How Incidental Learning Influences Train Driver Behavior and Safe

safety

Article What Employees Do Today Because of Their Experience Yesterday: How Incidental Learning Inﬂuences Train Driver Behavior and Safety Margins (A Big Data Analysis)

Julia Burggraaf 1,2,*, Jop Groeneweg 1,3,4, Simone Sillem 1 and Pieter van Gelder 1

1 Safety and Security Science Group, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, The Netherlands; [email protected] (J.G.); [email protected] (S.S.); [email protected] (P.v.G.) 2 ProRail, Moreelsepark 3, 3511 EP Utrecht, The Netherlands 3 Cognitive Psychology Unit, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands 4 TNO Leiden, Schipholweg 77-89, 2316 ZL Leiden, The Netherlands * Correspondence: [email protected]; Tel.: +31-639-108-553

Abstract: Employee behavior plays an important role in the occurrence and prevention of incidents, affecting safety margins. In this study, we examine the potential impact of incidental learning on human behavior in the presence of variation in task design. Incidental learning is the day-to-day on-the-job learning that occurs unintentionally. This learning influences which behavior (schema) is more likely to be activated in the employee’s brain. We posit that an incorrect schema can be activated and lead to undesired behavior if the employee is often exposed to (visually) similar tasks that require different behavior. In rail transport, there is a risk of trains passing through red signals. The train driver’s behavior plays an important role in preventing these signal passed at danger (SPAD) incidents. In this study we used speed and location data to analyze train driver deceleration behavior during red signal approaches in the Netherlands. The Dutch rail system showed variation in yellow Citation: Burggraaf, J.; Groeneweg, J.; signal aspects and signal distance. An analysis using 19 months of empirical data indicated changes Sillem, S.; van Gelder, P. What in behavior when the employee had been previously exposed to different behavior requirements in Employees Do Today Because of the same location with a similar yellow signal. These results imply that task design can be improved Their Experience Yesterday: How by taking into consideration what an employee is exposed to during other moments of the shift, Incidental Learning Influences Train and not just during the execution of the specific task. Driver Behavior and Safety Margins (A Big Data Analysis). Safety 2021, 7, Keywords: incidental learning; implicit learning; human behavior; schema; human error; task design; 2. https://doi.org/10.3390/ SPAD; train driver; rail safety7010002

Received: 29 September 2020 Accepted: 21 December 2020 Published: 5 January 2021 1. Introduction Incidents still occur, even in situations when rules and regulations are followed and Publisher’s Note: MDPI stays neu- organizations feel that they did everything they could to prevent them. One important tral with regard to jurisdictional clai- element in incident causation and prevention is the behavior of employees at the “sharp ms in published maps and institutio- end”. How can organizations support their employees in executing tasks as desired? nal affiliations. In this study, we look at the risk of a train passing through a red signal. A signal passed at danger (SPAD) event can lead to a derailment or collision. Even those SPAD events with negligible chances of derailment or collision incur costs in terms of delays, required reactive actions, and the emotional state of train drivers [1]. In 2019, there were 142 SPADs Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. in the Netherlands [2]. Train driver behavior has a large influence on whether SPADs occur This article is an open access article or not. Train drivers in the Netherlands are trained, tested, and experienced, and are fully distributed under the terms and con- aware of the risks involved during red aspect approaches. Many developments have also ditions of the Creative Commons At- been made to improve the infrastructure to better fit the tasks of train drivers. The visibility tribution (CC BY) license (https:// of signals has for example been improved and care is taken that signals are not placed in creativecommons.org/licenses/by/ confusing locations. There is however still some variation in train driving behavior that is 4.0/). not understood.

Safety 2021, 7, 2. https://doi.org/10.3390/safety7010002 https://www.mdpi.com/journal/safety Safety 2021, 7, x FOR PEER REVIEW 2 of 23

Safety 2021, 7, 2 2 of 22 locations. There is however still some variation in train driving behavior that is not understood. In this paper, we examine incidental learning as a factor impacting human behavior [3]. IncidentalIn this paper, learning we examinecan have incidental a positive learning or a negative as a factor impact. impacting Incidental human learning behavior is the [3 ]. learningIncidental that learning occurs without can have an a explicit positive intention or a negative [4]. It is impact. the on-the-job Incidental learning learning that is oc- the curs,learning in contrast that occurs to learning without during an explicit training intention sessions [4]. and It is courses. the on-the-job In experimental learning that settings, occurs, a indistinction contrast tobetween learning intentional during training learning sessions and incidental and courses. learning In is experimental made depending settings, on thea distinctioninstructions between that participants intentional are learning given. During and incidental the incidental learning learning is made condition, depending the on the instructions that participants are given. During the incidental learning condition, participants are not aware of the learning situations and are not instructed as to what they the participants are not aware of the learning situations and are not instructed as to what will truly be tested on [5]. they will truly be tested on [5]. If there is indeed a significant negative influence of incidental learning, this is im- If there is indeed a significant negative influence of incidental learning, this is impor- portant to understand as it can undermine results of explicit training and awareness cam- tant to understand as it can undermine results of explicit training and awareness campaigns paigns (See Figure 1). It is of course important to train employees (see top right of figure), (See Figure1). It is of course important to train employees (see top right of figure), but if but if incidental learning teaches employees different behavior (bottom left of figure), then incidental learning teaches employees different behavior (bottom left of figure), then this this explicit training will be partly undone. explicit training will be partly undone.

Figure 1. Differences during intentional and incidental learning can lead to human error despite Figure 1. Differences during intentional and incidental learning can lead to human error despite thoroughthorough explicit explicit training. training. Sectio Sectionsns in in green green indicate indicate an an employee employee receiving receiving explicit explicit training training on how howhe orhe sheor she should should perform perform according according to companyto compan standards.y standards. The The yellow yellow sections sections indicate indicate what what an anemployee employee actually actually experiences experiences on ona a day-to-dayday-to-day basis.basis. If these differ, differ, then then human human error error can can oc- occur curdespite despite the the employee employee successfully successfully following following the the intentional intentional learning learning sessions. sessions.

IncidentalIncidental learning learning is isdifficult difficult to to identify identify as as a acause cause for for changes changes in in human human behavior. behavior. OneOne reason reason for for this this is is that that incidental incidental learning learning can can be be part part of of implicit implicit learning. learning. This This means means thatthat the the employee employee is is not not necessarily necessarily aware aware of of what what he he or or she she has has learned learned or or even even that that he he or or sheshe has has learned. learned. Implicitly Implicitly learned learned knowledge knowledge can can control control action, action, but but the the learner learner himself himself is notis notable able to tell to tellothers others that thatthis thisis what is what happened happened [6–8]. [6 Wang–8]. Wang and Theeuwes and Theeuwes focus focus on im- on plicitimplicit attentional attentional bias and bias show and show that people that people quickly quickly pick up pick on upvisual on visualchanges changes in the envi- in the ronmentenvironment and change and change their beha theirvior behavior accordingly accordingly even though even though they are they not are aware not aware of the of changes.the changes. They Theyconclude conclude that “people that “people adapt adapt to a changing to a changing environment environment but that but there that there are lingeringare lingering biases biases from previous from previous learned learned experien experiencesces that impact that impactthe current the currentselection selection priorities”priorities” [9]. [9]. Another reason that incidental learning can be difficult to identify is that during an Another reason that incidental learning can be difficult to identify is that during an incident analysis, the situation at the time of the incident is analyzed. Whilst the causes incident analysis, the situation at the time of the incident is analyzed. Whilst the causes of of the situation might also be analyzed, the preceding “normal” situation is often not the situation might also be analyzed, the preceding “normal” situation is often not ana- analyzed. Thus, what the employee or train driver is exposed to on a daily basis before lyzed. Thus, what the employee or train driver is exposed to on a daily basis before the the incident is not necessarily considered. Even when it is, it is hard to prove the impact incident is not necessarily considered. Even when it is, it is hard to prove the impact of of previous exposure, i.e., incidental learning. In the case of SPADs, there are simply not enough incidents to analyze this cause systematically without specific direction and

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detailed hypotheses. A third reason for difﬁculty of detecting incidental learning in the past may be small effect size.

