Frustration at congested railway level crossings: How long before extended closures result in risky behaviours?

Applied Ergonomics 82 (2020) 102943

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Frustration at congested railway level crossings: How long before extended closures result in risky behaviours?

T

Grégoire S. Laruea,b,∗, Ross A. Blackmana, James Freemana a b

Queensland University of Technology (QUT), Centre for Accident Research and Road Safety-Queensland, Brisbane, Australia Australasian Centre for Rail Innovation (ACRI), Canberra, Australia

ARTICLE INFO

ABSTRACT

Keywords: Road safety Rail safety Railway level crossing Congestion Risky behaviour Frustration

Drivers’ non-compliance with rules is a prominent factor in collisions with trains at railway level crossings. Road user impatience and frustration has been identified as an underlying factor in non-compliance and can be characterised as a specific risk factor. However, research on non-compliance related to waiting times and driver inconvenience lacks in the literature. This paper, therefore, seeks to enhance the currently limited understanding of the relationship between waiting times and risky driver behaviour. An Advanced Driving Simulator was used to obtain objective measures of level crossing non-compliance. Subjective measures on driver frustration and decision-making processes were also collected. Sixty participants completed six driving tasks each, with the tasks varying in terms of traffic conditions, number of trains and associated waiting times. This study shows that increased waiting times result in higher levels of frustration and an increased likelihood of risky driving behaviour, particularly for waiting times longer than 3 min. Non-compliance included entering the activated crossing before boom gates are down, entering the crossing after the train passage but before signals are deactivated, stopping/reversing on the crossing. Subjective data revealed that participants did not comply with level crossing rules due to factors including time pressure, impatience/frustration and low perceived risk. The results suggest that, where possible, waiting times should be standardised at values lower than 3 min to reduce the likelihood of risky road user behaviour.

1. Introduction Previous research has shown road user behaviour to be a prominent factor in risk of collisions between trains and road users at railway level crossings (Larue et al., 2018a, 2018b; McCollister and Pflaum, 2007; Tey et al., 2011). These collisions are commonly associated with road users’ non-compliance with railway level crossing rules, whether they are equipped with active controls (flashing lights with or without boom gates, activated by the approach of a train) or passive ones (stop sign or give way sign). Current increases in road and rail traffic lead to more frequent and more prolonged closures of level crossings (Larue and Naweed, 2018). These extensive closures are a consequence of the technology used to activate level crossings: the closure is often activated based on the worstcase scenario – that is, for the fastest possible train approaching – to ensure safety, independently of the actual train speed and hence independently of its arrival time. In this context, the required minimum warning times (time between the activation of the level crossing and the presence of the train at the crossing) are provided to road users but are often exceeded by up to a minute (Larue and Naweed, 2018; Raslear, 2015). Combined with the

∗

need for closing crossings for the passage of multiple trains, this results in congestion and long waiting times for road users, waiting times being defined as the time road users have to wait before being able to proceed through the crossing. Waiting at congested level crossings can regularly last longer than 10 min (Larue and Naweed, 2018). The literature shows that errors of perception, errors of judgement and deliberate attempts to circumvent safety-oriented countermeasures, including regulatory and engineering measures, are factors influencing non-compliance (Abraham et al., 1998; Freeman and Rakotonirainy, 2015; Mulvihill et al., 2016). Within the category of deliberate circumvention, road user frustration and reluctance to wait has been identified as prevalent underlying factors at congested level crossings (Larue et al., 2018b; Raslear, 2015), and can thus be characterised as a specific risk factor at congested level crossings. Opportunities to circumvent level crossing rules are seen to vary according to the specific crossing conditions. As such, research has examined driver and pedestrian responses and compliance in some of these conditions, including the common measures designed to prevent rail and road user collisions. Such research has compared driver responses to passive controls (Larue et al., 2018a; Larue et al., 2019b;

Corresponding author. Queensland University of Technology (QUT), Centre for Accident Research and Road Safety-Queensland, Brisbane, Australia. E-mail address: [email protected] (G.S. Larue).

https://doi.org/10.1016/j.apergo.2019.102943 Received 8 October 2018; Received in revised form 19 August 2019; Accepted 22 August 2019 0003-6870/ © 2019 Elsevier Ltd. All rights reserved.

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Larue and Wullems, 2017; Lenné et al., 2011; Liu et al., 2015; Tey et al., 2011) as well as to different types of active controls (Larue et al., 2018b; Rudin-Brown et al., 2012), with combinations of boom gates and active signals generally found most effective (Raub, 2009). To a lesser extent, research has also addressed issues around road traffic volumes and congestion regarding driver behaviour and railway crossing safety (Hao and Daniel, 2014; Larue and Naweed, 2018; Oh et al., 2006). The finding that drivers are more likely to be fatally injured at level crossings during peak periods (Hao and Daniel, 2014) suggests that road traffic congestion creates a particularly hazardous railway crossing environment, but the specific mechanisms involved have received relatively little research attention. A range of level crossing research has been conducted with driving simulators, of varying levels of fidelity: medium-fidelity simulators (Tey et al., 2013), and advanced simulators (Larue et al., 2015; Lenné et al., 2011). Such simulators are valid for evaluating various driving behaviours in a range of driving conditions (Mullen et al., 2011). In particular, they are valid for passively protected level crossings (Larue et al., 2018), suggesting that they are an effective tool for evaluating driver behaviour at level crossings. While issues around road user frustration and non-compliance at level crossings have been identified, the literature is lacking substantially in research on deliberate non-compliance related to long waiting times and long warning times, although long waiting times at level crossings have been hypothesised as a factor in driver non-compliance (Yeh et al., 2013). Reluctance to wait is associated with risky pedestrian behaviour at railway level crossings (Freeman and Rakotonirainy, 2015), but this finding is of limited relevance to the behaviour of motorists. However, there is evidence that excessively long warning times at railway level crossings compromises warning credibility among vehicle drivers (Berg et al., 1982; cited in Kadiyala et al., 2016; Raslear, 2015; Richards and Heathington, 1990). Long warning times, and by extension waiting times, may, therefore, encourage some drivers not to comply at railway crossings by attempting to traverse a crossing before an approaching train has passed, including by driving through active flashing signals and around closed gate arms (Jenness et al., 2006; Kadiyala et al., 2016; Larue et al., 2018b). Drivers’ decisions at active level crossings have been modelled with the Signal Detection Theory (SDT) (Raslear, 1996, 2015; Yeh et al., 2016). SDT is a psychophysical method that both measures the reaction tendency of subjects, as well as the identification ability of subjects (Green and Swets, 1966), and is used when subjective inclinations are expected to impact on decisions. This approach models the ability of the driver to detect a signal (approach of a train), called sensitivity, and the decision process, called bias (representing the tendency to stop or proceed). Attitudinal or motivational factors may influence driver decisions to enter a crossing (bias). Given that there is evidence that excessively long warning times at railway level crossings compromise warning credibility among vehicle drivers (Berg et al., 1982; cited in Kadiyala et al., 2016; Richards and Heathington, 1990), Raslear (2015) suggests further investigations are required under controlled conditions to understand this bias better, as variations of waiting and warning times are currently limited in the literature. Importantly, a change in bias is not always linked to a similar change in sensitivity. For instance, a large reduction of risk at level crossings was found through a bias reduction, which occurred with a small change in sensitivity (Yeh et al., 2009). Given the variability of waiting times at level crossings, the effects of the credibility of the warning for drivers’ decision processes, and the likely associated delays in drivers perceiving a change in the crossing closure conditions (due to uncertainty), non-compliance is only likely when conditions change beyond the interval of uncertainty used by drivers in their decision process, suggesting that crossing compliance may follow a hysteresis behaviour (i.e. rates would be different whether the situation is improving or degrading at the crossing). This concept is largely used in

