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The effect of dangerous goods transporters on hazard perception and evacuation behavior – A virtual reality experiment on tunnel emergencies
Fire Safety Journal 78 (2015) 24–30
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The effect of dangerous goods transporters on hazard perception and evacuation behavior – A virtual reality experiment on tunnel emergencies Max Kinateder a,c,n, Daniel Gromer a, Philipp Gast a, Susanne Buld a, Mathias Müller a, Michael Jost a, Markus Nehfischer a, Andreas Mühlberger b, Paul Pauli a a b c
University of Würzburg, Department of Psychology I, Marcusstr. 9 11, D 97070 Würzburg, Germany University of Regensburg, Department of Psychology, Clinical Psychology, and Psychotherapy, Universitätsstr. 31, D-93053 Regensburg, Germany Brown University, Department of Cognitive, Linguistic, and Psychological Sciences, Providence, RI 02912, USA
art ic l e i nf o
a b s t r a c t
Article history: Received 19 September 2014 Received in revised form 15 June 2015 Accepted 30 July 2015
Introduction: Evacuation from tunnel fire emergencies may become problematic if tunnel occupants do not engage quickly enough into evacuation. Dangerous goods transporters increase the risks tunnel occupants are exposed to. The aim of the present study is to investigate the effect of an increased risk during a simulated tunnel emergency on participants' subjective hazard perception and evacuation behavior. Methods: Using a five sided CAVE system, two experimental groups were immersed into a virtual road tunnel fire emergency. In the dangerous goods condition a burning gasoline transporter was visible. In the control condition a burning heavy goods vehicle was visible. Hazard perception, pre-movement time, movement time and exit choices were analyzed. Results: In the dangerous goods condition the situation was rated significantly more dangerous than in the control condition. In both conditions participants showed appropriate behavioral reactions and either moved to an emergency exit or to an emergency phone. Discussion: In combination with the high threat ratings the results indicate good situation awareness of the participants, and that emergency signage in the given visibility conditions was effective in guiding participants towards an exit or to an emergency phone. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Virtual reality Evacuation Tunnel emergencies Dangerous goods Risk perception
1. Introduction Dangerous goods are commonly transported on European roads [1]. Inflammable liquids are the most common of these and bear special risks [2]. The confined space in road tunnels and petrol′s highly inflammable properties have contributed to severe fire emergencies in road tunnels in the past, in which extreme heat, smoke, and pressure waves in combination with not enough time to evacuate threatened the health and life of tunnel occupants. An extreme example was the collision of two flammable methanol transporters, which caused a fire in a road tunnel in northwestern China in 2014. Thirty-one tunnel occupants lost their lives during this incident, demonstrating the potential disastrous consequences of tunnel fires [3]. In order to minimize the risk of tunnel emergencies related to petrol and other dangerous n Corresponding author at: Brown University, Department of Cognitive, Linguistic, and Psychological Sciences, Providence, RI 02912, USA E-mail addresses: [email protected], [email protected] (M. Kinateder).
http://dx.doi.org/10.1016/j.firesaf.2015.07.002 0379-7112/& 2015 Elsevier Ltd. All rights reserved.
goods, the European Union defined safety standards for general tunnel safety and transportation of dangerous goods through road tunnels [4,5]. Given these risks, it is not surprising that safety measures for truck drivers and especially dangerous goods drivers have been studied extensively [6,7–11]. However, little attention has been directed to the effect of dangerous goods transporters on other tunnel occupants, whose life and health is at great risk in tunnel fire emergencies. Tunnel occupants do not always show the adequate evacuation responses when confronted with conflicting information [12–16]. Especially in severe tunnel fire emergencies, the physical environment may become very difficult to interpret [17], and certain behavioral patterns arise. For instance, occupants have been observed to be affiliated to familiar places (e.g., the entrance portal of a tunnel) and may neglect faster and safer egress routes [18]. Dangerous goods vehicles are labeled with orange plates which indicate the content of the transport [5]. In addition, the heavy goods vehicles carrying dangerous goods may have distinctive, easily recognizable shapes. However, such shapes may be confused
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by occupants with transporters of other liquids (e.g., a milk transporter may be mistaken for petrol transporter and vice versa). The question is whether or not tunnel occupants recognize a dangerous goods transporter in a tunnel fire, associate it with an increased risk, and adapt their behavior to the increased risk. So far, research on tunnel evacuation has not addressed subjective risk perception of tunnel users. Although, ‘panic’ during most evacuation scenarios has been identified as a myth [19], findings from the evacuation of the World Trade Center 2 on September 11, 2001, showed that an increase in perceived risk led to lower evacuation delays and faster total evacuation times [20– 23]. Risk perception and evacuation during the attacks on the World Trade Center (WTC) on September 11, 2001 have been analyzed in several independent studies. One study found that higher perceived risk was correlated with shorter evacuation [23]. In another study, however, higher risk perception was correlated with more information seeking, more pre-evacuation actions, and consequently longer evacuation delays [24]. Differences between these studies may be explained by differences in the samples and operationalization of perceived risk (see [25, 26] for a more detailed discussion). Delays in evacuation may increase the risk for occupants not reaching an emergency exit in time. In fact, inappropriate occupant behavior has been documented during several tunnel emergencies. For example, 27 of the 39 victims of the Mont-Blanc tunnel fire in 1999 had stayed in their vehicles and did not try to evacuate [27,28]. A potential explanation for this is that tunnel occupants overestimate the safety of their own vehicle and underestimate the risks of a situation. Although a recent review revealed that the role of risk perception is still not completely understood [25,26], we hypothesize that an increase in perceived threat of a situation is associated with pre-movement and movement times during a simulated road tunnel fire. Therefore, the goal of the present study was to systematically manipulate the perceived threat in a tunnel emergency by comparing a tunnel fire including a dangerous goods transporter with a tunnel fire without a dangerous goods transporter. To this end, a virtual reality (VR) experiment was developed in which two groups of participants were confronted with a tunnel fire.