1.1. Incidental Learning Influences the Schemas in an Employee’s Brain Incidental learning influences the development and activation of schemas in an employee’s brain. Schemas embody the procedural knowledge that is needed to carry out actions [10–12]. Schemas can be described as generalized procedures for carrying out actions. In novel tasks, when a schema does not yet exist, much attention is needed to carry out the action. Once schemas are present, these actions can mostly be performed automatically, i.e., with little attention required. Schemas thus help us perform actions more efficiently [13]. Actions will be performed correctly if the right schema is activated at the right time. Schemas can be activated in a top-down fashion via the intention to perform an action. This requires attention. Schemas can however also include triggering conditions. If the environmental conditions match the triggering conditions, then the schema can be activated without conscious thought. For example, if one has a cup nearby on the desk, he/she can pick it up and have a sip without explicit intention or even thirst. The mere sight of the glass can trigger the schema to pick it up (see Ref. [14,15] with respect to unconscious control of motor action; Ref. [16–19] specifically for hand movement). An event (a cue) can become a trigger for a schema when it is often paired with the execution of the schema. The more often they are paired, the stronger the schema activation will be upon perception of the cue. This linking of a cue to a schema is part of incidental learning. Problems occur when the incorrect schema in one’s head is activated. Correct behavior is then activated, but it is unsuitable for the specific situation. We hypothesize that this is more likely to occur if there is variation in task design. Specifically, we posit that human error is more likely to occur if different behavior is required in (visually) similar settings. An example is crossing the street on foot. In right-driving countries, pedestrians should look left and right and left again, before crossing. When a pedestrian goes on holiday to a left-driving country, he or she should look right and left and then right again, but the pedestrian is inclined to look in the pattern he or she is used to, namely left–right–left. This is clearly not caused by a sudden lack of head turning ability, but caused by a different requirement in a similar situation (crossing a road). It can therefore occur even if the pedestrian is fully aware of the rules that apply in a given country and wishes to adhere to them (see e.g., research using the Stroop test for ample evidence of people erring in the simple task of naming a color because they read the colored word instead [20]). The same applies to driving a car. People are perfectly capable of taking a roundabout clockwise. They are also perfectly capable of taking a roundabout anti-clockwise. However, going on holiday and driving on the opposite side of the road than one is used to is very difficult the first few times. When there are other cars around, this is a visual reminder that one is in a different country and the roundabout should be taken the other way round. However, when there are no other cars in sight or there are other distracting traffic situations present, it is easy to veer into the old pattern and take a roundabout the wrong way round.

1.2. Application to Rail During a red aspect approach, it is the train diver’s task to decelerate sufﬁciently to stop in front of the red aspect. The driver has schemas in his brain for the deceleration behavior. The signal aspects along the tracks provide information on which behavior is suitable. A red aspect is preceded by a yellow aspect to inform a train driver that a red aspect is coming and that he should start to decelerate. In contrast to road transport, this is necessary because trains have a very long braking distance (e.g., 580 m at 140 km/h and an emergency deceleration of 1.3 m/s2). In the Dutch signaling system, the aspect sequence green–yellow–red is most common, but other yellow aspects are also used. A yellow aspect can for example be combined with a number. If the distance between the yellow and red signal is relatively short given the track speed, then the yellow signal can be preceded by, Safety 2021, 7, x FOR PEER REVIEW 4 of 23

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green–yellow–red is most common, but other yellow aspects are also used. A yellow as-

Safety 2021, 7, 2 pectgreen–yellow–red can for example is be most combined common, with but a number.other yellow If the aspects distance are between also used. the Ayellow4 of yellow 22 and as- redpect signal can foris relatively example shortbe combined given the with track a number. speed, then If the the distance yellow signalbetween can the be yellowpreceded and by,red for signal example, is relatively yellow shortwith giventhe number the track four speed, (yellow:4) then the (See yellow Figure signal 2). In can that be case, preceded the driver will have to reduce his/her speed and drive at 40 km/h or less by the next signal. by,for example,for example, yellow yellow with the with number the number four (yellow:4) four (yellow:4) (See Figure (See2). In Figure that case, 2). the In driverthat case, the driverwill have will to have reduce to his/her reduce speed his/her and speed drive and at 40 drive km/h at or 40 less km/h by the or nextless signal.by the next signal.

Figure 2. A yellow signal with number 4 indicating a required speed reduction to 40 km/h, to be reached by the next signal. FigureFigure 2. A yellowyellow signal signal with with number number 4 indicating4 indicating a required a required speed speed reduction reduction to 40 km/h,to 40 km/h, to be to be reachedreached byby thethe next next signal. signal. There are multiple forms of variation in rail task design that can cause incorrect schemaThere activation areare multiplemultiple after incidental forms forms of of variation learning. variation in One rail in taskrailtype task design of variationdesign that can that is cause the can combination incorrectcause incorrect of variationschemaschema activationinactivation permitted after after track incidental incidental speed learning. and learning. in distance One One type betweentype of variation of variationsignals. is the These combinationis the cause combination variation of of variation in permitted track speed and in distance between signals. These cause variation in invariation the amount in permitted of deceleration track speedthat is and necessary in distance to stop between in front signals. of the redThese signal. cause In variation Figure 3 itthe is amountillustrated of deceleration that in the thatleft isscenario, necessary a tocontinuous stop in front deceleration of the red signal. rate of In 0.26 Figure m/s3 it2 would inis illustratedthe amount that of in deceleration the left scenario, that a is continuous necessary deceleration to stop in front rate of of 0.26 the m/s red2 signal.wouldbe In Figure be sufficient to stop in front of the red signal, while in the situation on the right, a 2decel- 3sufﬁcient it is illustrated to stop in that front in ofthe the left red scenario, signal, while a continuous in the situation deceleration on the right, rate a decelerationof 0.26 m/s would eration rate of 0.59 m/s2 is needed. If a driver is more often exposed to the situation on the berate sufficient of 0.59 m/s to 2stopis needed. in front If aof driver the red is moresignal, often while exposed in the to situation the situation on the on theright, left, a decel- left,erationthen then the ratethe cue cue of “yellow 0.59 “yellow m/s aspect”2 aspect”is needed. can can trigger If trigger a driver the initiation the is initiation more of often a schema of aexposed schema resulting toresulting the in asituation slower in a slower on the rateleft,rate of then of deceleration deceleration the cue “yellow than requiredrequired aspect” for forcan the the trigger situation situation the on initiation theon the right. right. of a schema resulting in a slower rate of deceleration than required for the situation on the right.

FigureFigure 3. There 3. There is variation is variation in inthe the necessary necessary rate rate of deceleration. In In the the left left approach, approach, a continuous a continuous deceleration deceleration rate of rate 0.26 of 0.26 m/s2 is sufficient2 to stop in front of the red signal, while the approach on the right requires a deceleration rate of at least Figurem/s 3.is There sufﬁcient is variation to stop in in front the ofnecessary the red signal, rate of while deceleration. the approach In the on left the approach, right requires a continuous a deceleration deceleration rate of at least rate of 0.26 0.59 m/s2. 2 m/s0.592 is m/ssufficient. to stop in front of the red signal, while the approach on the right requires a deceleration rate of at least 0.59 m/s2. TheThe above above example example illustrates illustrates variation inin thethe requiredrequired deceleration deceleration for for the the same same signalsignal aspect aspect (yellow). (yellow). In Dutch In Dutch rail, rail,there there is also is also variation variation in which in which signal signal aspect aspect is ispresent presentThe at above a given example location. illustrates Figure4 shows variation that signal in the Sx required can have deceleration signal aspect for yellow:4, the same sig- at a given location. Figure 4 shows that signal Sx can have signal aspect yellow:4, as part nalas partaspect of a(yellow). yellow:4 yellow–redIn Dutch rail, sequence. there is It also can alsovariation have ain yellow which aspect signal as aspect part of is a present ofatgreen–yellow–red a ayellow:4 given location. yellow–red sequence. Figure sequence. 4 shows Itthat can signal also haveSx can a haveyellow signal aspect aspect as part yellow:4, of a green– as part yellow–redof a yellow:4A signal sequence. canyellow–red also have sequence. a yellow:number It can aspectalso have as part a yellow of a speed aspect restriction. as part Thisof a green– yellow–redkind of speed sequence. restriction is sometimes needed to prevent trains from driving too fast over a switch (Figure5). Aspect yellow:4 indicates a speed restriction to 40 km/h by the next signal, whilst yellow:8 signals a speed restriction to 80 km/h, etc.

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Figure 4. There can be variation in the signal aspect at a specific location. In the top approach, the first signal has aspect yellow:4 (Y-4) because the signal at the station is red and the distance between the last two signals is insufficient for a green–yellow–red sequence. In the bottom approach, the first signal is yellow because the next signal is red.

FigureA 4. signalThere can can be also variation have in thea yellow:number signal aspect at a specificaspect location. as part In of the a topspeed approach, restriction. the This Figurekind of4. Therespeed can restriction be variation is sometimes in the signal needed aspect atto a prevent specific location.trains from In the driving top approach, too fast the over firstfirst signal signal hashas aspectaspect yellow:4 yellow:4 (Y-4) (Y-4) because because the signalthe si atgnal the at station the station is red and is red the and distance the distance between be- a switch (Figure 5). Aspect yellow:4 indicates a speed restriction to 40 km/h by the next tweenthe last the two last signals two issignals insufficient is insufficient for a green–yellow–red for a green–yellow–red sequence. In thesequence. bottom approach,In the bottom the first approach, thesignalsignal, first is signal yellowwhilst is because yellow:8yellow the because nextsignals signal the a next issp red.eed signal restriction is red. to 80 km/h, etc.

A signal can also have a yellow:number aspect as part of a speed restriction. This kind of speed restriction is sometimes needed to prevent trains from driving too fast over a switch (Figure 5). Aspect yellow:4 indicates a speed restriction to 40 km/h by the next signal, whilst yellow:8 signals a speed restriction to 80 km/h, etc.