physics (Bertotti, 2014), but has also been found relevant in economics contexts when modelling consumer decisions (Evgeny et al., 2014), as well as in human perception (Hock et al., 2003). Therefore, changes in conditions at level crossings should be considered, both in terms of improvements and deteriorations in waiting times. The current paper seeks to address an identified gap in research regarding the relationship between long waiting times at level crossings and risky driver behaviour. A clearer understanding of this relationship is needed to enhance the depth and reliability of evidence on which to base refined measures for improving level crossing safety. This study, therefore, examined whether drivers’ behaviour at level crossings became riskier with longer waiting times. More specifically, this driving simulator study aimed to determine the maximum time spent at a crossing beyond which deliberate non-compliance became more likely by simulating a level crossing with increasing road and rail traffic. The study also investigated whether decreasing waiting times (in reduced congestion) at a level crossing resulted in immediate return to baseline behaviour. 2. Methods 2.1. Experimental design This driving simulation study used a repeated measures design with all participants completing six driving tasks. Each driving task consisted of reaching the same destination within a given time and traversing an active level crossing once. Each task differed only in terms of two primary factors: level crossing warning and waiting times. In the current paper, we defined ‘waiting time’ as the time needed to traverse the crossing, from when first stopped before the crossing to when the crossing has been passed completely. On the other hand, ‘warning time’ is defined as the time taken by the train to arrive at the crossing from the activation of the flashing lights at the crossing. Waiting time includes a proportion of the warning time, the amount of which depends on how soon a driver stops after activation of the crossing signals. Warning time was a between-group factor. This approach was used to ensure that participants became familiar with the particular condition of the level crossing they were traversing (in terms of warning times), and participants became experienced with the level crossing, as a regular user would. A within-subject approach was used for waiting time, with participants experiencing a range of waiting times during six repetitions of an itinerary requiring level crossing traversal. The range of waiting times used was based on identified control durations and frequently observed extended closure durations at actual Australian railway crossings (Larue et al., 2019a; Larue and Naweed, 2018). Given that drivers' detection of changes in closure conditions may be delayed - due to inherent random variations of closure durations (e.g. trains arriving at different speeds) – drivers’ perceptions and bias toward stopping at or proceeding through the crossing are likely to depend on whether the situation is improving or worsening, suggesting a possible hysteresis behaviour. Therefore, participants were further randomly and equally divided into three groups varying in the sequence of waiting times experienced:

• Control: this group experienced only short waiting times (less than

• 2

3 min). It simulated a situation where a level crossing operates as intended, with warning times longer than the minimum required by rail standards to ensure safety (Standards Australia, 2015), and level crossing closure durations representing the average closure of the level crossing when one to two trains efficiently proceed through the crossing following their timetable (Larue et al., 2018b). This value represents a realistic, achievable waiting time under normal conditions; Increasing waiting times: this group experienced a worsening situation at the level crossing leading to the conditions that are currently experienced at level crossings with high levels of congestion and road user non-compliance; and

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Table 1 The six different scenarios. Waiting time (min)

Warning time (s)

Number of trains (size, speed)

Number of crossing closure cycles

Train stopped at the station

Road congestion

1.5

24 50 75 24 50 75 24 50 75 24 50 75 24 50 75 24

1 1 1 2

2 3 5 8 10

50 75

(long, 30 km/h) (long, 50 km/h) (short, 70 km/h) (short, 50 km/h)

1

No

No

1

No

No

2 (short, 60 km/h)

1

No

3 (short at 50 km/h; long at 25 km/h; short 60 km/h)

1

Second train (60s) Second train (34s) Second train (14s) Third train (35s)

2 (short, 60 km/h), then 2 (short, 60 km/h)

2 (re-opened for 2 min)

Yes

2 (short, 60 km/h), then 2 (short, 50 km/h), then 2 (short, 60 km/h) 2 (short, 60 km/h), then 2 (short, 50 km/h), then 2 (short, 60 km/h) 2 (short, 60 km/h), then 2 (short, 50 km/h), then 2 (short, 60 km/h)

3 (re-opened for 1 min each time

Second Second Second Second

• Decreasing waiting times: this group experienced an improving si-

train train train train

(60s) (34s) (14s) (60s)

No

Yes

Second train (34s) Second train (14s)

crossing with high non-compliance near the city of Melbourne, Australia. Waiting time had six levels ranging from 1.5 to 10 min, covering the range of recurrent level crossing closure durations recorded at this congested level crossing. Waiting times were on average 3 min long, allowing the efficient traversal of one to two trains, and therefore representing a value for the control condition.

tuation at the level crossing, simulating a situation where a currently congested level crossing is treated so that waiting times return to levels in line with the control condition.

2.1.1. Warning times Warning time was the time between activation of the flashing lights at the crossing and the arrival of the (first) train at the crossing. It was divided into three levels (low, medium and high). Although warning times comprise a proportion of (i.e., is included in) waiting time, initial activation of the flashing lights occurred some seconds (depending on vehicle speed and congestion) before the driver first stopped at or before the crossing (or proceeded in violation of the level crossing rules). Waiting time did not, therefore, include the total amount of warning time. Realistic Australian warning time values at congested level crossings values were obtained from the literature. Larue, Miska, et al. (2019)'s study in Brisbane, Australia measured warning times at seven railway level crossings known for their road congestion issues. This study suggested the use of the following values for this simulator study. The low warning time level used a 28 s warning time, as this value is the minimum warning time necessary to respect the Australian standard (for an active level crossing with two rail tracks), and the minimum warning time observed in the study conducted around Brisbane. A 75 s warning time was selected for the high warning time group as such duration was found to be a regularly observed extended warning time. It also aligns with long warning times reported in the US, the literature showing that warning times longer than 60 s are considered as long (Yeh et al., 2016). We used 50 s as a medium warning time level, as this value is approximately the average of the previous values and the average warning time provided to road users as observed in the study conducted around Brisbane.