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with a cylindrical container labeled with the hazard identification number for inflammable liquids, Fig. 1). 2.2. Dependent variables Participants' movement behavior during the emergency situation was recorded in space over time. In particular, participants′ destination choice (where a participant moved), movement trajectories (length of trajectories and movement patterns), premovement (the time participants waited after trial start until they started to move) and movement times were analyzed. Movement time was defined as the time participant moved through the tunnel until a trial ended (total trial time – pre-movement time) and two sub-parameters were defined. Movement times to reach the emergency exit or phone refer to the time participants needed to reach the emergency exit or phone after they started moving (if they went there). Immediately after the experiment, participants rated their perceived threat on a scale from 0 [no danger at all] to 100 [extremely dangerous] and completed a set of questionnaires (Table 1). Since anxiety can affect evacuation behavior, state, trait, (STAI; [29,30]) and tunnel anxiety (TAQ; [31]) were assessed as control variables after the experiment. In addition state anxiety (STAI-state) was also measured before the experiment. Finally, presence (the experience of being immersed into the VR) and simulator sickness (symptoms of nausea and dizziness associated with being immersed into a VR) were measured using the Igroup Presence Questionnaire (IPQ; [32]) and the Simulator Sickness Questionnaire (SSQ; [33]). In addition, participants rated their expertize in gamepad use and 3D gaming experience on a 5 point Likert scale after the experiment. 2.3. Sample
2. Material and methods
Forty-two participants were recruited for the experiment, of which two did not finish the experiment due to technical reasons. Forty participants (21 female, mean age¼25.40 years, sd¼4.78 years) remained in the study and were randomly assigned into the two experimental conditions (dangerous goods and a control condition). Table 2 gives an overview of the socio-demographic and questionnaire data.
2.1. Design and independent variables
2.4. Apparatus
A between subject design was used in the present study. All participants were confronted with a simulated tunnel emergency in a road tunnel. The control group was situated on foot near a burning heavy goods vehicle, whereas the dangerous goods group saw a burning dangerous goods transporter (petrol transporter
The 3D-multisensory CAVE laboratory at the Department of Psychology I of the University of Würzburg, Germany was used for the present study. The virtual scenario was rendered by a Source Engine Modification (VrSessionMod 0.5) of the multiplayer version of the first-person game Half-Life 2 (Valve, Bellevue, Washington,
Fig. 1. Screenshot of the virtual tunnel emergency situation in the dangerous goods condition (left) and the control condition with a regular heavy goods vehicle (right). The emergency situation was exactly the same in both conditions. Brightness and contrast of the screenshots were increased in this figure by 40% for improved illustration. Screenshots were taken at slightly different timings. Flame and smoke development were exactly the same in the two experimental conditions.
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Table 1 Overview of questionnaires. Questionnaire
Abbreviation
Description
State-Trait Anxiety Inventory Tunnel Anxiety Questionnaire Igroup Presence Questionnaire
STAI TAQ IPQ
Simulator Sickness Questionnaire
SSQ
Self-reported measures of state (currently experienced) and trait anxiety (personality trait) Self-reported measures of anxiety experienced while driving through a tunnel either as a driver or a passenger Self-reported measures of the sense of presence experienced in VR on four sub-scales: (1) Spatial presence: degree of the sense of being physically present in VR (2)Involvement: amount of attention focused on VR and the involvement experienced (3) Experienced Realism: subjective rating of realism of VR (4) General factor (G1) correlating with factors 1–3 Self-reported degree of oculomotor symptoms, disorientation, nausea and overall sickness after experiencing VR
Table 2 Socio-demographic and questionnaire data.
Age STAI trait STAI state before STAI state after TAQ IPQ G1 IPQ spatial presence IPQ involvement IPQ experienced realism SSQ total score
Control group
Dangerous goods
t
p
m
sd
m
sd
25.10 38.90 36.52 36.91 5.50 4.05 3.73 3.65 3.39
4.67 11.40 6.88 7.67 4.37 1.32 0.60 1.30 0.86
25.95 35.05 35.68 36.11 5.26 4.14 4.08 4.15 2.88
4.99 8.51 7.48 8.20 4.19 1.24 0.80 1.03 0.80
0.56 1.20 0.37 0.32 0.17 0.23 1.56 1.38 1.94
.58 .24 .71 .75 .86 .81 .12 .17 .05
7.20
7.00
8.28
9.08
0.43
.67
Note: n(Control group)¼19, n(Dangerous goods)¼21; all df ¼38; STAI¼ State-Trait Anxiety Inventory; TAQ ¼Tunnel Anxiety Questionnaire; IPQ ¼Igroup Presence Questionnaire; SSQ ¼ Simulator Sickness Questionnaire; Sumscores were calculated for each questionnaire.
USA). The VR simulation software CS-Research 5.6 (VTplus, Würzburg, Germany; see www.cybersession.info for detailed information) controlled the experimental procedure and performed the data collection. The virtual scenario was presented in a 5-sided Cave Automatic Virtual Environment (CAVE; I-Space5 by BARCO, Kuurne, Belgium) (size: 4 3 3 m; front-wall resolution: 2016 1486 pixels, floor and rear wall: 1627 1200 pixels, side walls: 1220 1200). The system used 12 channels to generate stereoscopic images with six projectors. Passive interference-filtering-glasses (Infitec Premium, Infitec, Ulm, Germany) were used to separate stereoscopic images for the participants. An active infrared LED system (4 cameras; PhaseSpace Impulse, PhaseSpace Inc., San Leandro, CA, USA) tracked movement and orientation of the participants' glasses and navigation device. Participants could navigate with a joystick on a tracked wireless gamepad at a maximum walking speed of 2.86 m/s. The head and gamepad position were used to adapt the visualization of the 3D images. A 7.1 Surround Sound system presented the audio simulation.
to open doors using the buttons of the navigation gamepad. After the training, the experimenter informed the participants that they would find themselves on foot in a road tunnel and that they should do what they thought was appropriate. Then, the displays in the CAVE faded to black, participants were teleported into the virtual road tunnel, the displays faded back to the tunnel, and the trial began. A virtual evacuation scenario in a two-bore uni-directional highway road tunnel was presented to the participants. The total size of the tunnel cross section was 9.50 m, in accordance with German Standards (RABT, 2006), consisting of two lanes (each 3.75 m) and sidewalks (each 1 m). The visible tunnel safety installations were in line with RABT 2006 standards. Emergency exits led to an emergency crossway for pedestrians, linking the two tunnel bores. The emergency exit was signposted by a standard European back-lit sign (European Commission, 2004) and standard European Emergency signage was available in the tunnel every 25 m (Bundesanstalt für Straßenwesen, 2006). No emergency messages were played back during the scenario. Fig. 1 illustrates the tunnel fire emergency. A burning heavy goods vehicle blocked the road close to the emergency phone. Smoke was rendered in the simulated tunnel in order to produce reduced visibility conditions (visibility of approximately 8–15 m). The smoke moved towards the participant′s starting position. In addition, there were several other vehicles placed on the road. Fig. 2 gives an overview of the scenario. Participants were placed on foot next to a car at the starting position and were oriented in driving direction. Participants could move freely in any direction in the tunnel. Over time, the tunnel filled with smoke. If a participant opened the door of the emergency exit or of a vehicle, used one of the emergency phones, or walked past the vehicles in the tunnel, the experiment ended. Then participants rated the perceived threat of the situation, left the CAVE, and completed the STAI-state, SSQ, and IPQ questionnaires. At the end of the experimental session, the experimenter debriefed, thanked, and reimbursed (course credits or monetary compensation) the participants.