FigureFigure 5. 5.There There can can be variationbe variation in the in signal the signal aspect aspect at a specific at a location.specific location. In the top approach,In the top the approach, first the signalfirst signal has aspect has aspect yellow:6 yellow:6 (Y-6) because (Y-6) thebecause switch the after switch the next after signal the next has a signal maximum has a permitted maximum per- speedmitted of speed 60 km/h. of 60 In km/h. the bottom In the approach, bottom theapproach, first signal the is first yellow signal because is yellow the next because signal isthe red. next signal is red. In this study, we investigated the effect of the above variations on train driver driving behavior. An additional infrastructure characteristic that was taken into account was the In this study, we investigated the effect of the above variations on train driver driving track speed limit just before the first yellow signal. We did not expect to see an effect of Figurebehavior. 5. There An can additional be variation infrastructure in the signal characaspect teristicat a specific that location. was taken In the into top account approach, was the the firstincorrect signal schema has aspect activation yellow:6 for (Y-6) those because approaches the switch with suchafter lowthe speednext signal that thehas train a maximum driver per- track speed limit just before the first yellow signal. We did not expect to see an effect of mittedcould speed start with of 60 decelerationkm/h. In the uponbottom sight approach, of the the red first signal signal and is still yellow come because to a standstill the next signal iswithincorrect red. mild schema deceleration. activation The issue for those of incorrect approaches schema with is assumed such low to bespeed mostly that relevant the train driver forcould those start approaches with deceleration where the upon train driversight of needs the red to decelerate signal and before still come the red to signala standstill is with visible.mildIn deceleration. this This study, is because we The automaticinvestigated issue of behavior incorrect the effect can schema also of the occur is above assumed without variations theto be use mostly on ofschemas. train relevant driver This fordriving those can occur when the information needed to perform the action is directly available in the behavior.approaches An whereadditional the train infrastructure driver needs charac to deceleteristicrate that before was thetaken red into signal account is visible. was Thisthe environment [21]. When the driver can see the red signal, he can estimate the distance trackis because speed automaticlimit just before behavior the canfirst also yellow occur signal. without We thedid use not of expect schemas. to see This an caneffect occur of andwhen the the best information rate of deceleration. needed Whento perform the train the driver action has is todirectly start decelerating available in before the environ- incorrectseeing the schema red signal, activation he needs for to those rely fully approaches on the information with such stored low speed in the schemathat the in train his driver couldlong-termment start [21]. memory. with When deceleration the driver upon can see sight the of red the signal,red signal he canand estimatestill come the to adistance standstill and with the mildbest deceleration. rate of deceleration. The issue When of incorrect the train schema driver is has assumed to start to decelerating be mostly relevant before seeingfor those the approaches1.3.red Previoussignal, wherehe Research needs the on to train Incidental rely driver fully Learning on needs the and infoto Taskdecelermation Designrate stored Variationbefore in the the red schema signal in is hisvisible. long-term This ismemory. becauseThe field automatic of human behavior factors looks can also at the occur influence without of system the use or taskof schemas. design on This human can occur whenbehavior the [ 22information]. There is however needed a to strong perform focus onthe task action design is directly at the moment available of performing in the environ- mentthe1.3. task Previous[21]. and When notResearch onthe the driver on potential Incidental can influencesee Learning the red of and previoussignal, Task he Design exposure can estimate Variation to other the task distance designs. and the Experience is also mentioned as a positive factor, without the nuance that experience and best rateThe of field deceleration. of human When factors the looks train at driver the infl hasuence to start of system decelerating or task before design seeing on human the variation can interact to lead to errors. redbehavior signal, [22].he needs There to is rely however fully ona strong the info focusrmation on task stored design in theat the schema moment in his of performinglong-term memory.

1.3. Previous Research on Incidental Learning and Task Design Variation

The field of human factors looks at the influence of system or task design on human behavior [22]. There is however a strong focus on task design at the moment of performing

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Some commonly used taxonomies of human error causes, for example, do not include this factor. One accident analysis method called the Human Factors Analysis and Clas- sification System (HFACS) was inspired by Reasons’ popular Swiss Cheese model and provides a taxonomy of failure across four organizational levels: unsafe acts, preconditions for unsafe acts, unsafe supervision, and organizational influences [23]. Of the seven preconditions for unsafe acts, the “technological environment” is most aligned with the idea of task design. This precondition is further clarified as encompassing “a variety of issues including the design of equipment and controls, display/interface characteristics, checklist layouts, task factors and automation” (p.62). The focus is mostly on the state of the individual at that precise moment, and not the impact of previous learning. One human reliability analysis method, the SPAR-H method, estimates error probability and contains a list of performance-shaping factors (PSFs). The eight PSFs are: available time, stress/stressor, complexity, experience/training, procedures, ergonomics/human- machine interface, fitness for duty, and work process. The PSF “experience/training” can only be scored as poor, nominal, or good (or it can be considered that there is insufficient information). For this factor, more experience is considered better and reduces the (calculated) probability of an error [24]. In our research, the hypothesis is that greater experience can lead to errors, if combined with problems in task design. The PSF “ergonomics/human- machine interface” comes closest to the idea of task design, but focuses mostly on the state at that moment and not the impact of previous learning. In scientific SPAD literature, the role of infrastructure elements is mainly considered with respect to visibility and interpretability of the signal [25–27]. One study on driver performance modeling and its practical application included line speed as related to signal and sign visibility and reading times [28]. One human factors SPAD hazard checklist contains the following scoring factors: the presence of driver’s personal factors, driver inattentiveness, signal visibility, the association between the signal and the correct line, the ability to read signal aspect correctly, the ability to interpret signal aspect correctly, and the ability to perform correct action [29]. There is no factor for task variation. Within the rail industry, there are some recommendations on infrastructure variation, such as making sure there is no standard caution or low speed aspect in front of the red signal, because “permanent caution signals, for example, do not provide drivers with information about the next signal, and can therefore be a SPAD trap” [30]. Incident investi- gations at the Dutch Rail infrastructure manager ProRail have also led to the hypothesis that such locations pose a risk, but the mechanism and the size of the effect are unclear. Up until recently, there was not enough data to test these effects rigorously. The UK Rail Safety and Standards Board (RSSB) conducted a large-scale investigation in 2016, reviewing 257 industry SPAD investigation reports and organizing SPAD workshops with 60 participants with various job titles from freight operating companies, passenger operating companies, and the UK Infrastructure manager Network Rail [31]. They identified 10 risk management areas, such as signal design/layouts and driver competence management including route knowledge. The recommendations for signal design/layouts focus mostly on visibility of the signal and design of the signal itself and of the gantry. The route knowledge was considered as positive in that report. Route knowledge is also in other countries mentioned as a positive and important factor [32]. Variations in signal aspect shown on the same route are not mentioned. Balfe, on the other hand, mentions expectation bias as a factor influencing SPADs in her review of 83 internal investigation reports of SPADs occurring between 2005 and 2015 on the Irish rail network [33]. The exact link between expectation bias and infrastructure is not specified. This author does mention the potential for congested networks to result in single or double signals being routinely experienced by drivers across a route, thereby leading to an expectation of continued movement rather than a subsequent stop signal upon seeing a yellow aspect. Safety 2021, 7, x FOR PEER REVIEW 7 of 23

leading to an expectation of continued movement rather than a subsequent stop signal upon seeing a yellow aspect.