2.1.3. Scenarios The testing session for each participant was composed of a sequence of six driving tasks (scenarios), differing in terms of waiting and warning times as determined by the number of trains and volume of traffic (the start point, destination and route remained the same). The six levels of waiting times selected for this study were 1.5, 2, 3, 5, 8 and 10 min, obtained by varying the number of consecutive trains going through the crossing (one to three, in line with observations at the Melbourne level crossing), as well as the number of road vehicles at the crossing (variable congestion). The long waiting times (above 5 min) were obtained by adding road congestion, which required the participants to experience two or three consecutive crossing closures before being able to proceed through the crossing. This approach was used as it has been shown at passive crossings by Larue et al. (2018c) that consecutive simulated driving is an effective approach to induce habitual driving behaviour when traversing passively protected level crossings. The details of each scenario in terms of train traffic and crossing activations can be found in Table 1. The level crossing was activated when participants were six to 10 s away from the crossing stop line. The activation time was randomly selected for each participant and each scenario with a uniform distribution. The range of activation times allowed participants to stop at or before the crossing without any difficulty in all of the six scenarios (it was necessary to stop before the crossing in the two scenarios where congested traffic prevented participants from reaching the level crossing stop line on initial approach).

2.1.2. Waiting times Waiting times were operationalised as the time from when the driver first stops before the crossing to the moment the driver has passed the crossing. It included the time needed to reach the crossing in case of congestion, and the time stopped at the crossing and the time to drive through the crossing beyond the stop line on the other side (for the traffic in the other direction). Realistic waiting times were obtained from Larue et al., 2018b's study of the level crossing closure times at a frequently congested level

2.1.4. Non-compliance measures Objective measures on non-compliance were obtained from the driving simulator data. They included the frequency of non-compliance with level crossing rules as found in the literature (Carlson and Fitzpatrick, 1999; Larue et al., 2018b; Liang et al., 2017a), specifically: entering the activated crossing before the boom gates are down; entering the crossing after the train passage but before the flashing lights are deactivated; stopping and/or reversing on the crossing. Compliance 3

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with general road rules, including speed limits during the simulated drives, was also examined.

2.3.2. Questionnaires Subjective measures were obtained from two questionnaires administered immediately before (Time 1) and after completion of all six simulated driving tasks (Time 2). The surveys were designed to collect data on participant traits, and post-experiment frustration (mood) levels as in Stephens and Groeger (2011). Questions offering participants the opportunity to provide comments and explanation were also included to permit a deeper qualitative exploration of factors and context underlying the overall (statistical) results through thematic (content) analysis. Measures of frustration levels were obtained immediately after each driving task using the NASA-TLX survey instrument (Hart and Staveland, 1987). The Time 1 questionnaire gathered information on socio-demographic characteristics, traffic crash and offence history, general driving behaviours, self-rated crash risk and driving ability (Comparative Optimism Bias Scale (Martha and Delhomme, 2014)). The Time 2 questionnaire asked participants to report their self-perceived performance in the simulated driving tasks in terms of completing the tasks on time (number of tasks), motivations for completing tasks on time, estimated average waiting time at the crossing, and number of and reasons for non-compliance (if any). The Time 2 questionnaire also included five further questions about general driving behaviour, compliance with level crossing and other traffic rules, and what participants considered a ‘reasonable’ level crossing waiting time.

2.2. Participants Participants were recruited from the general public in the city of Brisbane, Australia, including on the Queensland University of Technology campus. Recruitment methods included advertising on the university website, group email to university staff and students, printed A5 colour flyer distribution and snowballing techniques. Participants responded to the advertisements by email or telephone, a suitable time was scheduled with researchers, and the participants were sent an approved information sheet. A total of 62 male participants with a valid driver's licence were recruited for the study. Only male drivers were recruited as they are over-represented in level crossing violations and fatal crash statistics (Abraham et al., 1998; Raub, 2009) and therefore thought likely to provide more relevant data relating to non-compliance than a male/ female sample. All participants who completed their session (N = 60) received AU$50 for their time and effort. Excluding the two participants who did not complete the test drives, the mean age of participants was 29.4 years (SD 10.4), ranging from 18 to 63 years. Participants reported a mean period of 11.5 years since they first obtained a driver's licence (SD 10.1), with a range of 0.33–39.0 years.

2.4. Procedure

2.3. Materials

Participants first undertook a familiarisation drive to acquaint themselves with the driving simulator and the task they had to perform. The task was to drive in a suburban environment to an office building for an appointment (job interview) for which they should try to arrive on time, following an itinerary where the participant encountered one active level crossing with flashing lights, boom gates and bells. The route took around 3 min to complete if the crossing was open (no trains present). For all six driving tasks, participants were requested to drive as they normally would if driving on a public road. A monetary incentive/disincentive scheme was used to induce time pressure and a risk/reward component in the simulated driving tasks, similar to the methods used successfully by Fairclough and Spiridon (2012) and Lee (2010). All participants were offered AU$50 as compensation for their time and effort at the initial recruitment stage but were not informed about any other monetary incentives/disincentives until arriving at the testing station. For the incentive, participants were informed that they would be given an extra AU$5 in each instance that they arrived at the destination within 6 min of commencing the drives (i.e., ‘on time’). A digital clock was positioned on the centre display console of the vehicle so that participants could track time as they progressed. For the disincentive, participants were told that they might be penalised the amount of AU$5 for committing a traffic violation, if detected in the scenario by police, including ‘speeding, running a red light, or non-compliance with level crossing rules’. They were told that this penalty would also apply if they crashed the car. The incentive/disincentive scheme constituted a ‘minor deception’ in that, contrary to the information provided before the test drives, they did not receive any extra payments for arriving ‘on time’ nor receive any penalties for violations. Participants were debriefed after the session, which was approved in advance by the university's Human Research Ethics Committee (Approval number 1500000713).

