Individual Driving Behaviour in Wildfire Smoke

Private cars are the main mode of transportation during evacuations caused by wildfires [36]. The area affected by a wildfire is highly dependent on weather changes and topography, making it difficult to predict who needs to evacuate and when [24]. In addition, wildfires can create updrafts which can launch burning materials, called firebrands, into the atmosphere, causing the so called spotting phenomenon [2, 41]. This can lead to the development of fire-fronts kilometres away from the current location. This poses a set of challenges from the evacuation perspective, among which the possibility for people to be located in an area where smoke is present. Smoke emission can have a direct impact on human behaviour during a wildfire scenario by potentially triggering the evacuation process, affect the choice of evacuation route, and affect the choice of speed while driving away from the danger [15, 23].

While human response in case of wildfire scenarios has been investigated in several studies [5, 11, 26], to date, there is little to no knowledge on how people drive on roads where wildfire smoke is present [32]. This is a key issue during large scale evacuations, as reduced speeds may affect evacuation times, thus putting the evacuating population in higher danger [36]. This scenario has been reported in several actual wildfire evacuation scenarios (e.g. the 2016 Fort McMurray fire in which smoke hindered the evacuation process [43]).

In the context of building evacuation, studies have found that people often choose to move through smoke while on foot [35]. Several experimental studies [12, 18, 31] showed that people walking through smoke tend to decrease their movement speed with increased smoke density. This behaviour is linked to the fact that people have a shorter range of visible space and are afraid to be confronted with an unseen obstacle [31]. As for the case of walking speed in building evacuation, it is also important to understand if the reduction in driving speed is linked to the speed in clear conditions [33].

During an evacuation scenario, the conditions faced may cause drivers to attempt increase their driving velocity due to the urgency of the situation [28]. Currently, the main bulk of research conducted on driving behaviour in reduced visibility conditions relate to driving in fog (i.e. a non-emergency situation). In low-visibility conditions, drivers might rely on their own speed estimation rather than the speedometer in order to keep their attention on the road [8]. For instance, in presence of fog, the perceived speed may be lower than the actual speed, thus drivers might adopt a speed faster than their target speed [39, 45]. The chosen driving speed in fog can also be affected by the degree of experience of the driver [27]. A research study reports that drivers reduce their speed in low-visibility conditions by approximately 40% in order to increase safety [45].

In order to perform an accurate assessment of driving behaviour on the road, a set of key issues have been identified, namely (1) to build a correlation between driver speeds and smoke density and (2) to assess the extent to which the driving speed is affected by the speed adopted in the previous road segment having different visibility conditions (better or worse), (3) to check if the lateral position of the vehicle on the road is affected by presence of smoke, and 4) to study if the driving speed through smoke can be linked to the driving speed in clear conditions.

To address these issues, a virtual reality (VR) experiment with a head mounted displays (HMD), steering wheel and pedals has been conducted. The use of a steering wheel and pedals is common in driving simulators and HMDs are more and more used in fire evacuation research [19].

Different methodological approaches exist to collect data on driving behaviour in emergency evacuation scenarios. Data from driving behaviour during actual wildfire evacuation accidents have the highest validity. Nevertheless, the occurrence of a wildfire scenario cannot be precisely predicted, data are difficult to obtain and are generally not publicly available. In addition, the information available on reported smoke density during wildfires at the driver height is limited and is generally associated with a low accuracy. Given the need to study driving behaviour at controlled varying smoke density level, the use of a laboratory experiment has been chosen. Due to ethical, practical and financial constraints, the use of driving simulators displaying a VR environment has been identified as an ideal tool to perform this study. Those tools have been proved to be able to elicit different driving behaviour in relation to emotions [16]. A comparison of different types of research methods for fire evacuation research [19] highlights that VR experiments require a low time and cost for data collection, they allow the possibility of exact replication and full experimental control. A key issue to address in VR experiments is the lack of costs in terms of physical harm, monetary losses and/or liability of actions [3]. This might correspond to the lack of monetary or health consequences associated with damaging the car or having an accident in the present experiments. It is therefore crucial to design the VR scenarios and provide instructions which will elicit realistic behaviour [37]. Compared to actual driving tests, VR is therefore a low-cost solution which allows high experimental control and it permits to continuously log the driving behaviour of participants (both in terms of their chosen speed and their trajectories by outputting the parametric equations of each participant trajectory). A within-subject experimental approach was used as this allows linking the driving behaviour in clear conditions and those in presence of smoke for each participant. This experimental approach was chosen as it was recommended in a study investigating walking speeds in smoke [31].