1.4. Objective of this Study Safety 2021, 7, 2 7 of 22 The objective of this study was to investigate whether incidental learning impacted employee task performance in the presence of task design variation. We hypothesized that incorrect schema activation caused lower deceleration rates and thereby smaller safety 1.4. Objective of This Study margins between trains and red signals. We focused on similarities in the yellow aspect The objective of this study was to investigate whether incidental learning impacted and the location as triggers for schema activation. The specific question was: employee task performance in the presence of task design variation. We hypothesized that incorrect• Does schema frequent activation exposure caused to certain lower decelerationsignal aspects rates (at and certain thereby locations) smaller impact safety the be- marginshavior between in a (visually) trains and similar red signals. but Wedeviating focused situation? on similarities in the yellow aspect and theIn locationprevious as railway triggers forresearch, schema research activation. questions The specific like question this could was: not be answered due •to smallDoes sample frequent size. exposure Thanks to to certain technological signal aspects developments, (at certain locations)we now have impact different the tools that behaviormake it possible in a (visually) to answer similar questions but deviating that situation? could not be answered in the past. In previous railway research, research questions like this could not be answered due to1.5. small Hypotheses sample size. Thanks to technological developments, we now have different tools that make it possible to answer questions that could not be answered in the past. We hypothesized that incorrect schema activation was a cause of insufficient decel- 1.5.eration, Hypotheses potentially resulting in SPADs or near-misses. We identified four situations where this couldWe hypothesized occur. The that more incorrect common schema signal activation approach was ais cause here of referred insufficient to as decelera- “the standard tion,approach”. potentially The resulting less common in SPADs approach or near-misses. is referred We identifiedto as “the four deviating situations approach”. where This thisdeviating could occur.approach The moreis also common the safety-critical signal approach approach. is here referredIf the schema to as “the of the standard standard ap- approach”.proach is activated The less commonduring the approach deviating is referred approach, to as then “the deviatingan incorrect approach”. schema Thisis activated. deviatingThe more approachoften the istrain also driver the safety-critical is exposed to approach. the standard If the situation, schema of the the higher standard the chances approach is activated during the deviating approach, then an incorrect schema is activated. of incorrect schema activation during the deviating approach. The more often the train driver is exposed to the standard situation, the higher the chances of incorrectIn Dutch schema rail, activation there are during two main the deviating types of approach. situations when a specific signal is often yellowIn Dutchand can rail, become there are “the two standard main types approach”. of situations One when situation a specific is when signal the is scheduling often is yellowsuch that and the can signal become at “the the standardtrain’s stopping approach”. location One situation is often is red. when The the signal(s) scheduling preceding is it suchwill be that yellow the signal at an at equal the train’s frequency. stopping We location call this is often “yellow red. Theentrance signal(s) to the preceding station”. it will beWhen yellow “yellow at an equal entrance” frequency. is common, We call this this “yellow is the entrancestandard to situation. the station”. The deviating sit- uationWhen is then “yellow when entrance” one signal is common, earlier is this red. is theIn these standard situations, situation. we The can deviating find similarities situationbetween isthe then standard when one and signal deviating earlier situation is red. In thesein terms situations, of signal we aspect can find and similarities signal location. between the standard and deviating situation in terms of signal aspect and signal location. The required behavior is however different, because an earlier signal is red. In Figure 6, The required behavior is however different, because an earlier signal is red. In Figure6, the standard,standard, more more common common situation situation is visualizedis visualized at the at the top top (blue). (blue). Below Below this this is the is the de- deviatingviating situation situation (red). (red). In In this this scenario, scenario, the signal aspectaspect and and location location are are exactly exactly the the same sameduring during both bothapproaches. approaches.

Figure 6.6.The The yellow yellow aspect aspect in thein the deviating deviating approach approach (bottom) (bottom) is at the is same at the location same location as the yellow as the yel- aspectlow aspect during during standard standard approach approach towards towards the station the (top). station During (top). both During approaches, both approaches, the cue is the the cue sameis the (yellow) same (yellow) and the locationand the is location the same. is Thethe stationsame. stopThe isstation shown stop by a is rectangle shown withby a passengers. rectangle with passengers. The second main situation where a speciﬁc signal is often yellow is when that signal often functions as a speed limit indicator in front of a switch (Figure7). The standard, more common situation is shown at the top (blue). Below this is the deviating situation (red). In this scenario, the location is exactly the same during both approaches and the signal aspect is visually similar.

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The second main situation where a specific signal is often yellow is when that signal often functions as a speed limit indicator in front of a switch (Figure 7). The standard, more commonThe second situation main is situation shown atwhere the top a specific (blue). siBelowgnal is this often is theyellow deviating is when situation that signal (red).often In thisfunctions scenario, as athe speed location limit isindicator exactly thein front same of during a switch both (Figure approaches 7). The and standard, the signalmore aspect common is visually situation similar. is shown at the top (blue). Below this is the deviating situation Safety 2021, 7, 2 8 of 22 (red). In this scenario, the location is exactly the same during both approaches and the signal aspect is visually similar.

Figure 7. The yellow aspect in the deviating approach (bottom) is at the same location as the yellow:number aspect during the standard approach because of the upcoming switch (top). For both approachesFigure 7. the TheThe cue yellow yellowis visually aspect aspect similar in inthe the (yellowdeviating deviating vs approach. yellow:number) approach (bottom) (bottom) and is at the isthe location at same the samelocation is the location same. as the asyel- the yellow:numberlow:number aspect aspect during during the the standard standard approach approach because because of of the the upcoming upcoming switch switch (top). (top). For For both both approachesIn the above the cueexample, is visually the similar yellow (yellow(yellow and yellow:number vs.vs. yellow:number) signal and theaspects locationlocation are isis said thethe same.same.to be visually similar. Visual similarity is defined by the number of shared points or common features,In and the the aboveabove type example,example,of differenc thee. yellowyellowVisual similarity andand yellow:numberyellow:number is higher with signal deletion aspects at end are saidsaidpoints toto bebe (suchvisually as the similar.number Visual4 missing similarity below) isthan deﬁneddefine for ddifferences by thethe numbernumber like deletions ofof sharedshared leading pointspoints to oror breaks commoncommon in continuityfeatures, and or mirror the type image of difference.differenc reversalse. Visual[34]. The similarity signals isiswith higher yellow with and deletion yellow:number atat end points aspects(such are as thus the number visually 4 similar missing because below) thanthey forhave differences many visually likelike deletionsdeletions identical leadingleading points toto with breaksbreaks the indifference continuity being or mirror a deletion imageimage at the reversalsreversals bottom [[34].34 (Figure]. TheThe signalssignals8). withwith yellowyellow andand yellow:numberyellow:number aspects areare thusthus visuallyvisually similar similar because because they they have have many many visually visually identical identical points points with with the differencethe difference being being a deletion a deletion at the at bottomthe bottom (Figure (Figure8). 8).

Figure 8. Signals with “yellow:number” and “yellow” aspects are visually similar. Figure 8. Signals with “yellow:number” and “yellow” aspects are visually similar.

Speed restriction and entrance at yellow can also occur at the same location (Figure9). FigureSpeed 8. restrictionSignals with and “yellow:number” entrance at yellow and “ye canllow” also aspects occur are at visuallythe same similar. location (Figure While the signal aspects can differ (e.g., yellow:8 and yellow:4), the only relevant situations 9). While the signal aspects can differ (e.g., yellow:8 and yellow:4), the only relevant situ- are those where both are the same. ations areSpeed those restriction where both and are entrance the same. at yellow can also occur at the same location (Figure The last hypothesis encompasses the same signal, but with differences in location. As 9).The While last thehypothesis signal aspects encompasses can differ the (e.g.,same ye signal,llow:8 but and with yellow:4), differences the only in location. relevant As situ- mentioned previously, the distances between signals varies during approaches where the ations are those where both are the same. mentionedred aspect previously, is preceded the by distances a yellow between and green signals signal varies (GR–Y–R during approaches). approaches The where track the speed The last hypothesis encompasses the same signal, but with differences in location. As and signal distance determine the amount of deceleration that is needed. We call this “mean mentioned previously, the distances between signals varies during approaches where the deceleration”. The mean deceleration is not a ﬁxed value. The hypothesis is that GR–Y–R approaches with higher mean deceleration values are deviating situations in comparison to GR–Y–R approaches with lower mean deceleration values (See Figure 10). If the schema of

the blue (left) situation is activated during the red (right) situation, insufﬁcient deceleration is used. SafetySafety 2021 2021, ,7 7, ,x x FOR FOR PEER PEER REVIEW REVIEW 99 of of 23 23

redred aspectaspect isis precededpreceded byby aa yellowyellow andand greengreen signalsignal (GR–Y–R(GR–Y–R approaches).approaches). TheThe tracktrack speedspeed and and signal signal distance distance determine determine the the amount amount of of deceleration deceleration that that is is needed. needed. We We call call thisthis “mean “mean deceleration”. deceleration”. The The mean mean decelerati decelerationon is is not not a a fixed fixed value. value. The The hypothesis hypothesis is is thatthat GR–Y–R GR–Y–R approaches approaches with with higher higher mean mean deceleration deceleration values values are are deviating deviating situations situations inin comparison comparison to to GR–Y–R GR–Y–R approaches approaches with with lower lower mean mean deceleration deceleration values values (See (See Figure Figure Safety 2021, 7, 2 9 of 22 10).10). If If the the schema schema of of the the blue blue (left) (left) situatio situationn is is activated activated during during th thee red red (right) (right) situation, situation, insufficientinsufficient deceleration deceleration is is used. used.

Figure 9. The yellow aspect in the deviating approach (bottom) is at the same location as the FigureFigure 9. 9. The The yellow yellow aspect aspect in in the the deviating deviating approach approach (b (bottom)ottom) is is at at the the same same location location as as the the yel- yel- yellow:number aspect during the standard approach because of the entrance at station and/or the up- low:numberlow:number aspect aspect during during the the standard standard approach approach because because of of the the entrance entrance at at station station and/or and/or the the upcomingcomingupcoming switch switch switch (top). (top). (top). During During During both both both approaches, approaches approaches the, ,the cuethe cue cue is visually is is visually visually similar similar similar (yellow (yellow (yellow vs. yellow:number)vs. vs. yel- yel- low:number)andlow:number) the location and and theis the the location location same. is is Note:the the same. same. Both Note: Note: yellow:number Both Both yellow:number yellow:number aspects mustaspects aspects be must themust same be be the the during same same the duringblueduring approaches. the the blue blue approaches. approaches.