2.3.1. Driving simulator The study was conducted on an advanced driving simulator (Fig. 1). This simulator was composed of a complete automatic Holden Commodore vehicle with working controls and instruments. The advanced driving simulator used SCANeR™studio 1.4 software with eight computers, projectors and a six degree of freedom (6DOF) motion platform that could move and twist in three dimensions. When seated in the simulator vehicle, the driver was immersed in a virtual environment which included a 180-degree front field of view composed of three screens, simulated rear-view mirror images on LCD screens, surround sound for the engine and environment noise, real car cabin and simulated vehicle motion. The road and environment were developed consistent with Australian Standards at crossings and to create realistic traffic around the driven car. The participant sat in the driver's seat of the car, seeing three screens where three RGB video projectors played the simulation. The participant drove the simulator with two pedals (brake and accelerator only) and a steering wheel providing force feedback.

2.5. Data analysis Data analysis comprised two main parts: (1) quantitative analysis of the data collected in the driving simulator and (2) quantitative and qualitative analyses of questionnaire data to examine: (a) general driving behaviour; (b) risk-taking tendencies; (c) subjective driving assessment in the simulator including non-compliance with road rules;

Fig. 1. View of the advanced driving simulator from the control room. On the screen, the level crossing was just deactivated and remains congested. 4

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(d) general attitudes about traffic rule compliance; and (e) general perceptions regarding level crossing amenity, functionality and related behaviour. Statistical analyses were conducted with software R version 3.4.1. Analyses of non-compliance for the different scenarios used Generalised Linear Mixed Models (GLMMs) from a Gaussian (for continuous variables) or Binomial (dichotomous variables). GLMMs are an extension of linear models that can incorporate non-normal data and are adapted for the analysis of longitudinal data by taking into account the correlations of data collected from the same participants (repeated measures design). The analysis of data aimed at evaluating the effect of the following independent factors that influence decision making and rule compliance: (1) waiting time, (2) warning time, (3) time-on-task (through drive number), (4) the sequence of scenarios (i.e. control, ascending and descending groups), and (5) interactions between these factors. These effects were evaluated on the following dependent variables:

Two participants (3.3%) reported having received a penalty for violation of level crossing rules in the last three years. No participants reported other detected traffic light or stop sign violations in the previous three years. Participants were asked how often they drove through level crossings in their day-to-day lives, the results of which included daily (8.3%), once or twice a week (21.7%), monthly (36.7%), and less than monthly (33.3%). The corresponding between-groups analysis revealed no statistically significant differences between the frequency of exposure to level crossings and: (a) age, (b) years licensed, (c) crash involvement, or (d) history of traffic offences. 3.2. Simulated driving tasks 3.2.1. Observed non-compliant behaviours Non-compliance counts are presented in Table 2 for each drive and for each waiting time group (ascending, descending and control). The most common non-compliance observed was entering the level crossing while it was activated (lights flashing) but gates not yet down, with 23 participants (38%) entering the crossing under such conditions. With 50 total instances of non-compliance, participants did not comply on average 0.8 times over the four drives in which they had the opportunity to do so (generally this was not possible in Scenarios 5 and 6). It is equivalent to a 20.8% chance of such non-compliance occurring per activation of the level crossing. Participants were also found to enter the crossing before it was completely deactivated in 26 occurrences, equivalent to a 10.8% chance per activation. The next most common transgressions were to stop on the yellow marking at the crossing when there was congestion on the other side of the crossing. Fifteen participants (25%) over 18 occurrences stopped on the yellow marking, apparently not realising they would be unable to proceed completely through the crossing when they decided to undertake the manoeuvre. A further two participants stopped momentarily on the crossing but reversed back when they realised that they would become stuck on the crossing behind congested traffic. Participants stopping on the crossing performed this behaviour on average 1.2 times out of their two opportunities to stop on the crossing behind stationary traffic, suggesting that most learnt from their initial mistake.

• Observed non-compliant behaviour rates (binomial) • •

○ Entering the crossing before the boom gates are down ○ Entering crossing before the flashing lights are deactivated ○ Stopping on the level crossing Maximum speed (Gaussian) Frustration levels (Gaussian), as measured on a 20-point scale as part of the NASA-TLX questionnaire.

Then, participants' responses to standardised questionnaire instruments, including attitudinal and open-ended questions, were examined. The qualitative data obtained from open-ended questions were analysed thematically, facilitating a deeper exploration into personal as well as environmental factors that impacted upon perceptions of appropriate waiting times as well as actual intentions to disregard level crossing rules. Drawing on a grounded theory approach (Corbin and Strauss, 1990; Yin, 1993), text transcripts were coded to identify major themes evident in participants' comments regarding why they had broken level crossing rules during the simulator test drives. Participants’ estimated average wait times and perceived acceptable wait times were analysed for differences between groups (increasing/decreasing wait times) using t-tests.

3.2.1.1. Entering the crossing before the boom gates are down. Statistical analyses with GLMM showed that the odds ratio of entering the crossing before the boom gates descended were higher by an overall factor of 16.2 for participants who encountered the longer waiting times first compared to control participants (t = 2.75, d.f. = 58, p = .008). Further, the odds ratio was reduced by a factor 2.5 for each drive performed for this group of participants (t = −2.83, d.f. = 212, p = .005), while no difference was found for the control group. There was also a statistically significant effect of waiting time for this group of participants, with a reduction by a factor 1.61 times the amount of time waited at the crossing (t = −2.30, d.f. = 212, p = .022). No differences were observed between the ascending and control groups, and no differences were found with warning times. Compared to the control and the ascending groups, these odds ratios show that violation rates were almost double in the descending group for 2 min waiting time (12% versus 7.5%), and nearly triple for 3 min (20% versus 7.5%).

3. Results 3.1. Self-reported driving behaviour (time 1 questionnaire) Forty-one participants (68.3%) reported no crash involvement in the previous three years. Fifteen participants (25.0%) reported one crash, while the remaining four participants (6.7%) reported two crashes. None of the reported crashes was believed to have resulted in serious injury (hospitalisation), and none occurred at a railway level crossing. Crash involvement was not statistically related to age or driving history. Four participants reported previous licence suspension, for offences including exceeding the maximum prescribed blood-alcohol content (2), dangerous driving (1), and combined offences (speeding, fail to keep left) (1). The small cell size precluded bivariate analysis with other socio-demographic characteristics. In terms of detected traffic infringements in the last three years, the most frequently reported transgression was speeding, with half of all participants reporting at least one such offence. One speeding offence was reported by 17 participants (28.3%), while 13 participants (21.6%) reported multiple offences. The mean number of detected speeding offences over the last three years for the entire sample was 0.8 (SD 0.97). Between-groups analysis revealed that participants who had received a speeding ticket in the past three years were not more likely to report crash involvement. Most participants (71.7%) reported exceeding the speed limit, with some doing so often (25.0%) and others sometimes (46.7%), as measured on a seven-point Likert-type scale.