2.5. Procedure The ethics committee of the medical faculty of the University of Würzburg, Germany approved the present study before the data collection. After participants arrived at the laboratory, they consented to take part in a study on tunnel safety and completed a socio-demographic questionnaire, the STAI-state and trait, and the TAQ. Then, the experimenter equipped participants with the interference-filtering-glasses as well as the tracking equipment and explained the handling of the navigation gamepad. After that, participants entered the CAVE. Participants completed a training scenario in which they walked through a virtual maze in order to practice navigation in VR. In the training scenario, participants had
Emergency phone
Starting position
Heavy goods vehicle / Dangerous goods transporter
Car
Emergency Signage
Emergency Exit
10 m
Fig. 2. Schematic overview of the two tunnel bores and the emergency scenario; emergency signage is located every 25 m along the tunnel wall.
M. Kinateder et al. / Fire Safety Journal 78 (2015) 24–30
3. Results 3.1. Perceived threat Fig. 3 shows the threat ratings of the scenario. Participants in the dangerous goods group rated the emergency scenario as significantly more dangerous than the control group, t(38) ¼2.73, p ¼.011.
3.2. Behavioral parameters Table 3 shows the total trial, pre-movement, and movement time. The total length of the participants′ trajectories as well as the mean minimal and maximum distance to the burning heavy goods vehicle/dangerous goods transporter are displayed in the same table. There were no significant differences between the two experimental conditions. One participant from the dangerous goods condition moved past the heavy goods vehicle (Fig. 4). After removing this participant from the analysis, the minimum distance to the burning heavy goods vehicle was significantly lower – albeit marginally – in the control condition, t(38) ¼1.91, p ¼.065, d ¼0.60. All other behavioral parameters remained unaffected. Table 4 shows the participants' destination choices. Twentytwo participants decided to go to the emergency phone close to the starting position and 15 moved to the emergency exit. There were no significant differences between the two experimental groups regarding destination choice, χ²(3) ¼4.76, p ¼.190. In both groups, participants interacted (i.e., pressed a button on the gamepad) with the first safety installation they encountered. If a participant decided to move to the emergency exit, they also opened its door. If a participant approached the emergency phone, they also initiated a distress call.
100
Rating [0−100]
75
50
25
0 Control Group
Dangerous goods
Fig. 3. Boxplot of the threat ratings of the tunnel emergency. Boxplot whiskers indicate the inter-quartile range.
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3.2.1. Movement Trajectories The descriptive movement trajectories (Fig. 4) indicate no significant differences between the two experimental groups. One participant in the dangerous goods condition moved past the emergency phone and approached the burning heavy goods vehicle. Also note that some participants stopped (indicated by more opaque blue spots on the trajectories in Fig. 4), however this did not affect the differences in the movement time calculation. The heat-map analysis (Fig. 5) shows a graphical representation of how the two experimental groups where distributed over the tunnel. A grid of squared cells (side length 1.27 m/50 in.) was laid over the tunnel. Frequencies of participants moving through each cell were calculated, where a participant was only counted once per cell. That way, participants who stayed longer time in a cell would not be weighted more than participants who moved through a cell without waiting. For each cell, the difference in relative frequencies was calculated.
4. Discussion Two experimental groups were confronted with a virtual tunnel fire emergency. In the dangerous goods condition participants saw a burning dangerous goods transporter; in the control condition they saw a burning heavy goods vehicle. Although participants rated the dangerous goods condition as significantly more threatening, participants showed similar patterns of protective action in both experimental groups. Given that the control condition was already rated as highly threatening, it is possible that this situation already was ′dangerous enough′ to trigger protective action so that a higher perceived threat did not have an additional effect. Although participants in the dangerous goods condition moved longer distances and kept greater minimum and maximum distance to the burning dangerous goods transporter than participants in the control group, these descriptive differences were not or only marginally statistical significant after removing outliers from the analysis. These results can be explained by the strong variance in thebehavioral measures. However, given that in a real world tunnel emergency even a small difference in the distance to the nearest place of safety might save the life of a tunnel occupant, future studies should test occupants′ preferred minimum safety distance to fire sources with bigger sample sizes. In the case of tunnel fires, even small effects are worth investigating as they could reflect differences in lives saved or lost. In both experimental groups some participants did not move to an emergency exit or phone but went back to the tunnel entrance portal. The concept of movement to the familiar may explain this behavior [18]. It is possible that the entrance portal was a familiar place for these participants and they decided to neglect closer emergency exits along the tunnel. Occupants′ affiliation with familiar places might be especially problematic in tunnel emergencies when flames and smoke move too fast for occupants to reach the tunnel entrance portal in time. Overall, participants in both groups rated the scenario as very dangerous. This indicates that participants recognized the threat of the simulated tunnel fire as well as the dangerous goods transporter and, therefore, supports the validity of the VR simulation. Furthermore, the dangerous goods scenario was rated as even more dangerous than the control condition. It is interesting to note that one participant in the control group rated the situation as ‘not threatening at all’. There are two potential explanations: first, the participant did not recognize, or underestimated the risks of a tunnel fire. Second, the participant did not experience enough presence and was always completely aware that what he/she saw was a simulation. This is supported by
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Table 3 Behavioral parameters in the two experimental conditions: trial time, pre-movement, and movement times as well as total trajectory length, minimum and maximum distance to the burning heavy goods vehicle. Control group