Figure 10. During the deviating approach (right), a greater deceleration rate is required than during the standard approach FigureFigure 10. 10. During During the the deviating deviating approach approach (right), (right), a a greater greater decelerati decelerationon rate rate is is required required than than du duringring the the standard standard approach approach (left). During both approaches, the cue is the same (yellow) but the location is different. The two mean deceleration (left).(left). During During both both approaches, approaches, the the cue cue is is the the same same (yellow) (yellow) but but the the location location is is different. different. The The two two mean mean deceleration deceleration values values values (values in bold) are examples. In reality, the mean deceleration value can be any value below the track speed- (values(values in in bold) bold) are are examples. examples. In In realit reality,y, the the mean mean deceleration deceleration value value can can be be any any value value below below the the track track speed-dependent speed-dependent maximums.dependentmaximums. maximums.

2. Method 2.2. Method Method 2.1. The Braking Behavior Measure (Dependent Variable) 2.1.2.1. The The Braking Braking Behavior Behavior Measure Measure (Dependent (Dependent Variable) Variable) The driving behavior is operationalized in one value for each red aspect approach. TheThe driving behaviorbehavior isis operationalized operationalized in in one one value value for for each each red red aspect aspect approach. approach. The ThemeasureThe measure measure is called is is called called the maximum the the maximum maximum deceleration decelerati decelerati toon SPADon to to SPAD SPAD (mDtSPAD). (mDtSPAD). (mDtSPAD). First, theFirst, First, deceleration the the deceler- deceler- to ationation toto SPADSPAD isis calculatedcalculated forfor eacheach locationlocation loglog viavia thethe formulaformula × DtSPADDtSPAD2 == 0.50.5 ×× SPAD is calculated for each location log via the formula DtSPAD = 0.5 speed /distance 𝑠𝑝𝑒𝑒𝑑to𝑠𝑝𝑒𝑒𝑑 red/distance aspect,/distance where to to red red speedaspect, aspect, is where where measured speed speed inis is measured metersmeasured per in in second meters meters per and per second second distance and and in distance distance meters. inThein meters. meters. DtSPAD The The indicates DtSPAD DtSPAD the indicates indicates deceleration the the decelerati decelerati rate theon trainon rate rate needs the the train totrain maintain needs needs to to maintain bemaintain able to to to stop be be exactly at the red signal. The maximum value of these is the mDtSPAD. In Figure 11 the relationship between DtSPAD and actual deceleration is visible. The DtSPAD increases during an approach if the actual deceleration is lower than the DtSPAD value, and the DtSPAD decreases again if the actual deceleration is higher than the DtSPAD value. Safety 2021, 7, x FOR PEER REVIEW 10 of 23

able to stop exactly at the red signal. The maximum value of these is the mDtSPAD. In Figure 11 the relationship between DtSPAD and actual deceleration is visible. The DtSPAD increases during an approach if the actual deceleration is lower than the DtSPAD Safety 2021, 7, 2 10 of 22 value, and the DtSPAD decreases again if the actual deceleration is higher than the DtSPAD value.

FigureFigure 11. 11.TheThe risk risk indicator indicator deceleration deceleration to to signal signal passed passed at at danger danger (DtSPAD), (DtSPAD), clariﬁedclarified using using three three different different cross cross sections sections of theof thesame same red redaspect aspect approach. approach. (top) (top) Risk Risk indicator indicator over over distance distance in meters in meters (m). (m). The TheDtSPAD DtSPAD reaches reaches its maximum its maximum value at 152value m atbefore 152 m the before red the signal. red signal. (middle) (middle) Actual Actual deceleration deceleration in inmeters meters per per squared squared secondsecond (m/s (m/s22)) overover distance. distance. The The DtSPADDtSPAD declines declines once once the the actual actual deceleration is is higher higher than than the the DtSPAD. DtSPAD. (bottom) (bottom) Speed Speed in kilometer in kilometer per hour per (km/h) hour (km/h) over overdistance distance graph. graph.

TheThe train’s train’s speed speed and and position position is is needed needed to calculatethe the mDtSPAD. mDtSPAD. This This information information waswas gathered gathered from from Dutch Dutch Railways Railways (NS) trains thatthathave have Orbit. Orbit. Orbit Orbit is is an an auditory auditory SPAD SPAD warningwarning system. system. For For this systemsystem to to work, work, both bo theth the train’s train’s speed speed and positionand position are registered, are registered,among among other other data. data. This This data data is logged is logged multiple multiple times times per second per second from thefrom moment the moment the thetrain train is withinis within 1000 1000 m of m a of red a aspect.red aspect. Frequent Frequent logging logging (more than(more once than per once second) per madesecond) this data source the most suitable. Automatic signals, which cannot be inﬂuenced by trafﬁc made this data source the most suitable. Automatic signals, which cannot be influenced controllers, are not monitored by the Orbit system due to technical limitations. by traffic controllers, are not monitored by the Orbit system due to technical limitations. In our study we were interested in changes in behavior leading to higher mDtSPAD In our study we were interested in changes in behavior leading to higher mDtSPAD values. There is no absolute criterion for what constitutes a high DtSPAD value. In this values.study, There 0.5 m/s is2 nowas absolute chosen ascriterion a criterion for forwhat two constitutes reasons: a high DtSPAD value. In this study, 0.5 m/s2 was chosen as a criterion for two reasons: 1. In previous initial analyses with similar data, the mDtSPAD followed a roughly normal distribution. The value of 0.5 m/s2 was in the right tail of that distribution.

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2. The Orbit warning system can alter the behavior of the train driver, and thus its mDtSPAD value, if the SPAD alarm sounds. For approaches where the Orbit alarm sounded, the mDtSPAD might have been higher if no warning system had been in place. In previous research it was noted that during most of the relevant approaches the alarm did not sound for DtSPAD values below 0.5 m/s2. Unfortunately, the warning does not sound at a speciﬁc DtSPAD value. The algorithm for the warning system is based on other indicators that are not suitable for the current study. Nineteen months of train data were analyzed, starting from 20 August 2018. On this date, approximately 50% of the trains of the operator NS had been equipped with Orbit (±300 trains). More trains were equipped with Orbit following this date, and their data were included as well. All were passenger trains with a brake power of up to 1.0 to 1.4 m/s2. Train drivers were from the Dutch operator NS. The NS employs over 3000 train drivers and has 28 places of employment where train drivers start and end their shifts [35,36]. The Orbit system employs a quality ﬁlter to the GPS data. The warning system is temporarily shut down when the GPS quality becomes too low. In this study, we only used the data when the warning system was active. We also only included approaches where the time between two logs was always below three seconds.

2.2. Inclusion Criteria Braking behavior was calculated for the approaches falling within the hypothesis criteria and when: • For speed: The track speed was higher than 80 km/h according to permanent traf- ﬁc signs. • For speed: The train did not pass a yellow signal before the red signal approach as part of a previous red aspect approach. Previous yellow aspects would have already resulted in lower train speed. • For speed: The train was driving before passing the yellow signal instead of departing from a station. • For exposure: The red signal remained red until standstill of the train or until the train was within 123 m of the red signal. At 123 m, the train can still have a high value on our risk indicator at a speed of 40 km/h. This is the speed train drivers are instructed to decelerate to after having passed a yellow aspect to be able to stop for the red aspect. • For other factors: The red signal was not at a scheduled stop location. These approaches were excluded because the train driver would need to stop at these locations regardless of the aspect color. • For other factors: The speed at which mDtSPAD was recorded was higher than 10 km/h.

2.3. Measures of Variation (Independent Variables) The two independent variables were the mean deceleration and the frequency of yellow in last 14 days for this train series. The mean deceleration (m/s2) was calculated via 0.5 × track speed (m/s)2/distance between signals (m). The frequency was calculated by counting the number of times the same train series passed the yellow(-number) signal in the last 14 days. Data from the Dutch infrastructure manager ProRail were used to calculate the frequency so that all train approaches could be used, not just those of trains with Orbit.

2.4. Tests Overview An approach can be inﬂuenced by different effects. To deal with this overlap, the following tests were performed: • To test the mean deceleration effect, approaches were selected where only the mean deceleration was a factor (exclusion of yellow entrance or yellow speed restriction; n = 3478 red aspect approaches). Safety 2021, 7, 2 12 of 22

• To test the yellow:number entrance effect, locations with speed restrictions were included if these speed restrictions had the same signal aspect. Three types of tests were done. The first used all the approaches (n = 3429 red aspect approaches). The second used approaches within a specific mean deceleration range (n = 2021 red aspect approaches for a high mean deceleration range and n = 1287 for a low mean deceleration range). The third used approaches towards one specific signal. Only one signal was eligible as it had a sufficiently large number of approaches across different frequencies of entrance at yellow–x (n = 415 red aspect approaches). • To test the speed restriction effect, approaches were selected where there were speed restrictions via yellow–x and a specific mean deceleration range. Locations with entrance at yellow were excluded (n = 509 red aspect approaches). • To test the y–y–red effect, all y–y–red locations were included where there was no yellow–x speed restriction or yellow–x station entrance (n = 20 red aspect approaches).