3.2.1.2. Entering crossing before the flashing lights are deactivated. For entering the crossing after the train passed, but before the lights were deactivated, the odds ratio of non-compliance increased by a factor 0.62 (t = 3.49, d.f. = 246, p < .001) for each drive of the participant, showing an effect of driving the same road six times in a row. The odds ratio increased by 1.28 times the waiting time experienced in a particular scenario (t = 2.35, d.f. = 246, p < .001), but this was compensated through its interaction with the drive number, which decreased the ratio by a factor 1.28 for each drive (t = −4.97, d.f. = 246, p < .001). The effect of driving the scenarios with 5

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Table 2 Number of participants involved in risky behaviours at level crossings per drive, maximum speed per scenario (60 km/h limit), and levels of frustration. Waiting time group

Ascending (N = 23)

Descending (N = 20)

Control (N = 17)

Waiting time sequence

Non-compliant behaviours at the level crossing Traversing the activated LC before the gate is down

Entering the crossing before the signal is deactivated

Stopping on the yellow box

Reversing on the crossing

1.5 2 3 5 8 10 10 8 5 3 2 1.5 1.5 2 3 3 3 3

6 3 5 5 – – – – 5 6 5 2 4 0 3 1 2 3

0 3 2 1 3 1 2 0 0 1 3 2 3 1 1 0 2 1

– – – – 7 1 6 4 – – – – N/A N/A N/A N/A N/A N/A

– – – – 1 0 0 1 – – – – N/A N/A N/A N/A N/A N/A

Maximum speed (km/h)

Frustration (scale 1 to 20)

71 71.3 73.1 74.3 76.1 74.7 69.4 70.8 72.2 70.6 71.4 73.5 67.4 71.6 69.4 69.2 70.6 71.1

4.8 5.2 5.2 6.5 10.1 9.6 9.3 10.2 6.8 6.7 5.4 4.7 6.2 6.4 7.9 7.6 6.8 8.2

increasing waiting times resulted in an overall reduction of this type of non-compliance as compared to the control by a factor of −3.86 (t = −3.01, d.f. = 58, p = .004). This effect was complemented with an interaction with the waiting time of factor 1.25 (t = 4.38, d.f. = 246, p < .001). No statistically significant effects were found with warning times, or for the descending waiting time group. These odds ratio mean that for control participants, the probability of entering the crossing before it was deactivated tended to decrease as they drove more scenarios. On the other hand, participants experiencing increasing waiting times were more likely not to comply with the flashing lights as waiting times increased. It must be noted that participants in this group tended to be more respectful of the rules as compared to the control participants, as can be seen with their initial non-compliance rates in their first scenario. The increasing trend stopped when participants experienced a congested level crossing in scenarios 5 and 6.

times order (control, ascending, descending). Only one driver never drove above the posted speed limit of 60 km/h (with an overall maximal speed of 59.9 km/h). Statistical analyses with GLMM showed that warning times, waiting times, the order of the scenarios and their interactions were not having any statistical effect on the maximum speed that participants drove at during their drives. The only factor with an effect on speeding behaviour in this experiment was the number of the drive (i.e. time on task). The maximum speed increased by 0.72 km/h for each new drive (t = 3.20, d.f. = 212, p = .002). Participants drove at a maximum speed of 69.9 km/h during their first drive. Their speed increased up to a final maximal speed of 73.5 km/h for their last drive. This result is consistent with preliminary motorist and pedestrian research that has indicated being more familiar with crossings is associated with increased risktaking behaviour and violations (Freeman and Rakotonirainy, 2015; Wullems et al., 2014).

3.2.1.3. Stopping on the level crossing. Statistical analyses were conducted with GLMM to evaluate the effects of the factors of interest in this study on the probability of stopping on the level crossing (yellow grid). The odds ratio for non-compliance for participants experiencing increasing waiting times was lower by a factor 5.06 (t = −4.29, d.f. = 41, p < .001) for scenario 6 as compared to scenario 5. This ratio was lower for participants experiencing the decreasing waiting times by a factor 1.32 (t = −4.61, d.f. = 41, p < .001) for scenario 5, and by a factor 0.26 for scenario 6 (t = −4.69, d.f. = 41, p < .001). Warning times, time on task and other interaction between factors were not found to affect this non-compliance rate. These results show that participants tended not to make the same mistake twice, and were more likely to stop on the crossing the first time they arrived at the congested crossing. Non-compliance rate was higher for the participants who experienced the increasing waiting times, as these scenarios were their last two driving scenarios, as opposed to being the first two scenarios for the group of participants experiencing decreasing waiting times. However, there was no indication from the statistical analyses that this difference was due to time on task.

3.2.2. Frustration Statistical analyses with GLMM showed that the only factor that statistically significantly affected frustration was waiting time. For each increase in waiting time by a minute, frustration increased by 0.62 on the scale (t = 10.23, d.f. = 288, p < .001), starting during the first drive at 5.3. This corresponds to an increase to an average of 10.6 for the longest waiting time. These results are presented in Table 2, which highlights the statistically significant changes in the frustration levels for the different scenarios. 3.3. Self-reported non-compliance, estimated and perceived acceptable waiting times (time 2 questionnaire) Twenty-six participants (43.3%) reported that they thought they did not comply with level crossing rules (the mean number of rules thought broken was 1.01), while another five participants (8.3%) were unsure whether they had done so. Although the numbers were insufficient for reliable statistical analysis, there was no notable apparent difference between warning time groups regarding self-reported level crossing non-compliance during the test drives, either in the number of participants reporting a lack of compliance (Table 3) or in the themes that emerged as reasons for doing so (Table 4). Of those 26 participants who thought they had broken a rule, 92% commented on why they did so, and these responses were thematically analysed. The four themes that emerged from this analysis included, in

3.2.1.4. Speeding. The speeding behaviour of participants was evaluated by measuring the highest speed at which they were driving during each of their drives. Table 2 presents the distribution of the maximum speed for each scenario and for the three groups of waiting 6

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3.4. General comments about railway level crossings

Table 3 Self-report of non-compliance at the level crossing during test drives. Warning time Did you break any level crossing rules?

