in Schindler and Piccinini [5
], who highlighted the importance of ADAS in preventing vulnerable road user (VRU) collisions for trucks. A study by Jung et al. [6
] also highlighted the perceived importance of ADAS for tram drivers; this was, in particular, the case for navigation-based speed profile generator, driver fatigue warning, and emergency brake assist. Still, very limited research exists overall for truck and tram driving simulators. While certain risk factors may be a particular issue for certain transport modes, such as sleepiness and fatigue being an issue in shift-working truck drivers [7
], many other factors are common across modes, such as vulnerable road user interactions or collisions, forward collisions, etc. A gap in research exists in investigating the similarities and differences across modes, which might help transferring findings where applicable, but also focusing research where needed. This paper aims to fill this gap by assessing drivers’ acceptance to a warning–monitoring system, developed within the context of an EU naturalistic driving study project (i–DREAMS), and common across different modes, namely cars, trucks, and trams. The assessment will be done using questionnaire data from a multi-modal driving simulator study. In particular, the aim of this paper is to assess the findings in order to highlight the similarities and differences between professional drivers (trucks and trams) and passenger car drivers, to help advising future similar studies. The objectives of this work would be, therefore, to: (i
) identify the factors affecting the system’s acceptance for trucks and trams; (ii
) compare those with the factors of interest for passenger cars; and (iii
) develop a multi-modal ADAS acceptance model, highlighting the common findings across the modes, as opposed to the mode-specific ones.
In the rest of the manuscript, the methods used are presented under Section 2
, including the study design, the study protocol for data collection, and the data analysis and model development tools. After that, Section 3
presents the data collected, including the sample characteristics, and an initial exploratory data analysis, including a descriptive analysis based on the questionnaire data, but also a qualitative analysis, resulting from the open-ended questions within those questionnaires. In Section 4
, the developed models are given, including the exploratory factor analyses results, but also the models testing the different hypotheses within the technology acceptance models. Finally, Section 5
discusses the main findings for the truck and tram studies, compares them with the previous findings of the car driving simulator study, and presents the validated multi-modal technology acceptance model based on the different simulator experiments. The section also answers the research questions initially developed, highlights the study limitations, but also draws insights for future research.
3. Data Collection and Exploratory Data Analysis
3.1. Sample Characteristics
As elaborated in Section 2
, data assessed in this paper has been collected in a multi-modal context, including car, truck, and tram simulator experiments. An overview of the collected sample characteristics is given in Table 2
. The table provides a summary for characteristics including gender, age, employment status, weekly kilometers driven (relevant for truck drivers), fines history, accidents history, number of working years, and number of years since drivers’ license (for professional drivers, such as truck and tram drivers, this refers to the number of years since they acquired their licenses for the specific professional mode under investigation) was acquired.
Remark 1. While the statistics reported in Table 2 intend to be for the entire sample, for some variables, there were some missing values (usually 1 or 2 at most); therefore, values are generally provided in absolute values, but also in percentages. Moreover, the interquartile range is provided for variables whose answer options were not discrete, but rather continuous, such as age, number of years worked, or number of years since acquiring the license. Remark 2.
Both “Fine” and “Accident” variables refer to the last three years of using the truck or the car.
Weekly kilometers is an estimate of the mileage using the truck.
The variables “fines”, “accidents”, “working years”, and “license years” refer to the main mode investigated. For instance, even if truck drivers also drive cars, the reported numbers refer to accidents or fines or working years as a truck driver; the same applies for tram drivers.
For each of the modes, different surveys were used; therefore, some statistics were not available for all modes. For instance, for car drivers, the weekly kilometers driven were not requested, as they are not professional drivers, and it was not as relevant for those participants; the same applies for working years for cars. Similarly, fines were not relevant for tram drivers, for instance.
An overview of the demographics reveals findings on the different samples. While gender seems to be balanced for car drivers, this was not the case for truck and tram drivers, who tend to be mostly (exclusively) males; however, this makes sense as it reflects the population of professional drivers, who usually are male drivers. Similarly to car drivers, most fines for truck drivers pertained to overspeeding, the same way accidents resulted in material damage only. Still, it is interesting to note that, on average, the percentage of truck drivers having had at least one fine is double that for car drivers, the same way the percentage having had an accident is way higher for truck drivers.
In addition to the demographics and variables reported in Table 2
, additional questions were asked on the roadway environments, but also sleep quality, for each of truck and tram drivers. Results indicated that truck drivers mostly drove on motorways (a distribution of, on average, 42%), followed by rural (on average 36%), then urban roads (on average 27%). Their working time was mostly during the day (53%), followed by a combination of both daytime and night-time (44%). On the other hand, tram drivers worked an average of 28 h per week, mostly (71%) in a combination of day and night shifts. This mostly indicates that tram drivers drove, on average, more mixed day and night shifts.