2.5. Statistical Analysis To test the relation between the binary dependent variable and the (ratio) independent variables, a logistic regression analysis was considered. However, the assumption of linear- ity of independent variables and log odds was violated. Since there was no continuously increasing effect and we wanted to understand the actual shape of the relation, we considered an alternative analysis. In piecewise regression, more than one line is fitted to the data. Multiple points in the independent variable can be chosen to split the data. These points of separation are called knots. Choosing the number of knots and their location is however very difficult. To refrain from using subjective input we decided to split the data evenly five ways. The first split was in half. The second split was in three segments, the third in four segments, the fourth in five segments, and the fifth in six segments. The different splits lead to differences in under- and overfitting and in sample size per segment. Most importantly, insight is provided on the shape of the curve, which can be difficult with a binary dependent variable. The effect of knot selection is also shown. If the pattern remains the same across splits this is evidence for an effect. The p-value was calculated per segment by comparing the observed number of high values with the number of high values that is expected for the segment under the H0 assumption that there was no difference between segments. The analyses were run in R, version 3.6.2. No additional packages were used for the analyses. The R Code is provided in AppendixA. The steps are clarified with an example in Figure 12. In the Results section the exact p-values were recorded when they were below 0.05, and were listed as p < 0.001 when they were below 0.001. p-values above 0.05 were recorded as non-significant (N.S.).

2.6. Signal Effects It is possible that there are signals which have many approaches with high values. If the results are fully attributable to one or a few signals, the results are less likely to be caused by the investigated variable. To check whether the results were not fully attributable to one or a few signals, signals with more than three high values were identiﬁed. These signals are listed in the tables in the Results section and have been used to interpret the results. Safety 2021, 77, x FOR PEER REVIEW 13 of 23 2021, , 2 13 of 22

Figure 12. The p-value is calculated per segment by comparing the observed number of high values with the number Figure 12. The p-value is calculated per segment by comparing the observed number of high values with the number expected if the high values are distributed evenly. expected if the high values are distributed evenly.

2.6.3. Results Signal Effects 3.1. MeanIt is possible Deceleration that Effectthere are signals which have many approaches with high values. If the resultsIf incidental are fully learning attributable occurs, to we one expect or a afew correlation signals, betweenthe results the are percentage less likely of to high be causedvalues andby the the investigated mean deceleration, variable. with To highercheck whether percentages the results for higher were mean not fully decelerations. attribut- ableFigure to 13one shows or a thefew results signals, with signals the mean with rate more on than the x -axis.three Significanthigh values results were were identified. found Thesefor four signals out of are five listed splits in (i.e., the thetables rows in the in the Results figure). section In general, and have the been expected used patternto interpret was theseen, results. with high percentages for higher mean decelerations (See Figure 13). Can the effect be caused by an alternative explanation of signal effects? 3. ResultsSignal B is almost solely responsible for the significant cells on the far right, annotated with3.1. Mean an asterisk, deceleration contributing Effect four out of five high values. This signal had a track speed of 160 km/h. Separate inspection of approaches with track speed of 160 km/h showed If incidental learning occurs, we expect a correlation between the percentage of high that the percentage around a mean deceleration value of 0.6 m/s2 seemed lower than values and the mean deceleration, with higher percentages for higher mean decelerations. those for approaches with track speeds below 160 km/h and a mean deceleration value Figure 13 shows the results with the mean rate on the x-axis. Significant results were found around 0.6 m/s2. It might be the case that approaches at a track speed of 160 km/h are for four out of five splits (i.e., the rows in the figure). In general, the expected pattern was experienced differently. Potentially, (a) this highest mean deceleration segment in fact seen, with high percentages for higher mean decelerations (See Figure 13). shows less behavior change and the effect seen is all due to Signal B with other unknown factors; or (b) there is learning within 160 km/h where the mean deceleration value of 0.6 m/s2 is experienced as “much space” and only 0.7 m/s2 as a “short” distance; or (c) the speed difference is attributable to chance and the effect on the outermost right cells is caused by the mean deceleration effect and not by a signal effect.

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The high percentage for the segment between 0.53 and 0.63 m/s2 is not attributable to Safety 2021, 7, x FOR PEER REVIEW speciﬁc signal effects, since there are 50 high values and only 13 of these are caused by14 of two 23

signals that have high percentages.

Figure 13. MeanMean deceleration deceleration effect analysis. This This figure figure shows the re resultssults when the mean deceleration is split five five different ways. The percentages referrefer toto thethe percentagepercentage of of high high values values in in a a segment. segment. The The numbers numbers directly directly below below indicate indicate the the number num- berof high of high values values and and the total.the total. Orange Orange indicates indicates that thethat percentage the percentage is significantly is significan highertly higher than expectedthan expected and blue andindicates blue indi- a cates a percentage significantly lower than expected. White color indicates a non-significant result. The mean deceleration percentage significantly lower than expected. White color indicates a non-significant result. The mean deceleration value value ranged from 0.22 to 0.83 m/s2. The mean percentage was 2.1%. ranged from 0.22 to 0.83 m/s2. The mean percentage was 2.1%. Can the effect be caused by an alternative explanation of signal effects? 3.2. Yellow(-Number)Signal B is almost Entrance solely responsible at Station Effect for the significant cells on the far right, annotated with3.2.1. an Yellow asterisk, Entrance contributing at Station four Effect: out of Analysis five high 1 values. This signal had a track speed of 160The km/h. effect Separate of yellow inspection entrance of approaches is expected with to be track higher speed when of 160 the km/h frequency showed of that yel- 2 thelow:number percentage at thataround location a mean has deceleration been higher value in the of last 0.6 14 m/s days, seemed and thereby lower than more those learning for approachescan have occurred. with track Figure speeds 14 shows below that 160 thekm/h significant and a mean results deceleration follow the value expected around pattern 0.6 2 m/sof increasing. It might percentages. be the case Thethat non-significantapproaches at outera track right speed percentages of 160 km/h are howeverare experienced surpris- differently.ing. Since the Potentially, total number (a) ofthis approaches highest mean is almost deceleration 500 for the segment outer rightin fact cell shows in the less second be- haviorsplit from change the top,and thisthe effect percentage seen is is all most due likelyto Signal non-significant B with other becauseunknown it isfactors; close toor the(b) theremean is and learning is not duewithin low 160 power. km/h The where effect the on mean behavior deceleration thus seems value to taperof 0.6 off,m/s rather2 is experi- than encedshowing as “much the expected space” continuous and only 0.7 increase. m/s2 as a “short” distance; or (c) the speed difference is attributableCan the effect to chance be caused and the by effect an alternative on the outermost explanation right of signalcells is effects?caused by the mean decelerationA surprisingly effect and high not percentage by a signal of effect. 22.9% was found for Signal D. Upon inspection by randomlyThe high sampling percentage some for approaches, the segment it wasbetween noted 0.53 that and the 0.63 preceding m/s2 is yellownot attributable signal was to specificoften yellow:8 signal aseffects, part ofsince a yellow:8–yellow–red there are 50 high values sequence. and This only red 13 signalof these was are however caused notby twoat a stationsignals stop,that have which high iswhy percentages. these approaches were not added in the calculation of the frequency. Despite the presence of this signal in the outer left segments, these segments are 3.2.still Yellow(-Number) significant on the Entr lowerance end. at Station The possible Effect signal effect of Signal D therefore does not 3.2.1.affect Yellow the interpretation Entrance at of Station the pattern. Effect: Analysis 1 The effect of yellow entrance is expected to be higher when the frequency of yellow:number at that location has been higher in the last 14 days, and thereby more learning can have occurred. Figure 14 shows that the significant results follow the expected pattern

Safety 2021, 7, x FOR PEER REVIEW 15 of 23 Safety 2021, 7, 2 15 of 22

of increasing percentages. The non-significant outer right percentages are however surprising.The Since other the signals total havenumber a wide of approaches range in frequency is almost which 500 for would the outer cause right any cell potential in the signalsecond effect split from to be the spread top, this out. percentage The signal is percentagesmost likely non-significant were not higher because than theit is high-close estto the signiﬁcant mean and cell is percentages, not due low making power. itThe unlikely effect thaton behavior the pattern thus was seems fully to caused taper off, by signalrather effects.than showing the expected continuous increase.