Trial time [s] Pre-movement time [s] Movement time (total) [s] Movement time to emergency exit [s] Movement time to emergency phone [s] Total trajectory length [m] Minimum distance to burning heavy goods vehicle [m] Maximum distance to burning heavy goods vehicle [m]
Dangerous goods
m
Sd
m
sd
56.23 7.04 49.19 80.64 33.23 87.04 15.65 79.38
36.54 14.12 28.71 14.45 9.02 80.38 6.78 68.27
65.29 5.76 59.53 82.79 31.49 102.18 19.47 86.89
55.38 4.70 52.81 46.94 6.94 99.06 9.14 56.24
the participant′s low self-reported involvement (a subscale of the IPQ) in the virtual environment. Understanding perceived threat during fire emergencies is relevant for researchers and practitioners in fire evacuation research. Since perceived threat is often postulated to influence the point of transition from pre-evacuation to evacuation stage [25,26], an accurate description of the tunnel evacuation process mandates a precise understanding of risk perception. Evacuation models, which aim to predict available and required safe egress time during fires could, for example, benefit from a better understanding of the role of perceived threat and consequently develop more accurate evacuation models. 4.1. Limitations Some limitations of the study need to be addressed. First, participants were alone in the scenario. In most tunnel fires other occupants are present and are likely to influence each other. Reports from tunnel emergencies [34,35] and virtual reality studies have demonstrated that social influence affects tunnel evacuation behavior [36,37]. Second, the present VR scenario consisted of visual and auditory simulation. Tunnel fires, however, are also characterized by smells of smoke and burning materials as well as by the heat and airflow generated by the fire. Future studies should include olfactory simulations and, if possible, heat simulations. However, the high presence ratings (especial the G1 and spatial presence factors of the IPQ) underline the high immersiveness of the VR simulation in the present study. On a more general note, the question of validity, reliability, and objectivity of a research method is an important debate within empirical research on fire evacuation. VR studies aim to create scenarios and problems that are analogous to the real world (not identical). The results of the present study should be interpreted in a relative and not an absolute manner since VR data of, for example, movement
n
t
p
40 40 40 15 22 40 40 40
0.62 0.38 0.78 0.13 0.51 0.91 1.51 0.38
.54 .71 .44 .89 .61 .36 .14 .70
Table 4 Destination choice in the two experimental conditions. Control group Dangerous goods Emergency phone close to starting position Emerncy phone further away from starting position Emergency exit Tunnel entrance portal
12 0
10 1
5 2
10 0
speed may not match the absolute walking movement speeds in a real world tunnel emergency. For a more general discussion of the strengths and weaknesses of VR for fire evacuation research, see [38]. Third, participants were mainly university students, thus other populations are not represented. Future studies should use more heterogeneous samples including, for example, different age groups and experts on tunnel safety (e.g., fire fighters, professional drivers). Age specifically might be an important confounding factor, since tunnel safety has become a mandatory part of Germany's driver's education in 2008. However, adding age as a covariate did not change the results in the present study. Fourth, participants were teleported into the tunnel and were on foot instead of sitting in a car and driving into the tunnel. Using a vehicle has been found to be a common means of evacuation in real tunnel incidents. In many cases this may not be possible since vehicles, debris, or other obstacles may block the lanes. Thus, the goal of the present study was to study protective actions in a walking scenario. However, the nature of the scenario may lead to an overestimation of the absolute frequency emergency exit usage. Fifth, participants in the dangerous goods condition reported the scenario to be less realistic. After removing outliers, however, these differences were no longer statistically significant. Sixth, participants navigated through the virtual environment using a gamepad and did not walk. That is, differences in the pre-movement and movement
Fig. 4. Movement trajectories of the participants in the control group (a) and the dangerous goods group (b). The darker the shade, the more participants or the more frequently a participant passed a coordinate. Single blue spots indicate where a participant paused moving. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 5. Heat-map with group comparisons. Differences between relative frequencies of participants counted in each cell were calculated. Blue color/positive values indicate that more participants in the dangerous goods condition were counted in a cell. Red color/negative values indicate that more participants in the control group were counted in a cell. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
data of the present study could also be the results of differences in the ease with which participants could handle the navigation device. However, participants self-reported expertize in gamepad use and 3D gaming experience was not correlated to the premovement and movement time in the study nor were there any group differences. Further research is necessary to investigate to what degree different navigation methods in virtual environments correspond.
[3] [4]
[5]
[6]
5. Conclusions
[7]
In conclusion, the present study demonstrated an effect of a dangerous goods vehicle on the threat experienced in a tunnel fire but not on protective actions (destination choice, pre-movement and movement time, movement trajectories and movement distance). Participants realized the severity of the situation in both experimental conditions, but rated the dangerous goods transporter as more threatening, indicating that participants had good situation awareness.
[8]
[9]
[10]
[11]
[12]
Funding information The data published here were recorded within the scope of the project “Protection of critical bridges and tunnels” [Schutz kritischer Brücken und Tunnel– SKRIBTPlus, grant number: 13N9636], supported by the German Federal Ministry of Education and Research. The funding source had no involvement in the design and analysis of the study, nor at any other step of the preparation of the manuscript.
[13]
[14]
[15]
[16]
Conflict of interest statement
[17] [18]
Prof. Paul Pauli, Prof. Andreas Mühlberger, and Mathias Müller are shareholders of a commercial company that develops virtual environment research systems for empirical studies in the field of psychology, psychiatry, and psychotherapy. Mathias Müller is executive officer of the same company. No further potential conflicting interests exist.
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[22]
Acknowledgments [23]
The authors would like to thank Jason Roth for carefully proofreading the manuscript and Stefanie Löw for helping with the data collection.