Low N (%)

Medium N (%)

High N (%)

Yes No Unsure Total

9 (42.9) 10 (47.6) 2 (9.5) 21

7 (36.8) 11 (57.9) 1 (5.3) 19

10 (50.0) 8 (40.0) 2 (10.0) 20

Asked if they would like to make any other comments about driving through level crossings, 28 of the 60 participants offered a response. As with reasons for breaking level crossing rules, these responses were grouped, culminating in the identification of 13 separate themes. The most prominent theme to emerge from this analysis was that participants felt unnecessarily delayed and frustrated if forced to wait when they could not see a moving train in close proximity (No apparent danger). Countering the prevailing overall view somewhat was the observation of one participant who warned against relying entirely on the signals to indicate the imminent approach of trains. Examples of comments relating to this theme include:

Total N (%)

26 (43.3) 29 (48.3) 5 (8.3) 60

order of prominence: (1) time pressure/monetary incentive; (2) impatience/frustration; (3) situation perceived as safe; and (4) misjudgement of the driving task. Four comments did not reveal an apparent reason and were therefore tagged “ambiguous”. It should be noted that the identified themes are not mutually exclusive, and therefore, multiple themes were attached to some comments. The prominence of time pressure/monetary incentive indicates that the incentive/disincentive scheme was successful in motivating participants to try to complete the driving tasks ‘on time’. Some examples of the reasons given for breaking the rules and the themes to which they relate include:

Crossing should not be closed when no trains are passing or approaching. The wait between two trains and the gates stay down often feels unnecessary. Waiting at a crossing while the train is still stopped at the station also feels unnecessary. In the second most prominent theme (Technology can reduce wait times), four participants commented that technology could be used to minimise waiting times and driver frustration. It was believed these objectives could be achieved by providing a visible countdown timer to inform drivers of the expected waiting time, having (or improving) train-activated signals, and by improved coordination of rail crossing closures with other nearby traffic signals.

To get to the interview fast and the $5 (Theme 1) Perhaps not waiting for the boom gates and lights to finish, because I was annoyed and in a hurry (Themes 1 and 2)

Put a count-down to know how long the driver have (sic) to wait. I think it is unreasonable to make cars wait while train is stopped at platform. Maybe something that activates the crossing once the train is ready to go would be good.

I had a very important appointment and couldn't risk (being) late just because the railway lights blink. If the train would be close already the bars would be down. So I did not see the necessity to stop (Themes 1 and 3)

The third prominent theme identified (Conditions influence risktaking) was that risk-taking behaviour at railway level crossings may be influenced by the traffic and environmental context. This may relate to the length and perceived speed of trains, whether a crossing is in a rural or urban setting, and the volume and characteristics of surrounding traffic.

I was sick of waiting and I could see the train (Themes 2 and 3) The gates weren't down and the train wasn't in sight, so it looked safe enough (Theme 3)

Different when waiting for very long coal trains in central Qld - much longer wait - influences desire to cross tracks to save time.

Felt light came on when too close to crossing, would have required harsh braking (Theme 4)

I'm more likely to drive through the red signals at a level crossing if there are no other vehicles around me.

Participants were asked how long they thought they waited on average at a level crossing during the test drives. Responses to this question ranged from 1 to 15 min, with a mean of 3.8 min (SD 2.67). Comparing the estimated wait times by sequence (increasing/decreasing wait times), those experiencing increasing wait times estimated a mean time of 5.0 min, compared with 2.6 min for decreasing wait time participants, t(41) = 3.08, p = .004. Secondly, participants were also asked how long they think it is reasonable to wait at a level crossing when driving on actual roads. Responses to this question ranged from 1 to 10 min, with a mean of 3.1 min (SD 1.95). The three most frequent responses to this question were 2 min (23.3%), 3 min (25.0%) and 5 min (18.3%). Comparing the perceived reasonable wait times by sequence, those experiencing increasing wait times reported on average that a longer time was acceptable than those experiencing decreasing wait times (M = 4.1 vs 1.9 min), t(41) = 3.94, p < .001.

The remaining themes largely reflect a range of comments made in isolation and were related to stopping distance, knowledge of rules, separation of road and rail traffic, audible and visual warnings, or identified as ambiguous. Findings from the analysis of general comments reinforce the analysis of self-reported reasons for breaking level crossing rules. 4. Discussion 4.1. Risky behaviours The non-compliant behaviours found in this simulator study are in line with what has been observed in the field by multiple research

Table 4 Reason for breaking rules theme by the warning time group. Theme

Low warning time

Medium warning time

High warning time

Total

1. Time pressure/monetary incentive 2. Impatience/frustration 3. Situation perceived as safe 4. Misjudgement of the driving task Ambiguous Total

4 3 3 3 – 13

4 – – 1 2 7

4 4 3 2 2 15

12 7 6 6 4 35

7

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studies: entering the level crossing once flashing lights are activated and before the train has passed (Larue et al., 2018b; Liang et al., 2017b; Zhang et al., 2018); stopping on the level crossing, whether the lights are activated or not (Larue et al., 2018b; Liang et al., 2017a); and entering the level crossing before lights are completely deactivated, or before being able to completely traverse the level crossing (Larue et al., 2018b). Such findings suggest that using driving simulation with successive traversals of a level crossing is appropriate for studying the regular behaviour of drivers at active crossings, complementing the previous validation of non-compliant behaviour due to complacency at passive crossings conducted by Larue et al. (2018c). Non-compliance rates found in this study tend to be larger than the ones found in field studies such as Larue and Naweed (2018) and Liang et al. (2017b). When compared to the field rates found in Australia during peak hours at a similar active level crossing (Larue and Naweed, 2018), the chance of a vehicle entering the activated level crossing before the train was around 10% per activation, compared to 21% in our study. For entering the crossing before it is completely deactivated, this was found in 2.6% of the cases in the field, compared to 11% in our simulator study. Stopping on the crossing was different, being found to occur 47.9% of the time per activation in the field, while it was 15% in our study. This difference might be because multiple vehicles could be stuck on the crossing in the field, as opposed to this simulator study. Such higher non-compliance rates in this study are likely due to two factors. First, we used a sample of male participants only, half of them being young drivers. Such drivers are more likely to deliberately disregard road rules as compared to the overall driving population. Further, non-compliance is likely to be higher in a simulator study as compared to an on-road study, given the reduced risks and consequences of such driving. Such findings are in line with the validation study conducted by Larue et al. (2018c) on passive level crossings, a study which has shown a relative validity of driving simulators, with higher non-compliance compared to field driving.