FigureFigure 14.14. EntranceEntrance atat yellowyellow effecteffect (analysis(analysis 1).1). This figurefigure shows the results when thethe frequencyfrequency in thethe lastlast 1414 daysdays isis splitsplit fivefive differentdifferent ways.ways. TheThe percentagespercentages reflectreflect thethe percentagepercentage ofof highhigh valuesvalues inin thethe segment.segment. TheThe numbers directlydirectly belowbelow indicate the number of high values and the total. Orange indicates that the percentage is significantly higher than expected indicate the number of high values and the total. Orange indicates that the percentage is significantly higher than expected and blue indicates a percentage significantly lower than expected. White color indicates no significance. The frequency and blue indicates a percentage significantly lower than expected. White color indicates no significance. The frequency ranged from 0 to 525. The mean percentage was 4.7%. ranged from 0 to 525. The mean percentage was 4.7%. Can the effect be caused by an alternative explanation of signal effects? 3.2.2.A Yellow surprisingly Entrance high at Stationpercentage Effect: of 22.9% Analysis was 2 found for Signal D. Upon inspection by randomlyThe previous sampling analysis some approaches, contained approaches it was noted with that different the preceding mean decelerations.yellow signal was We knowoften yellow:8 there is as asignificant part of a yellow:8 effect of–yellow mean–red deceleration. sequence. This Therefore, red signal the testwas was however repeated not usingat a station only approachesstop, which in is thewhy mean these deceleration approaches range were ofnot 0.5–0.6 added m/s in the2. This calculation segment of was the significantfrequency. inDespite the mean the presence deceleration of this analysis signal (andin the not outer potentially left segments, explained these by segments a signal effectare still like significant the mean decelerationon the lower ranges end. The above possible 0.6 m/s signal2). The effect mean of decelerationSignal D therefore of 0.6 m/s does2 wasnot affect the highest the interpretation mean deceleration of the pattern. value permitted for track speeds up to 140 km/h in the absenceThe of other an inclining signals have slope. a wideSignal range D was in freq removeduency fromwhich this would subset cause because any potential there seemed signal toeffect be ato frequent be spread yellow:8 out. The aspectsignal percentages at that specific were location not higher which than was the highest not measured significant in ourcell currentpercentages, method making for frequency it unlikely calculationthat the pattern (see Sectionwas fully 3.2.1 caused). by signal effects. Figure 15 shows that the significant results still followed the expected pattern of increasing3.2.2. Yellow percentages. Entrance at There Station are Effect: in fact Analysis more significant 2 values, despite a smaller number of approaches.The previous The analysis low percentages contained on approaches the outer right with are different surprising. mean The decelerations. pattern remains We ofknow an effect there thatis a taperssignificant off or effect even hasof mean an inverted deceleration. u-shape. Therefore, the test was repeated using only approaches in the mean deceleration range of 0.5–0.6 m/s2. This segment was

Safety 2021, 7, x FOR PEER REVIEW 16 of 23

significant in the mean deceleration analysis (and not potentially explained by a signal effect like the mean deceleration ranges above 0.6 m/s2). The mean deceleration of 0.6 m/s2 was the highest mean deceleration value permitted for track speeds up to 140 km/h in the absence of an inclining slope. Signal D was removed from this subset because there seemed to be a frequent yellow:8 aspect at that specific location which was not measured in our current method for frequency calculation (see Section 3.2.1). Figure 15 shows that the significant results still followed the expected pattern of increasing percentages. There are in fact more significant values, despite a smaller number Safety 2021, 7, 2 16 of 22 of approaches. The low percentages on the outer right are surprising. The pattern remains of an effect that tapers off or even has an inverted u-shape.

Figure 15. Entrance at yellow effect (analysis 2a). This figure figure shows the results when the frequency in the last 14 days was split in fivefive differentdifferent waysways forfor thethe subset:subset: meanmean decelerationdeceleration valuevalue 0.5–0.60.5–0.6 m/sm/s22, without signal SIGNALSIGNAL D. PercentagesPercentages reflectreflect the percentage ofof high values in this segment. The numb numbersers below indicate the number of high values values and and the the total. total. Orange indicates that the percentage is significantlysignificantly higher than expected and blue indicates a percentage significantlysignificantly lower than expected. White color indicates no significance. The frequency ranged from 0 to 525. The mean percentage was lower than expected. White color indicates no significance. The frequency ranged from 0 to 525. The mean percentage 5.6%. was 5.6%.

The prior analysis was repeated for the subset with mean deceleration smaller than 2 0.5 m/s .2 None. None of of the the splits splits led led to tosignificant signiﬁcant cells. cells. There There were were however however relatively relatively few fewap- proachesapproaches with with a high a high entrance entrance at at yellow yellow freque frequencyncy (See (See Figure Figure 16).16). This This caused caused problems with statistical power, especiallyespecially because the number of approaches was very low in the middle section, section, which which showed showed the the highest highest percen percentagestages in inthe the previous previous analyses. analyses. It is Itun- is knownunknown whether whether there there was was too toolittle little power, power, or wh orether whether the yellow the yellow entrance entrance effect effect was only was presentonly present in combination in combination with witha higher a higher mean mean deceleration. deceleration.

3.2.3. Yellow Entrance at Station Effect: Analysis 3 The final analysis for the entrance at yellow effect contains only data from one signal. Figure 17 shows two significant results in the expected direction. Most approaches were concentrated around the frequency of 300, leading to many cells with relatively few total approaches. Although the number of significant cells is underwhelming, the pattern displayed by the percentages is in line with the previous results. Safety 2021, 7, x FOR PEER REVIEW 17 of 23

Safety 2021, 7, 2 17 of 22 Safety 2021, 7, x FOR PEER REVIEW 17 of 23

Figure 16. Entrance at yellow effect (analysis 2b). This figure shows the results when the frequency in the last 14 days is split five different ways for the subset: mean deceleration value <0.5. Percentages reflect the percentage of high values in this segment. The numbers below indicate the number of high values and the total. Orange indicates when the percentage is significantly higher than expected and blue indicates when a percentage is significantly lower than expected. White color indicates no significance. The frequency ranged from 0 to 504. The mean percentage was 1.5%. There were no signals with over three high values and a percentage above 3.0%.

Figure 16.16. Entrance at yellow3.2.3. effect Yellow (analysis Entrance 2b). This at figurefigureStation showsshow Effect:s the Analysis results when 3 the frequency in the last 14 days is split five different ways for the subset: mean deceleration value <0.5. Percentages reflect the percentage of high values in split five different ways for the subset:The final mean analysis deceleration for the value entrance <0.5. Percentages at yellow effect reflect contains the percentage only data of high from values one insignal. thisthis segment.segment. The numbers below indicate thethe number ofof high values and the total. Orange indicates when the percentage is significantly higher than Figureexpected 17 and shows blue two indicates significant when aresults percentage in the is expectedsignificantly direction. lower than Most expected. approaches White were is significantly higher than expected and blue indicates when a percentage is significantly lower than expected. White color color indicates no significance.concentrated The frequency around ranged the from frequency 0 to 504. Theof 300, mean leading percentage to many was 1.5%. cells Therewith wererelatively no signals few total indicates no significance. The frequency ranged from 0 to 504. The mean percentage was 1.5%. There were no signals with with over three high values approaches.and a percentage Although above 3.0%. the number of significant cells is underwhelming, the pattern dis- over three high values and aplayed percentage by the above percentages 3.0%. is in line with the previous results. 3.2.3. Yellow Entrance at Station Effect: Analysis 3 The final analysis for the entrance at yellow effect contains only data from one signal. Figure 17 shows two significant results in the expected direction. Most approaches were concentrated around the frequency of 300, leading to many cells with relatively few total approaches. Although the number of significant cells is underwhelming, the pattern displayed by the percentages is in line with the previous results.

Figure 17. Entrance at yellow effect (analysis 3). This figure shows the results when the frequency in the last 14 days was split in five different ways for the subset: one signal with mean deceleration value of 0.54 m/s2. Percentages reflect the

percentage of high values in this segment. The numbers below indicate the number of high values and the total. Orange indicates that the percentage is significantly higher than expected and blue indicates a percentage significantly lower than expected. White color indicates no significance. The frequency ranged from 0 to 473. The mean percentage was 9.6%.

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3.3. Speed Restriction Effect Incidental learning was expected to inﬂuence driving behavior in locations where the signal aspect frequently was yellow–number due to speed restrictions. There were 509 approaches at locations with speed restrictions that were not at a yellow station entrance location. Unfortunately, 479 of those had a speed restriction frequency of 0 in the last 14 days. The remaining 30 approaches had a frequency between 1 and 15. There was thus insufﬁcient data to examine this effect separately.

3.4. Yellow–Yellow–Red Effect There were only 20 approaches that fell within the selection criteria. Many more approaches would have been present if approaches had included where the red aspect was at the station stop during the “deviating approach”. Unfortunately, looking at these planned stops creates many methodological issues, including the inﬂuence of the distance between the red signal and the stopping location.