[24]
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survenu le 24 mars 1999 au tunnel routier du Mont Blanc [Task Force For Technical Investigation Of The 24 March 1999 Fire In The Mont-Blanc Vehicular Tunnel]. Retrieved from the Ministère de l′Intérieur & Ministère de l′ Equipement,des Transports et du Logement website: 〈http://www2.equipe ment.gouv.fr/archivesdusite/actu/jcg-1997-2002/dossiers/1999/rapportfr. htm〉, 1999. L. Laux, P. Glanzmann, P. Schaffner, C.D. Spielberger, State-Trait-Angstinventar, Beltz, Weinheim, 1981. C.D. Spielberger, R.L. Gorsuch, R.E. Lushene, Manual for the State-TraitAnxiety Inventory, Consulting Psychologists Press, Palo Alto, CA, 1970. A. Mühlberger, P. Pauli, Tunnelangstfragebogen [Tunnel Anxiety Questionnaire: short-description and questionnaire], In: [Information] L-ZfPIuDZLIfP (Ed.), 2000. T.W. Schubert, The sense of presence in virtual environments: a three-component scale measuring spatial presence, involvement, and realness, Z. Medienpsychologie 15 (2003) 69–71. R.S. Kennedy, N.E. Lane, K.S. Berbaum, M.G. Lilienthal, Simulator Sickness
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Fire Safety Journal journal homepage: www.elsevier.com/locate/firesaf
The effect of dangerous goods transporters on hazard perception and evacuation behavior – A virtual reality experiment on tunnel emergencies Max Kinateder a,c,n, Daniel Gromer a, Philipp Gast a, Susanne Buld a, Mathias Müller a, Michael Jost a, Markus Nehfischer a, Andreas Mühlberger b, Paul Pauli a a b c
University of Würzburg, Department of Psychology I, Marcusstr. 9 11, D 97070 Würzburg, Germany University of Regensburg, Department of Psychology, Clinical Psychology, and Psychotherapy, Universitätsstr. 31, D-93053 Regensburg, Germany Brown University, Department of Cognitive, Linguistic, and Psychological Sciences, Providence, RI 02912, USA
art ic l e i nf o
a b s t r a c t
Article history: Received 19 September 2014 Received in revised form 15 June 2015 Accepted 30 July 2015
Introduction: Evacuation from tunnel fire emergencies may become problematic if tunnel occupants do not engage quickly enough into evacuation. Dangerous goods transporters increase the risks tunnel occupants are exposed to. The aim of the present study is to investigate the effect of an increased risk during a simulated tunnel emergency on participants' subjective hazard perception and evacuation behavior. Methods: Using a five sided CAVE system, two experimental groups were immersed into a virtual road tunnel fire emergency. In the dangerous goods condition a burning gasoline transporter was visible. In the control condition a burning heavy goods vehicle was visible. Hazard perception, pre-movement time, movement time and exit choices were analyzed. Results: In the dangerous goods condition the situation was rated significantly more dangerous than in the control condition. In both conditions participants showed appropriate behavioral reactions and either moved to an emergency exit or to an emergency phone. Discussion: In combination with the high threat ratings the results indicate good situation awareness of the participants, and that emergency signage in the given visibility conditions was effective in guiding participants towards an exit or to an emergency phone. & 2015 Elsevier Ltd. All rights reserved.
Keywords: Virtual reality Evacuation Tunnel emergencies Dangerous goods Risk perception
1. Introduction Dangerous goods are commonly transported on European roads [1]. Inflammable liquids are the most common of these and bear special risks [2]. The confined space in road tunnels and petrol′s highly inflammable properties have contributed to severe fire emergencies in road tunnels in the past, in which extreme heat, smoke, and pressure waves in combination with not enough time to evacuate threatened the health and life of tunnel occupants. An extreme example was the collision of two flammable methanol transporters, which caused a fire in a road tunnel in northwestern China in 2014. Thirty-one tunnel occupants lost their lives during this incident, demonstrating the potential disastrous consequences of tunnel fires [3]. In order to minimize the risk of tunnel emergencies related to petrol and other dangerous n Corresponding author at: Brown University, Department of Cognitive, Linguistic, and Psychological Sciences, Providence, RI 02912, USA E-mail addresses: [email protected], [email protected] (M. Kinateder).
http://dx.doi.org/10.1016/j.firesaf.2015.07.002 0379-7112/& 2015 Elsevier Ltd. All rights reserved.
goods, the European Union defined safety standards for general tunnel safety and transportation of dangerous goods through road tunnels [4,5]. Given these risks, it is not surprising that safety measures for truck drivers and especially dangerous goods drivers have been studied extensively [6,7–11]. However, little attention has been directed to the effect of dangerous goods transporters on other tunnel occupants, whose life and health is at great risk in tunnel fire emergencies. Tunnel occupants do not always show the adequate evacuation responses when confronted with conflicting information [12–16]. Especially in severe tunnel fire emergencies, the physical environment may become very difficult to interpret [17], and certain behavioral patterns arise. For instance, occupants have been observed to be affiliated to familiar places (e.g., the entrance portal of a tunnel) and may neglect faster and safer egress routes [18]. Dangerous goods vehicles are labeled with orange plates which indicate the content of the transport [5]. In addition, the heavy goods vehicles carrying dangerous goods may have distinctive, easily recognizable shapes. However, such shapes may be confused
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by occupants with transporters of other liquids (e.g., a milk transporter may be mistaken for petrol transporter and vice versa). The question is whether or not tunnel occupants recognize a dangerous goods transporter in a tunnel fire, associate it with an increased risk, and adapt their behavior to the increased risk. So far, research on tunnel evacuation has not addressed subjective risk perception of tunnel users. Although, ‘panic’ during most evacuation scenarios has been identified as a myth [19], findings from the evacuation of the World Trade Center 2 on September 11, 2001, showed that an increase in perceived risk led to lower evacuation delays and faster total evacuation times [20– 23]. Risk perception and evacuation during the attacks on the World Trade Center (WTC) on September 11, 2001 have been analyzed in several independent studies. One study found that higher perceived risk was correlated with shorter evacuation [23]. In another study, however, higher risk perception was correlated with more information seeking, more pre-evacuation actions, and consequently longer evacuation delays [24]. Differences between these studies may be explained by differences in the samples and operationalization of perceived risk (see [25, 26] for a more detailed discussion). Delays in evacuation may increase the risk for occupants not reaching an emergency exit in time. In fact, inappropriate occupant behavior has been documented during several tunnel emergencies. For example, 27 of the 39 victims of the Mont-Blanc tunnel fire in 1999 had stayed in their vehicles and did not try to evacuate [27,28]. A potential explanation for this is that tunnel occupants overestimate the safety of their own vehicle and underestimate the risks of a situation. Although a recent review revealed that the role of risk perception is still not completely understood [25,26], we hypothesize that an increase in perceived threat of a situation is associated with pre-movement and movement times during a simulated road tunnel fire. Therefore, the goal of the present study was to systematically manipulate the perceived threat in a tunnel emergency by comparing a tunnel fire including a dangerous goods transporter with a tunnel fire without a dangerous goods transporter. To this end, a virtual reality (VR) experiment was developed in which two groups of participants were confronted with a tunnel fire.