To date, there is almost no research on how people drive in wildfire smoke. On one hand, this makes this study a necessary step for several wildfire safety stakeholders, e.g. traffic evacuation modellers and emergency responders. On the other hand, it is not possible to validate the results of the experiments with real world data.

The findings of this study clearly indicate that people tend to reduce their driving speed in smoke-filled environments. This is in line with research studies concerning walking speeds in smoke [31] and driving in fog [45] and it demonstrates that traffic models would need to implement this factor for their use in wildfire evacuation scenarios. This study indicates that the reduction of driving speed in wildfire smoke can get greater than in the presence of fog. In fact, the analysis of the fractional speeds show that the driving speed can get as low as to approximately 30% of the speed in clear conditions. This is a greater reduction than what reported in studies concerning driving in fog [45]. It is important to note that existing studies concerning driving speed in presence of fog are collected in normal conditions (i.e. not during an emergency), thus future comparisons should address this issue in light of the urgency of the situation.

No clear trend was found on the impact of the visibility conditions in the previously driven road segment. Traffic evacuation models currently neglect the impact of smoke and they do not explicitly consider the impact of reduced visibility conditions on speed [15]. They can subsequently produce non-conservative results when applied to wildfire evacuation scenarios.

Most of the participants reported being afraid of getting into an accident while driving in smoke. This is reasonable as the participants were experiencing high densities of smoke and had highly limited visibility. The scenario which the participants were facing was also a highly unfamiliar scenario as almost none of the participants had ever experienced a wildfire in real life. The combination of both limited visibility and an unfamiliar situation may provide an explanation on why they felt worried that they would get in an accident.

The perceived risk among the participants may be related to their limited experience on wildfires. As previously mentioned, only a few participants had any experience of wildfires. A speculation can be that a person who has previously experienced a wildfire would be less prone to wanting to pull over or turn around as s/he may not be aware of the negative consequences of driving through a wildfire. Alternatively, they may be more prone to drive faster as they have previously successfully evacuated an area affected by a wildfire. Another speculation could support the contrary: a person who has experienced a wildfire drives slower as s/he may have experienced issues with obstacles on the road and therefore drive more carefully than what was observed in the experiments. The impact of experience which leads to underestimate risks is linked to the anchoring bias, which is the tendency to rely on past (e.g., positive) experiences [20]. Similarly, risk underestimation can be linked to the cry wolf effect (a tendency to ignore a risk after experiencing situations with positive outcomes), which is a known issue in evacuation research [7, 10, 21, 22, 30, 42].

Some of the participants also mentioned—either in the survey or after the experiment was completed—that they felt stressed by the presence of smoke and the poor visibility. Feeling stressed during the experiment is in line with real wildfire evacuation situations [26] and it can then be seen as an indicator that the participants felt immersed by the environment, which enhances the validity of the results [19].

Other participants mentioned that they would have not driven as fast as they did if they had experienced the same scenario in real life. This issue may lead to an overestimation of the choice of speed among evacuees. Nevertheless, the average speeds in smoke-free conditions were in the order of 70 km/h which is in line with what would be expected in the type of road under consideration. In modelling applications, this issue may lead to recommend the use of a fractional reduction of speed (see Eq. 5) based on the unimpeded speed in clear conditions of the road under consideration, as this provides a proportional reduction of speed rather than an absolute value for a given visibility condition.