4. Discussion Can incidental learning contribute to SPAD incidents? In this study we took a step towards answering that question by first checking whether there was evidence of a change in behavior as a result of incidental learning. Significant results were found in the expected direction. Other factors can however also influence the results, like signal effects. Decelera- tion behavior can be different for certain signals, for example because signal approaches differ in track speed, signal distance, and (early) signal visibility. The “entrance at yellow” effect was however also seen within one specific signal. That result cannot be influenced by any signal effects. The same result pattern was seen during the other “entrance at yellow” tests. The effect was therefore not only present for the one signal. Unfortunately, there was insufficient data to test whether the effect was also present for signals with a lower mean rate. It is therefore not yet known whether the “entrance at yellow” effect is always present, or only for those approaches with a higher mean rate. It is possible that the approaches with a lower mean rate provide more time for the driver to correct his or her deceleration behavior before it shows up in our behavior measure. In theory, low mean rates might “buffer” against problematic situations. In the Netherlands, the trains are forced to decelerate at a minimal deceleration rate after passing the yellow signal. For approaches with low mean rates in particular this brings the speed down significantly. The shape of the effect was not entirely as expected for the entrance at yellow effect. The effect seemed to taper off as the entrance at yellow frequency reached very high values. Given the high frequencies, these were approaches where the train series had entrance at yellow almost every time. A potential explanation is that the extreme familiarity with the situation leads to a heightened awareness when something is different. This is comparable to coming to a friend’s house occasionally and going there nearly every day. When visiting occasionally one will recognize the picture on their living room wall. One might not notice when they change the picture to a comparable one. However, when the individual visits nearly every day he/she is more likely to notice that they changed the picture despite minimal changes. It is of course also possible that there is a hidden factor that happens to be more present for those entrances with the highest frequency of entrance at yellow. This is unlikely, because a similar pattern was seen when looking within one signal, but the possibility cannot be excluded. Further research is needed to see whether the pattern is indeed caused by this psychological effect or whether it was an artefact of our data. During the “entrance at yellow” effect, incidental learning occurred because the approach was in the same location and with a similar cue (e.g., yellow:4 and yellow). We also obtained evidence of a mean deceleration effect. In these situations, the location is different, but the cue is identical (yellow). The pattern for mean rate was as expected, with higher mean rates leading to higher percentages. However, the high percentages at the Safety 2021, 7, 2 19 of 22

highest mean rates were caused by one signal and could thus be the result of a signal effect. Even if this is the case, the pattern remains for the low to medium–high mean rates. It would be jumping to conclusions to say that this pattern was deﬁnitely caused by incidental learning. It could be a conscious choice to always decelerate at for example 0.4 m/s2, which would lead to a mDtSPAD above 0.5 m/s2 for approaches with a mean rate above 0.4 m/s2 and to low mDtSPAD values for approaches with a mean rate below 0.4 m/s2.

4.1. Limitations and Future Research In future research, additional factors could be included. One identified factor was the presence of a frequent yellow:number aspect caused by a red signal that was not at a station stop. While the timetable is designed to avoid this kind of approach frequently in the same place, it is of course possible for this to occur. Additional involved factors could be line of sight, with early visibility as a protective factor. An extra finding was the identification of signals with high percentages. It is clear that there are behavior-influencing factors that are currently out of scope and unknown. Whilst they did not interfere with the conclusions of this research, it would be an interesting avenue to discover what causes these differences between signals. A limitation of our research was that the exposure frequency was calculated by train series and not by train driver. Since learning takes place in the mind of an individual, it would have been preferable to measure how often the train driver had previously experienced similar situations. Information about the train driver was not disclosed for privacy reasons. The same train series was considered the next best alternative under the assumption that a train driver often drives the same train series. Another possibility was to simply calculate how often any train was exposed to yellow aspects at the relevant location. We however assumed that train drivers link their experiences with the infrastructure to the train series they are in, since their driving experience is influenced by the present train series. A train driver might for example drive from Utrecht to Amsterdam, as many trains do, but the train series he is in determines which stations he has to stop at, what his timetable looks like, and the continuation of his journey. Fortunately, our research focuses on relative changes. When a train series has an entrance at yellow frequency of 200 over the past 14 days, the specific train driver probably does not experience a yellow entrance in that location all 200 times. However, the train driver is likely to have experienced a greater number of entrances in yellow than in those cases where the frequency was only 100. Nonetheless, the research would be improved by replication using driver data. This would also give more insight into how often an employee actually needs to be exposed to a certain situation for incidental learning to occur. Another related avenue for future research could be individual differences in incidental learning.

4.2. Answering the Question and Using the Answer Our results indicate changes in train driver behavior when employees have previously been exposed to different behavior requirements in the same location with a similar yellow signal. The results are in line with our expectations of incidental learning. Using actual data, we identiﬁed a shift in braking behavior in the direction of a lower safety margin. We thus found evidence for the notion that incidental learning impacts employee behavior and thereby safety margins. It is possible that the effects of incidental learning results in SPADs in certain situations. Further research can test whether the effects of incidental learning are indeed also visible using data of actual SPADs. A commonly known disadvantage of using incident data for quantitative analysis is that there is usually a small amount of data since there are relatively few (large) incidents. This is especially the case in the Netherlands when looking at nuanced causes. There are for example multiple SPADs with aspect sequence green– yellow–red, but fewer with that speciﬁc aspect sequence and entrance at yellow. There are even fewer incidents within that segment with various frequencies of entrance at yellow. Safety 2021, 7, x FOR PEER REVIEW 20 of 23

It is possible that the effects of incidental learning results in SPADs in certain situations. Further research can test whether the effects of incidental learning are indeed also visible using data of actual SPADs. A commonly known disadvantage of using incident data for quantitative analysis is that there is usually a small amount of data since there are relatively few (large) incidents. This is especially the case in the Netherlands when looking Safety 2021 7 , , 2 at nuanced causes. There are for example multiple SPADs with aspect sequence green20 of 22– yellow–red, but fewer with that specific aspect sequence and entrance at yellow. There are even fewer incidents within that segment with various frequencies of entrance at yellow. The results of this study, based on data of allall red aspect approaches, can be used to focus an analysis with incident data. The results of this study can also be used as an input for decision-making on desired interventions.interventions. It It is is clear clear that that crude crude measures, measures, such such as as no no longer using a specificspecific signalsignal aspect, are not necessary to eliminate certain behaviors or increase safety margins. We see thatthat specific specific effects effects add add up up to to create create the the locations locations with with the the highest highest percentages. percentages. Figure Figure 18 gives18 gives a simplified a simplified overview overview of ofhow how one one signal signal approach approach can can lead lead to to different different behavior depending on the mean deceleration, deceleration, entrance at yellow yellow frequency, frequency, and presence of speed restriction.

FigureFigure 18. 18. AA simplified simpliﬁed illustration illustration that that different factors need to be considered to predict differences in behavior.

InIn a a general general sense, sense, organizati organizationsons can can reevaluate reevaluate their their task task designs designs by taking by taking the pres- the encepresence of incidental of incidental learning learning into intoaccount. account. Organizations Organizations often often focus focus on making on making sure surethat thethat task the design task design for a forspecific a speciﬁc task helps task helps the employee the employee to perform to perform the task the successfully. task successfully. This isThis important is important but does but doesnot address not address the whole the whole story. story. To further To further improve improve task taskdesign, design, one shouldone should not notonly only consider consider what what the the employee employee is isexposed exposed to to during during the the execution execution of of the specificspeciﬁc task, but also what he or she has been exposed to during other moments of his shift. Yesterday Yesterday matters, especially if it is visually similar.

Author Contributions: conceptualization,Conceptualization, J.B. and J.G.; methodology,methodology, J.B.,J.B. J.G.J.G. andand P.v.G.;P.v.G.; validation, validation,J.G.; formalJ.G.; formal analysis, analysis, J.B.; investigation,J.B.; investigation, J.B.; dataJ.B.; data curation, curation, J.B.; writing—originalJ.B.; writing—original draft draft preparation, preparation,J.B.; writing—review J.B.; writing—review and editing, and editing, J.G., P.v.G. J.G., and P.v.G. S.S.; and visualization, S.S.; visualization, J.B.; supervision, J.B.; supervision, P.v.G. and S.S.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by ProRail, the Dutch rail infrastructure manager. Institutional Review Board Statement: Ethical review and approval were waived for this study, because (non-medical) retrospective data was used which was anonymous. Informed Consent Statement: Anonymous, retrospective data was used. Use of the data for safety related analyses was permitted by the Dutch Railways, employer of the train drivers. Safety 2021, 7, 2 21 of 22

Data Availability Statement: Restrictions apply to the availability of these data. Data was obtained from Dutch Railways and ProRail and are available from the authors with the permission of Dutch Railways and ProRail. Acknowledgments: We thank Jelle van Luipen (the innovation manager at ProRail), and Wilco Tielman (data scientist at ProRail), for arranging the research infrastructure and for the data curation, respectively, and both for their emotional and intellectual support. Conﬂicts of Interest: The authors declare no conﬂict of interest. The authors alone are responsible for the content and writing of this article. The funder provided access to the data. The funder had no role in the design of the study; in the analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A R code for statistical test: ## Statistical testing: get p-value for 1 segment by comparing observed with expected # example values in total sample high<-1+39 tot<-38+377 low<-tot-high values<-c(rep(1,high), rep(0,low)) # example values in subset high_subset<-1 n_subset<-38 # Prepare for 100000 runs reps<-100000 result<-logical(length=reps) # Check side: greater or lesser than; if greater than expected: if(high_subset>(high/tot*n_subset)){ for(i in 1:reps){ # draw random sample of subset size without replacement and check if as many or more values in drawn as in measured result[i]<-sum(sample(values, n_subset, replace = F)==1)>=high_subset } } # Check side: greater or lesser than: if lesser than expected: if(high_subset<(high/tot*n_subset)){ for(i in 1:reps){ # draw random sample of subset size without replacement and check if as little or less values in drawn as in measured result[i]<-sum(sample(values, n_subset, replace = F)==1)<=high_subset } } p<-sum(result)/reps p

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