25
with a cylindrical container labeled with the hazard identification number for inflammable liquids, Fig. 1). 2.2. Dependent variables Participants' movement behavior during the emergency situation was recorded in space over time. In particular, participants′ destination choice (where a participant moved), movement trajectories (length of trajectories and movement patterns), premovement (the time participants waited after trial start until they started to move) and movement times were analyzed. Movement time was defined as the time participant moved through the tunnel until a trial ended (total trial time – pre-movement time) and two sub-parameters were defined. Movement times to reach the emergency exit or phone refer to the time participants needed to reach the emergency exit or phone after they started moving (if they went there). Immediately after the experiment, participants rated their perceived threat on a scale from 0 [no danger at all] to 100 [extremely dangerous] and completed a set of questionnaires (Table 1). Since anxiety can affect evacuation behavior, state, trait, (STAI; [29,30]) and tunnel anxiety (TAQ; [31]) were assessed as control variables after the experiment. In addition state anxiety (STAI-state) was also measured before the experiment. Finally, presence (the experience of being immersed into the VR) and simulator sickness (symptoms of nausea and dizziness associated with being immersed into a VR) were measured using the Igroup Presence Questionnaire (IPQ; [32]) and the Simulator Sickness Questionnaire (SSQ; [33]). In addition, participants rated their expertize in gamepad use and 3D gaming experience on a 5 point Likert scale after the experiment. 2.3. Sample
2. Material and methods
Forty-two participants were recruited for the experiment, of which two did not finish the experiment due to technical reasons. Forty participants (21 female, mean age¼25.40 years, sd¼4.78 years) remained in the study and were randomly assigned into the two experimental conditions (dangerous goods and a control condition). Table 2 gives an overview of the socio-demographic and questionnaire data.
2.1. Design and independent variables
2.4. Apparatus
A between subject design was used in the present study. All participants were confronted with a simulated tunnel emergency in a road tunnel. The control group was situated on foot near a burning heavy goods vehicle, whereas the dangerous goods group saw a burning dangerous goods transporter (petrol transporter
The 3D-multisensory CAVE laboratory at the Department of Psychology I of the University of Würzburg, Germany was used for the present study. The virtual scenario was rendered by a Source Engine Modification (VrSessionMod 0.5) of the multiplayer version of the first-person game Half-Life 2 (Valve, Bellevue, Washington,
Fig. 1. Screenshot of the virtual tunnel emergency situation in the dangerous goods condition (left) and the control condition with a regular heavy goods vehicle (right). The emergency situation was exactly the same in both conditions. Brightness and contrast of the screenshots were increased in this figure by 40% for improved illustration. Screenshots were taken at slightly different timings. Flame and smoke development were exactly the same in the two experimental conditions.
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Table 1 Overview of questionnaires. Questionnaire
Abbreviation
Description
State-Trait Anxiety Inventory Tunnel Anxiety Questionnaire Igroup Presence Questionnaire
STAI TAQ IPQ
Simulator Sickness Questionnaire
SSQ
Self-reported measures of state (currently experienced) and trait anxiety (personality trait) Self-reported measures of anxiety experienced while driving through a tunnel either as a driver or a passenger Self-reported measures of the sense of presence experienced in VR on four sub-scales: (1) Spatial presence: degree of the sense of being physically present in VR (2)Involvement: amount of attention focused on VR and the involvement experienced (3) Experienced Realism: subjective rating of realism of VR (4) General factor (G1) correlating with factors 1–3 Self-reported degree of oculomotor symptoms, disorientation, nausea and overall sickness after experiencing VR
Table 2 Socio-demographic and questionnaire data.
Age STAI trait STAI state before STAI state after TAQ IPQ G1 IPQ spatial presence IPQ involvement IPQ experienced realism SSQ total score
Control group
Dangerous goods
t
p
m
sd
m
sd
25.10 38.90 36.52 36.91 5.50 4.05 3.73 3.65 3.39
4.67 11.40 6.88 7.67 4.37 1.32 0.60 1.30 0.86
25.95 35.05 35.68 36.11 5.26 4.14 4.08 4.15 2.88
4.99 8.51 7.48 8.20 4.19 1.24 0.80 1.03 0.80
0.56 1.20 0.37 0.32 0.17 0.23 1.56 1.38 1.94
.58 .24 .71 .75 .86 .81 .12 .17 .05
7.20
7.00
8.28
9.08
0.43
.67
Note: n(Control group)¼19, n(Dangerous goods)¼21; all df ¼38; STAI¼ State-Trait Anxiety Inventory; TAQ ¼Tunnel Anxiety Questionnaire; IPQ ¼Igroup Presence Questionnaire; SSQ ¼ Simulator Sickness Questionnaire; Sumscores were calculated for each questionnaire.
USA). The VR simulation software CS-Research 5.6 (VTplus, Würzburg, Germany; see www.cybersession.info for detailed information) controlled the experimental procedure and performed the data collection. The virtual scenario was presented in a 5-sided Cave Automatic Virtual Environment (CAVE; I-Space5 by BARCO, Kuurne, Belgium) (size: 4 3 3 m; front-wall resolution: 2016 1486 pixels, floor and rear wall: 1627 1200 pixels, side walls: 1220 1200). The system used 12 channels to generate stereoscopic images with six projectors. Passive interference-filtering-glasses (Infitec Premium, Infitec, Ulm, Germany) were used to separate stereoscopic images for the participants. An active infrared LED system (4 cameras; PhaseSpace Impulse, PhaseSpace Inc., San Leandro, CA, USA) tracked movement and orientation of the participants' glasses and navigation device. Participants could navigate with a joystick on a tracked wireless gamepad at a maximum walking speed of 2.86 m/s. The head and gamepad position were used to adapt the visualization of the 3D images. A 7.1 Surround Sound system presented the audio simulation.