behaviour did not change for the control group. From waiting times longer than 3 min, participants in both the increasing and decreasing waiting time groups were more likely to engage in this risky behaviour as compared to control participants. Second, participants in the descending waiting time group were also more likely than other participants (i.e. control and ascending groups) to enter the crossing before the boom gates were down, and this for waiting times above 2 min. These results suggest that once participants are accustomed to a level crossing with a high level of congestion and extended waiting times, it is difficult for them to return to their baseline behaviour, even when waiting times are decreasing. The effects of decreasing waiting times are not immediate, suggesting a delay for participants in realising that the situation is improving at the crossing. To return to a baseline situation, waiting times shorter than 2 min were required. It suggests that once drivers get used to extended waiting times at a level crossing and are more likely to violate the crossing rules, reducing waiting times does not result in an immediate return to baseline behaviour, and non-compliance rates are above what they would be until waiting times reduce to below 2 min. Longer waiting times were attained with congestion at the level crossing. When congestion was present at the exit of the crossing, there was less incentive to traverse before complete deactivation of the flashing lights, as the road traffic would block participants after or on the crossing. Therefore, under such congested conditions, the main risky behaviour observed was being stopped on the level crossing. Overall participants experienced two scenarios with congestion (except for control participants), and they were not likely to be blocked on the crossing the second time. It suggests that these risky behaviours are not directly related to waiting durations, but rather to perception errors related to the complexity of this dynamic environment. It also suggests that these behaviours are not deliberate violations, in line with the observations reported in Larue and Naweed (2018)'s study. However, extended waiting times create conditions for increased errors by drivers navigating level crossings, and as a consequence, also increase exposure to risky conditions independent of deliberate violations. This study also found that the group experiencing increasing waiting times was more likely to engage in this error at level crossings. It is likely due to higher levels of cumulative frustration among participants who drove the longer scenarios last, and the repetition of the scenarios was found to be related to increased engagement in this behaviour. Regarding other traffic non-compliance in the vicinity of the level crossing, waiting times were not found to have any effect on the road users’ speeding behaviour. Speeding at or near the crossing was only a result of the repetition of the same scenario. Hence waiting times appear to have had effects on risky driving behaviour only at the level crossing itself, rather than more generally on their itinerary to their destination. Frustration was found to increase linearly with the waiting times; the longer the waiting times, the more frustrated the drivers became. There were no other factors included in this study which showed effects on the reported frustration levels. In particular, the repetition of similar scenarios did not increase frustration. The increment in frustration was linear and did not highlight a particular value for which the frustration started to increase faster. It should be noted that while closed crossings naturally contribute toward overall waiting times, other contributory factors such as congestion can also increase frustration levels in motorists and influence the likelihood of engaging in risky behaviours.

4.2. Waiting times Control group participants provided a baseline value for non-compliance at the level crossing under normal operating conditions. Participants in this group tended to behave similarly at the level crossing under given waiting time conditions, independently of the repetition of the task. Their level of frustration also aligned with waiting time durations, independent of time on task. Therefore, the control group allowed disentanglement of the effects of waiting times from the effects of task repetition, so the effects of waiting times can be investigated with the two other groups of participants. However, overspeeding by this group was shown to be related to time-on-task rather than the condition at the level crossing. Overall the total waiting time was found to affect participant noncompliance at level crossings. Considering all types of observed risky behaviours, participants in the current study engaged in more deliberate non-compliance when waiting times increased, in line with the findings from the literature (Richards and Heathington, 1990; Yeh and Multer, 2008). This was observed for all groups, including the control group. Odds of non-compliance were highest when drivers had to wait at the crossing longer than 3 min. Our results differ from those of Richards et al. (1990), where much shorter waiting times were necessary to attain significant levels of non-compliance. In that study, noncompliance was as high as 70% when waiting times were longer than 30 s. Some of the difference in results may be due to changes over time in regard to road safety attitudes and culture, and changes in the road and rail environment in terms of traffic and enforcement. This 3-min threshold is apparent in our study from two different findings. First, participants experiencing increasing or decreasing waiting times were more likely to enter the crossing before its full deactivation as waiting times increased, while the likelihood of engagement in this

4.3. Warning times Regarding the effect of warning times on driver behaviour and the likelihood of non-compliance with level crossing rules, warning times were only relevant in that they contribute to overall waiting times. In other words, longer warning times did not of themselves affect participants’ behaviour but contributed to longer waiting times, which are shown to be associated with greater rates of risky behaviours at level 8

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crossings. It is also worth recalling that according to previous research (Kadiyala et al., 2016) excessively long warning times can diminish warning credibility and therefore encourage non-compliance with rules, despite the lack of a specific finding to that effect in the current study (the current study did not seek to measure the perceived credibility of warning or waiting times directly). Other research suggests that warning time variability may be more relevant for understanding driver non-compliance at level crossings (Raslear, 2015), with higher variability being linked to uncertainty and hence better compliance, given the lack of accurate prediction that the drivers can make about the train arrival time. While there was no interaction between warning and waiting times, reducing warning times should be considered as one of the strategies to reduce the overall waiting time.

risks associated with driving a vehicle, this may not extend to level crossing scenarios. However, further research (with larger sample size) may illuminate the extent of the relationship between attitudes, risk perceptions and level crossing behaviours, particularly in regards to waiting times, as this was one of the first studies of its kind. Finally, and consistent with the above, participants’ level of sensation seeking was not related to attitudes about road safety nor level crossing rule compliance. This contrasts with the limited previous research (on pedestrians) indicating that males who frequently use pedestrian crossings and report higher sensation-seeking traits are most likely to break the rules (Freeman et al., 2015). Further research is also required into this relationship. The findings again indicate that motorists may well be aware of the risks associated with deliberately breaking crossing rules in vehicles. Overall, the current research has demonstrated that participants generally reported appropriate attitudes regarding road safety and crossing rules, and socio-demographic characteristics did not influence these attitudes.

4.4. Rail crossing and general attitudes toward non-compliance A complementary analysis of the self-report data was undertaken to explore further the attitudinal and socio-demographic characteristics associated with non-compliance at level crossings. This is important to consider, as driver behaviour theoretical models have proposed that attitudes are a significant determinant of driver behaviour (Vardaki and Yannis, 2013; Xu et al., 2014) and may yet be proven to be the strongest predictor of risky driving (Vardaki and Yannis, 2013). Firstly, a series of analyses focused on participants' attitudes towards level crossings and general road rules. The current study is one of the first to specifically examine a group of motorists’ attitudes regarding level crossing deliberate violations. This investigation revealed a high level of agreement with the importance of abiding by level crossing rules. Participants generally reported it was not acceptable to (a) cross at level crossings when the boom gate is down and the lights are flashing, nor (b) break crossing rules as long as the train is not in sight. It is noteworthy that this latter item recorded the highest level of consensus regarding non-acceptance, indicating that the sample recognised significant risks associated with the behaviour. However, as attitudes do not always correspond with subsequent behaviours, some level of caution should be used when interpreting this result. More broadly, the sample also believed it was important to obey general traffic rules, including speed limits, despite all but one of the participants exceeding the speed limit at some point during the trial. No significant between-group differences were identified in regards to the attitudes and (a) age or (b) traffic offending or crash involvement history. This contrasts with preliminary research indicating safer driving attitudes among older drivers (Xu et al., 2014), although the current findings may be due to the sample characteristics (i.e., the sample involved a larger proportion of younger drivers). Overall, the results support recent research indicating that motorists generally report appropriate attitudes regarding road rule compliance (Laapotti et al., 2003), although females may report more positive attitudes than males (Laapotti et al., 2003)). Further research is required to determine whether other personal and environmental characteristics influence (a) calculated perceptions of risk, (b) rule compliance attitudes, (c) self-reported transgressions and (d) actual transgressions.