Concerning the vehicle position on the road while driving in smoke, results do not show a clear relationship between the distance to the centre line on the road and smoke density. Nevertheless, it can be seen in Table 2 that the average distance to the centre line slightly increases as the optical density becomes higher. Although no statistical difference could be established, it is possible that drivers chose to drive further away from the centre when visibility is reduced. A possible explanation for this behaviour is that as the visibility is reduced, the drivers choose to go further to the right side of the road in order to have an extra margin to vehicles driving in the opposite direction (as the case study refers to a bi-directional road with right-hand traffic).

The result of the experiments is based on the trajectory data obtained from 46 people in 5 different visibility conditions and transitions between them. The participants were all students. A larger sample size with greater variations in their characteristics, such as occupation and experience as a driver, would allow a greater generalization of the findings. The average age among the participants was equal to 21.5 years old, thus the sample includes a young group of inexperienced drivers. This issue should be taken into consideration when using the results obtained, as younger drivers generally have a higher risk propensity [14]. An evaluation of this issue was made comparing the impact of gender on observed driving speed, as young women are generally more risk averse in driving scenarios [25], but no statistical differences were found comparing the average speeds of the two groups under each visibility condition. In addition, young age can also have an impact on risk aversion in emergencies as younger men may be less likely to take protective actions [6]. These aspects should be taken into consideration while using the results obtained in this study.

Furthermore, the use of the results should consider the simplifications in the driving simulation setup and the surrounding environment. The driving setup included a gaming steering wheel which is smaller than an actual car steering wheel (as it is designed to resemble the steering wheel of a racing car). This limitation should be considered as the size of a steering wheel is known to have an impact on the driver (especially given existing steering wheel assistance systems which depends on steering wheel size [40]). Similarly, the driving seating position while performing the driving tasks did not correspond to an ordinary car. This might impact the posture of the driver while seating, which is known to have an impact on driving [29].

Regarding the virtual environment, there were no other vehicles on the road and no intersections. Although no information about whether or not there would be other vehicles on the road was given to the participants, participants may have assumed that—as there were no cars in the beginning of the experiment—there would not be any later on. Others may not have made this assumption. The lack of costs/consequences in VR may be linked to more risk-taking behaviour by participants during the experiments. Despite the simplifications in the scenario, no participant got off road and the observed speeds were reasonable given the type of road under consideration. It should also be noted that driving simulators have been tested in several contexts [44], with dedicated validation studies being performed for the performance of speed research [13]. This study confirms that driving simulators are able to elicit a sufficiently realistic driving behaviour for speed research. Assuming higher risk averse behaviour in the real world compared to VR may led to further advocating for the introduction of a reduction of driving speed due to the presence of smoke. The simplifications of the VR experiments should however be carefully considered when applying the results in real life situations.

The impact of reduced visibility due to wildfire smoke on driving has been investigated in isolation. Future research should focus on investigating the combination of reduced visibility with other variables which might affect a wildfire evacuation scenario from both an environmental perspective (e.g. different road layouts, signage, the presence of ember showers, different proximity levels of the wildfire threat, etc.) as well as the actual driving perspective (e.g. the behaviour of other drivers on the road, road management instructions, etc.).

This study shows that people tend to reduce their driving speed when the visibility is reduced due to wildfire smoke. There is a great variation in the speed at which the participants chose to drive. Simple regression models of driving speed versus visibility have been provided so that they could be implemented in traffic modelling tools. They consider either an absolute choice of speed based on the visibility conditions or a fractional choice of speed based on the individual speed in smoke-free conditions. No clear difference can be found regarding different choice of speed depending on the smoke density in the previously driven road segment. Despite the distance to the centre line does not clearly change based on the density of the smoke, a slight increase in distance has been observed as the smoke density increased.