to open doors using the buttons of the navigation gamepad. After the training, the experimenter informed the participants that they would find themselves on foot in a road tunnel and that they should do what they thought was appropriate. Then, the displays in the CAVE faded to black, participants were teleported into the virtual road tunnel, the displays faded back to the tunnel, and the trial began. A virtual evacuation scenario in a two-bore uni-directional highway road tunnel was presented to the participants. The total size of the tunnel cross section was 9.50 m, in accordance with German Standards (RABT, 2006), consisting of two lanes (each 3.75 m) and sidewalks (each 1 m). The visible tunnel safety installations were in line with RABT 2006 standards. Emergency exits led to an emergency crossway for pedestrians, linking the two tunnel bores. The emergency exit was signposted by a standard European back-lit sign (European Commission, 2004) and standard European Emergency signage was available in the tunnel every 25 m (Bundesanstalt für Straßenwesen, 2006). No emergency messages were played back during the scenario. Fig. 1 illustrates the tunnel fire emergency. A burning heavy goods vehicle blocked the road close to the emergency phone. Smoke was rendered in the simulated tunnel in order to produce reduced visibility conditions (visibility of approximately 8–15 m). The smoke moved towards the participant′s starting position. In addition, there were several other vehicles placed on the road. Fig. 2 gives an overview of the scenario. Participants were placed on foot next to a car at the starting position and were oriented in driving direction. Participants could move freely in any direction in the tunnel. Over time, the tunnel filled with smoke. If a participant opened the door of the emergency exit or of a vehicle, used one of the emergency phones, or walked past the vehicles in the tunnel, the experiment ended. Then participants rated the perceived threat of the situation, left the CAVE, and completed the STAI-state, SSQ, and IPQ questionnaires. At the end of the experimental session, the experimenter debriefed, thanked, and reimbursed (course credits or monetary compensation) the participants.
2.5. Procedure The ethics committee of the medical faculty of the University of Würzburg, Germany approved the present study before the data collection. After participants arrived at the laboratory, they consented to take part in a study on tunnel safety and completed a socio-demographic questionnaire, the STAI-state and trait, and the TAQ. Then, the experimenter equipped participants with the interference-filtering-glasses as well as the tracking equipment and explained the handling of the navigation gamepad. After that, participants entered the CAVE. Participants completed a training scenario in which they walked through a virtual maze in order to practice navigation in VR. In the training scenario, participants had
Emergency phone
Starting position
Heavy goods vehicle / Dangerous goods transporter
Car
Emergency Signage
Emergency Exit
10 m
Fig. 2. Schematic overview of the two tunnel bores and the emergency scenario; emergency signage is located every 25 m along the tunnel wall.
M. Kinateder et al. / Fire Safety Journal 78 (2015) 24–30
3. Results 3.1. Perceived threat Fig. 3 shows the threat ratings of the scenario. Participants in the dangerous goods group rated the emergency scenario as significantly more dangerous than the control group, t(38) ¼2.73, p ¼.011.
3.2. Behavioral parameters Table 3 shows the total trial, pre-movement, and movement time. The total length of the participants′ trajectories as well as the mean minimal and maximum distance to the burning heavy goods vehicle/dangerous goods transporter are displayed in the same table. There were no significant differences between the two experimental conditions. One participant from the dangerous goods condition moved past the heavy goods vehicle (Fig. 4). After removing this participant from the analysis, the minimum distance to the burning heavy goods vehicle was significantly lower – albeit marginally – in the control condition, t(38) ¼1.91, p ¼.065, d ¼0.60. All other behavioral parameters remained unaffected. Table 4 shows the participants' destination choices. Twentytwo participants decided to go to the emergency phone close to the starting position and 15 moved to the emergency exit. There were no significant differences between the two experimental groups regarding destination choice, χ²(3) ¼4.76, p ¼.190. In both groups, participants interacted (i.e., pressed a button on the gamepad) with the first safety installation they encountered. If a participant decided to move to the emergency exit, they also opened its door. If a participant approached the emergency phone, they also initiated a distress call.
100
Rating [0−100]
75
50
25
0 Control Group
Dangerous goods
Fig. 3. Boxplot of the threat ratings of the tunnel emergency. Boxplot whiskers indicate the inter-quartile range.
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3.2.1. Movement Trajectories The descriptive movement trajectories (Fig. 4) indicate no significant differences between the two experimental groups. One participant in the dangerous goods condition moved past the emergency phone and approached the burning heavy goods vehicle. Also note that some participants stopped (indicated by more opaque blue spots on the trajectories in Fig. 4), however this did not affect the differences in the movement time calculation. The heat-map analysis (Fig. 5) shows a graphical representation of how the two experimental groups where distributed over the tunnel. A grid of squared cells (side length 1.27 m/50 in.) was laid over the tunnel. Frequencies of participants moving through each cell were calculated, where a participant was only counted once per cell. That way, participants who stayed longer time in a cell would not be weighted more than participants who moved through a cell without waiting. For each cell, the difference in relative frequencies was calculated.