Regarding sleep patterns, on average neither truck or tram drivers reported any sleep issues; no sleep disorders were reported (except one tram driver who had sleep apnoea). Both truck and tram drivers indicated that their sleep quality was mostly good or very good (about 64% of drivers), while only 21% of truck drivers revealed that their sleep quality was not so good, as opposed to 18% for tram drivers. The majority of truck drivers (61%), only very occasionally (less than 2 to 4 times per month in the last year) had to fight sleep to stay awake, as opposed to only 12% of them indicating that they never had to do so in the past year. For tram drivers, the last two figures were 57% and 36%, respectively. Most truck drivers (52%) never had to stop driving due to drowsiness in the past year, and about 21% of them had to do so more than three times that year. The percentages were similar for drivers who wanted to stop driving due to drowsiness, but were not able to do so at that time; on the other hand, no tram driver indicated that they had to stop because of feeling sleepy. Only one person indicated that they wanted to stop the tram but were unable to (3.6 %).
The last figures indicate that sleepiness was potentially more of an issue with truck drivers, which could potentially be explained by the longer distances travelled. On the other hand, it could be argued that truck drivers have more opportunity to stop than tram drivers who are confined to their cab. Finally, very few truck drivers indicated that in the previous year they fell asleep while driving (only one driver), as opposed to no tram driver; also, only one driver indicated that they had a sleep-related incident in the previous year (incident due to falling asleep while driving), as opposed to only two tram drivers (in the past 10 years).
3.2. Exploratory Data Analysis
3.2.1. Descriptive Analysis
This sub-section provides an overview of respondents’ exposure and overall attitudes towards ADAS [mostly for car and truck participants, as both modes have very similar ADAS; on the other hand, tram ADAS are a bit more different, and even the ones having the same functionalities have different names, therefore the comparison of ADAS perceptions and exposure for the rail mode (with other modes) is less feasible.], but also their attitudes and perceptions towards the i–DREAMS system.
3.2.2. Qualitative Analysis
Besides attitudinal questions, the survey included open-ended questions, whose answers have been analyzed qualitatively. Truck drivers generally found the system to be clear, simple, easy to understand, useful (bringing awareness), realistic, and quite timely (warnings on time). The visuals and auditory systems were well perceived. However, there seemed to be a confusion with regards to the numbers on the pictograms. A suggestion was to replace the time in seconds with distance in meters. Further comments included the integration of the system into the existing dashboard devices, and the improvement in screen resolution (and size of display screen), and in road signs recognition (for it to be faster). Moreover, while the auditory system was generally found to be good, there seemed to be a lack of consensus on whether it was loud enough or not, some finding it possibly distracting. An overall suggestion was to possibly reward participants based on their “good” behavior. Participants also praised the “coffee” sign, which they seemed to understand as a warning to stop for a few minutes, to avoid fatigue. Yet, some participants were skeptical about it, stating they would prefer to rely on themselves to know when they are tired or not.
An analysis of the open-ended questions for tram participants highlighted the driving challenges tram drivers often face: drivers indicated mostly that driving can be more demanding during rush hours, due to the presence of additional road users, including pedestrians, school children, scooters, delivery riders, bikes, or other vehicles. Additionally, bad weather conditions were indicated as a factor making driving more demanding, such as having wet, or frosty (and therefore slippery) roads. Finally, fatigue was mentioned, mostly when driving long continuous hours (consistent environments without much change, leading to repetition), or due to very early or very late shifts. Tram assistance systems (Drivers Safety Device, Correct Side Door Enabling, Emergency Stop Button, and Emergency Pantograph Down button) were found to be useful, reliable, important and essential, although the latter was less used. For the overspeeding aid, it was found to be necessary and positive, with a few saying that it is distracting. Finally, the Guardian (a system to detect fatigue and distraction) received some skepticism; while many described it as useful, some found it distracting and unreliable. Additional desired safety systems included warnings for: upcoming signals or bends, speed limits and over-speeding, proximity to pedestrians or other vehicles (collisions), obstacles or object detection in swept path. Moreover, tram drivers indicated their wish for louder warnings, but also for improvements for the current “Guardian” system.
5. Discussion and Conclusions
The investigated ADAS (the i–DREAMS system) seemed to be well perceived and accepted across the different modes. It was generally found to be clear, with a higher perceived visual (as compared to auditory) clarity; while the warning visuals were found to be clear, there was a slightly lower consensus with regards to the sound clarity, wherein some respondents found it to be too loud, or not loud enough. This was noticed in the different experiments (in all modes), meaning there was room for improvement for that particular feature. For truck drivers, a previous analysis of the simulator data [16
] already confirms the assessed acceptance (based on the questionnaire analysis); in fact, speed warnings within the truck simulator experiments were found to reduce speed behavior by giving drivers feedback about the enforced speed limit, which in some cases was different to the limits seen on the roads (that were only applicable to car drivers).