4.6. Reasons for breaking level crossing rules Thematic analysis was undertaken to examine the self-reported reasons why participants did not comply with level crossing rules during the simulated driving tasks. Again, consistent with previous research (Freeman et al., 2015), prominent themes emerged regarding (a) general refusal to wait and (b) running late. The results also align with current chronic time pressure and perceptions of time shortage found in contemporary industrialised countries (Szollos, 2009). These results indicate that decisions to violate crossing rules mostly constitute a deliberate act, rather than a perceptual error, and are often associated with a perceived lack of danger. The results are reinforced by the analysis of general comments made by study participants. Within the broader rail safety literature, questions have remained regarding whether crossing transgressions result from deliberate acts or errors (Freeman et al., 2015; Stefanova et al., 2015). Preliminary research has generally indicated that drivers are likely to inadvertently engage in risky behaviours as a result of not detecting crossings, failing to notice approaching trains and misjudging the risk of approaching trains (Australian Transport Safety Bureau Statistical Unit, 2002; Wallace, 2008). In contrast, pedestrians appear more likely to make deliberate violations according to emerging evidence (Freeman et al., 2015). In the current context, the experiment required deliberate consideration of several elements, and it appears that running late is a primary reason for why crossing rules may be violated. Further research is required in this field that focuses on the decision-making processes (and corresponding factors) underlying disregard for level crossing rules and transgressions despite the recognised risks associated with the act. 4.7. Practical implications This study has highlighted the importance of ensuring that waiting times experienced by road users remain within reasonable values. In the current level crossing standards, only minimum waiting times are specified. It may be important for maximum waiting times also to be considered in these standards. Given the changes in the environment, it also appears important to regularly review waiting times and warning times in the field at level crossings, to identify level crossings with congestion issues. Such factors should also be included in the models used for prioritising level crossings requiring upgrades. In an environment with increasing congestion, it may not always be possible to treat all level crossings to ensure that waiting times remain below road users' acceptable threshold. Further, such crossings would be already be equipped with what is considered the gold standard for level crossings. When possible, grade separation should be considered, while other affordable treatments could also be considered by practitioners. For instance, train detection systems could be upgraded to take into account train speeds and predict arrival at the crossing more

4.5. Comparative optimism bias and sensation seeking Consistent with previous research (Delhomme et al., 2009; Martha and Delhomme, 2014), when required to rate their driving ability compared with the average driver of same age and gender, participants generally reported superior driving ability compared to others and a reduced likelihood of crash involvement. While this finding has important implications for road safety in general (in regards to addressing biased assessments of risk), for the current study it is noteworthy that this tendency was not strongly associated with attitudes about road safety. More specifically, those who believed they had lower risk did not report poorer attitudes about road safety or an increased willingness to breach rules at level crossings. This finding suggests that while drivers may tend to reduce the 9

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precisely. Train line speed could be reduced if actual train speeds are lower than the posted line speed. Train timetabling could also be further optimised to be more resilient toward variations from trains’ planned arrival time. Once no further improvements can be attained, enforcement of road rules may also be considered to reduce non-compliance by road users.

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4.8. Limitations This study has been conducted in a driving simulator. While this study has shown driver behaviour consistent with observations on real railway level crossings, it would be useful to confirm the findings in the field. The sample size used in this study was calculated to detect mediumsize effects. While it was statistically sufficient for determining the effects on our participant cohort, it is not enough to estimate whether such results can be generalised to the wider Australian driving population. The effects of warning time were only considered as a static constraint, using a given value to represent the situation at a given level crossing, and for ensuring participants were able to familiarise themselves with the level crossing conditions. While we found no effect of warning times, further studies may be required to evaluate the effects of variability of warning times, as some studies suggest this may be an important factor leading to non-compliance. We have also only considered a particular level crossing. While we ensured that it was representative of a typical congested Australian level crossing, using realistic warning and waiting times, the infrastructure environment and the type of level crossings may influence motorists’ decisions to disregard level crossing rules. Therefore, further research should investigate in the field the decision-making processes that lead to deliberate transgressions at railway level crossings. 5. Conclusion The current study has demonstrated through an Advanced Driving Simulator experiment that increased waiting times at railway level crossings result in higher levels of frustration and an increased likelihood of risky driving behaviour, particularly for waiting times longer than 3 min. Subjective data revealed that participants did not comply with level crossing rules due to factors including time pressure, impatience/frustration and low perceived risk. The comments offered to explain why participants broke level crossing rules indicate that the incentive/disincentive scheme was successful in motivating participants to try to complete the driving tasks ‘on time’. The analysis has shown that while waiting times are of high importance for increasing the likelihood of risky driver behaviour at railway level crossings, the presence of congestion and the need to stop at crossings for multiple cycles are additional factors likely to compound driver frustration and impatience. Overall, the results suggest that, where possible, waiting times should be standardised at values lower than 3 min to reduce the likelihood of risky road user behaviour. Additionally, the findings further reinforce the recognised need to minimise road congestion around railway level crossings. Acknowledgements The authors gratefully acknowledge the Australasian Centre for Rail Innovation (ACRI) for funding this research (Project LC/7–8). References Abraham, J., Datta, T.K., Datta, S., 1998. Driver behaviour at rail-highway crossings. Transp. Res. Rec. 1648, 28–34. Australian Transport Safety Bureau Statistical Unit, 2002. Level crossing incidents: fatal crashes at level crossings. In: Paper Presented at the 7th International Symposium on Railroad-highway Grade Crossing Research and Safety, Melbourne. Berg, W.D., Knoblauch, K., Hucke, W., 1982. Causal factors in railroad-highway grade crossing accidents. Transp. Res. Rec. 847, 47–54. Bertotti, G., 2014. The Science of Hysteresis 3-volume Set. Elsevier Science, Burlington.

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