4. Discussion Two experimental groups were confronted with a virtual tunnel fire emergency. In the dangerous goods condition participants saw a burning dangerous goods transporter; in the control condition they saw a burning heavy goods vehicle. Although participants rated the dangerous goods condition as significantly more threatening, participants showed similar patterns of protective action in both experimental groups. Given that the control condition was already rated as highly threatening, it is possible that this situation already was ′dangerous enough′ to trigger protective action so that a higher perceived threat did not have an additional effect. Although participants in the dangerous goods condition moved longer distances and kept greater minimum and maximum distance to the burning dangerous goods transporter than participants in the control group, these descriptive differences were not or only marginally statistical significant after removing outliers from the analysis. These results can be explained by the strong variance in thebehavioral measures. However, given that in a real world tunnel emergency even a small difference in the distance to the nearest place of safety might save the life of a tunnel occupant, future studies should test occupants′ preferred minimum safety distance to fire sources with bigger sample sizes. In the case of tunnel fires, even small effects are worth investigating as they could reflect differences in lives saved or lost. In both experimental groups some participants did not move to an emergency exit or phone but went back to the tunnel entrance portal. The concept of movement to the familiar may explain this behavior [18]. It is possible that the entrance portal was a familiar place for these participants and they decided to neglect closer emergency exits along the tunnel. Occupants′ affiliation with familiar places might be especially problematic in tunnel emergencies when flames and smoke move too fast for occupants to reach the tunnel entrance portal in time. Overall, participants in both groups rated the scenario as very dangerous. This indicates that participants recognized the threat of the simulated tunnel fire as well as the dangerous goods transporter and, therefore, supports the validity of the VR simulation. Furthermore, the dangerous goods scenario was rated as even more dangerous than the control condition. It is interesting to note that one participant in the control group rated the situation as ‘not threatening at all’. There are two potential explanations: first, the participant did not recognize, or underestimated the risks of a tunnel fire. Second, the participant did not experience enough presence and was always completely aware that what he/she saw was a simulation. This is supported by
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Table 3 Behavioral parameters in the two experimental conditions: trial time, pre-movement, and movement times as well as total trajectory length, minimum and maximum distance to the burning heavy goods vehicle. Control group
Trial time [s] Pre-movement time [s] Movement time (total) [s] Movement time to emergency exit [s] Movement time to emergency phone [s] Total trajectory length [m] Minimum distance to burning heavy goods vehicle [m] Maximum distance to burning heavy goods vehicle [m]
Dangerous goods
m
Sd
m
sd
56.23 7.04 49.19 80.64 33.23 87.04 15.65 79.38
36.54 14.12 28.71 14.45 9.02 80.38 6.78 68.27
65.29 5.76 59.53 82.79 31.49 102.18 19.47 86.89
55.38 4.70 52.81 46.94 6.94 99.06 9.14 56.24
the participant′s low self-reported involvement (a subscale of the IPQ) in the virtual environment. Understanding perceived threat during fire emergencies is relevant for researchers and practitioners in fire evacuation research. Since perceived threat is often postulated to influence the point of transition from pre-evacuation to evacuation stage [25,26], an accurate description of the tunnel evacuation process mandates a precise understanding of risk perception. Evacuation models, which aim to predict available and required safe egress time during fires could, for example, benefit from a better understanding of the role of perceived threat and consequently develop more accurate evacuation models. 4.1. Limitations Some limitations of the study need to be addressed. First, participants were alone in the scenario. In most tunnel fires other occupants are present and are likely to influence each other. Reports from tunnel emergencies [34,35] and virtual reality studies have demonstrated that social influence affects tunnel evacuation behavior [36,37]. Second, the present VR scenario consisted of visual and auditory simulation. Tunnel fires, however, are also characterized by smells of smoke and burning materials as well as by the heat and airflow generated by the fire. Future studies should include olfactory simulations and, if possible, heat simulations. However, the high presence ratings (especial the G1 and spatial presence factors of the IPQ) underline the high immersiveness of the VR simulation in the present study. On a more general note, the question of validity, reliability, and objectivity of a research method is an important debate within empirical research on fire evacuation. VR studies aim to create scenarios and problems that are analogous to the real world (not identical). The results of the present study should be interpreted in a relative and not an absolute manner since VR data of, for example, movement
n
t
p
40 40 40 15 22 40 40 40
0.62 0.38 0.78 0.13 0.51 0.91 1.51 0.38
.54 .71 .44 .89 .61 .36 .14 .70
Table 4 Destination choice in the two experimental conditions. Control group Dangerous goods Emergency phone close to starting position Emerncy phone further away from starting position Emergency exit Tunnel entrance portal
12 0
10 1
5 2
10 0
speed may not match the absolute walking movement speeds in a real world tunnel emergency. For a more general discussion of the strengths and weaknesses of VR for fire evacuation research, see [38]. Third, participants were mainly university students, thus other populations are not represented. Future studies should use more heterogeneous samples including, for example, different age groups and experts on tunnel safety (e.g., fire fighters, professional drivers). Age specifically might be an important confounding factor, since tunnel safety has become a mandatory part of Germany's driver's education in 2008. However, adding age as a covariate did not change the results in the present study. Fourth, participants were teleported into the tunnel and were on foot instead of sitting in a car and driving into the tunnel. Using a vehicle has been found to be a common means of evacuation in real tunnel incidents. In many cases this may not be possible since vehicles, debris, or other obstacles may block the lanes. Thus, the goal of the present study was to study protective actions in a walking scenario. However, the nature of the scenario may lead to an overestimation of the absolute frequency emergency exit usage. Fifth, participants in the dangerous goods condition reported the scenario to be less realistic. After removing outliers, however, these differences were no longer statistically significant. Sixth, participants navigated through the virtual environment using a gamepad and did not walk. That is, differences in the pre-movement and movement
Fig. 4. Movement trajectories of the participants in the control group (a) and the dangerous goods group (b). The darker the shade, the more participants or the more frequently a participant passed a coordinate. Single blue spots indicate where a participant paused moving. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
M. Kinateder et al. / Fire Safety Journal 78 (2015) 24–30
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Fig. 5. Heat-map with group comparisons. Differences between relative frequencies of participants counted in each cell were calculated. Blue color/positive values indicate that more participants in the dangerous goods condition were counted in a cell. Red color/negative values indicate that more participants in the control group were counted in a cell. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
data of the present study could also be the results of differences in the ease with which participants could handle the navigation device. However, participants self-reported expertize in gamepad use and 3D gaming experience was not correlated to the premovement and movement time in the study nor were there any group differences. Further research is necessary to investigate to what degree different navigation methods in virtual environments correspond.
[3] [4]
[5]
[6]
5. Conclusions
[7]
In conclusion, the present study demonstrated an effect of a dangerous goods vehicle on the threat experienced in a tunnel fire but not on protective actions (destination choice, pre-movement and movement time, movement trajectories and movement distance). Participants realized the severity of the situation in both experimental conditions, but rated the dangerous goods transporter as more threatening, indicating that participants had good situation awareness.
[8]
[9]
[10]
[11]
[12]
Funding information The data published here were recorded within the scope of the project “Protection of critical bridges and tunnels” [Schutz kritischer Brücken und Tunnel– SKRIBTPlus, grant number: 13N9636], supported by the German Federal Ministry of Education and Research. The funding source had no involvement in the design and analysis of the study, nor at any other step of the preparation of the manuscript.
[13]
[14]
[15]
[16]
Conflict of interest statement
[17] [18]
Prof. Paul Pauli, Prof. Andreas Mühlberger, and Mathias Müller are shareholders of a commercial company that develops virtual environment research systems for empirical studies in the field of psychology, psychiatry, and psychotherapy. Mathias Müller is executive officer of the same company. No further potential conflicting interests exist.
[19] [20]
[21]
[22]
Acknowledgments [23]
The authors would like to thank Jason Roth for carefully proofreading the manuscript and Stefanie Löw for helping with the data collection.
[24]
[25]
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