Furthermore, perceived ease of use and perceived usefulness of the system were identified as the main factors resulting from the factor analyses applied to the attitudinal statements that were part of the different questionnaires. Findings on the system’s perception for trucks and trams were therefore quite comparable with those of the car experiments. Overall, it means that a higher acceptance of a warning–monitoring system can be reached by focusing on highlighting the usefulness of the product (might come with awareness, or might depend as well on previous experiences, etc.) and its ease of use (this could be actually improved based on the product development). In this case for instance, it can be achieved by improving the sound system, which seemed to be less clear for some of the participants.
However, tram drivers seemed to have specific concerns, such as driving challenges with regards to VRU interactions. Tram drivers expressed their interest in having additional safety systems including warnings for upcoming signals or bends, speed limits and over-speeding, proximity to pedestrians or other vehicles (collisions), obstacles or object detection in swept path. This aligns with findings from a previous study, in which route familiarity appeared to be an important factor influencing driving stress for tram drivers [17
]. As VRU warnings already exist in other modes, this finding might be transferable across modes, from cars to trams for instance. While trucks and tram driver groups did not have major sleep problems, both indicated that they would appreciate a system telling them when they are tired. For trucks, this was relevant also to be able to make stops if there is a high level of fatigue. Tram drivers did not seem to particularly like the existing “Guardian” system; therefore, further improvement on a fatigue safety assistance would be needed there. In particular, tram drivers reported challenges faced driving early and late hours. Fatigue and sleepiness were therefore concerns shared by professional drivers. In other words, although the tram drives faced similar road safety challenges to cars and trucks as they share the road (e.g., pedestrians, traffic etc), rail also has a different operation than road driving, and therefore is it unsurprising that tram drivers suggested additional warnings more suited to their transport mode.
To summarize, some risk factors have transferable findings across modes, as they are of interest in different contexts, such as forward collision warning (FCW) and VRU collision warning; in the case of our study, this was observable between cars and trucks for FCW, and between cars and trams for VRU collision warning. On the other hand, some other warnings such as fatigue and sleepiness were rather common between trucks and trams, and not very common for car drivers, at least not typically. There is a potential, therefore, to use the proposed system to monitor the sleepiness and fatigue levels of truck and tram drivers, using minimally invasive techniques such as heart rate and heart rate variability.
However, despite seemingly closer findings between trams and trucks, with respect to fatigue mostly, the tested hypotheses within the technology acceptance model (TAM) for the different modes showed rather similar findings between cars and trucks, than for trucks and trams. For the former, most relations were validated, namely the relation between the intention to use, as a function of the perceived ease of use and the perceived usefulness, which was not validated for tram drivers. Similarly, the relation between perceived usefulness and external variables was proven for trucks and cars, but not trams. Only the relation between perceived ease of use and external variables was confirmed for all modes. The technology acceptance model was therefore mostly validated for truck drivers (as was for car drivers), but not for tram drivers. This indicates that despite some transferable findings, the relation may not be as similar for rail, which should be perhaps assessed separately.
A summary of the findings on the validated multi-modal technology acceptance model is given in Figure 8
; in this representation, an indication of the modes where specific links were validated is given where possible.
This paper contributed to the body of research by answering the different research questions formulated in Section 1
, namely: identifying the factors impacting the acceptance of warning–monitoring systems in different modes, comparing those with the car mode, extending the technology acceptance model for the multi-modal context, and highlighting the transferable findings. However, this work does not come without limitations. This included the well-known simulator sickness challenges during data collection, but also the fact that there was not enough time to investigate sleepiness within the limited simulator experimental timeline, and generally that the duration of exposure to the system was limited. Moreover, the analysis also relied on self-reported data, which could at times have had biases. Finally, despite challenges in data collection and recruitment within driving simulator experiments (experiments are time- and effort-consuming and participants are particularly challenging to recruit, especially truck and tram drivers), the sample size remains limited.
Still, the findings highlighted by this research point out to the commonalities, but also differences across modes, with the key takeaways of the common attributes between car and truck modes (in the acceptance model), between truck and tram (in fatigue and sleepiness), but also the unique features that tram drivers have, which makes sense as it is the only rail transport investigated here. Transport modes can indeed learn from each other, and this research has shown that it can be relevant to investigate the acceptance of a specific technology in a professional mode, to make use of the learning obtained in another mode, laying the founding for future work modal transferability which would possibly help better scope multi-modal studies and also allow more efficient research where resources are limited.