161 - Community evacuation with Enrico Ronchi and Max Kinateder

Transcript

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Hello everybody, welcome to the Fire Science Show.

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You've most likely noticed that lately in the Fire Science Show we have a lot of wildfire content.

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The podcast is not changing into Wildfire Science Show, but I try to cover this for multiple reasons.

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I honestly believe, seeing what's happening in academia, seeing the changes in funding agencies, seeing the trends that are happening, seeing how we became connected as an evidence of the climate change, seeing the monstrous losses in wildfires, I clearly see that wildfire safety engineering will become a profession of the future and I really want to cover that well in the fire science show so you gain the best access to this knowledge that could be useful in a few years from now who knows how soon and for the younger colleagues who are just starting their career as a fire safety engineer.

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Perhaps you'll become a wildfire safety engineer one day and I hope that fire science show brought you a great inspiration to become one.

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Anyway.

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We've talked about what the wee problem is in general.

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We've talked about trigger boundaries so when does a community have to leave their houses, when they have to start to evacuate, and use of the method assessing, this time to mark a boundary around the community, helping them in their risk assessment.

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Now, in that episode, as you may recall, we have not touched the evacuation process itself, and this is the subject of today's show.

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So I've invited Enrico Ronchi, associate Professor at Lund University, and Max Kinteder, research Officer at NRC Canada, who are largely involved in studying this aspect of wildfires, that is, the evacuation layer, the evacuation processes, the traffic conditions and the interaction between the smoke driving and, in general, the wildfire and the evacuation process itself.

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So a very interesting episode.

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Again, I also believe that a lot of stuff we learn for wildfires can be translated into buildings, and this is the case in here.

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I also believe a lot of things that we learn about wildfire evacuation or community evacuation we can translate into the building evacuation processes.

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So, yeah, it's a good one, let's spin the intro and jump into the episode.

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Good one, let's spin the intro and jump into the episode.

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Welcome to the Firesize Show.

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My name is Wojciech Wigrzyński and I will be your host.

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Hello everybody, welcome to the Fire Science Show.

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I am joined today by two gentlemen Enrico Ronchi, associate Professor at Lund University.

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Hey, enrico, welcome back to the podcast after a long time.

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Thanks.

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Thanks for the invitation.

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Wojciech, I'm very happy to be back.

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And first time in the show, dr Max Kineteder from NRC Canada.

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Hey Max, welcome to the podcast.

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Hi Wojciech, Very nice to be here.

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Thank you for having us.

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Thanks, thanks for coming and it seems I'm having a wee month in the podcast because there's so many interesting topics around the subject of wildfires and wee problem in general.

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Actually, we tried to define what is a wee problem a few weeks ago and that was the most interesting conversation.

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Also a few weeks ago I had Harry and Nick from Imperial College talking here about the trigger boundaries and they were telling me about how to measure when to evacuate a community.

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And I know you guys are on the other end of this question because you are studying how would we evacuate, how does the process look like and what actually happens when large communities are evacuating?

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And my first question actually most of the listeners of the Fire Science Show would be related to civil engineering or fire safety engineering in buildings.

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I wonder to what extent evacuating a community, how much is it similar to evacuating a building and how much it's different, the scale?

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that we talk about is completely different, but also the modes of evacuation are different.

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When we look into buildings, we mostly look at evacuation on food.

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Instead, when we look at communities, we look at many different means of evacuation.

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So not only evacuation on food, but also using different types of transportation like private vehicles, public vehicles and also alternative means.

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Sometimes we've seen very, let's say, unique types of evacuation with boats or things like this, but in any case, I would say both the mode of evacuating is different as well as the scale, because even the largest buildings, in terms of population, we talk about thousands of people.

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I mean very rarely.

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Okay, you need to have a massive high rise to have tens of thousands, but that's not super common, while instead, when we talk about community evacuation, unfortunately it's more and more common that we talk about tens of thousands of people to evacuate.

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So to look into that kind of scale is a problem on its own.

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How about the timescale?

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Because, like in the building, I sound an alarm and hopefully after a pre-evacuation time distribution.

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Let's not go too deep into arguments about this.

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But after a certain amount of time I guess everyone left the building or at least started evacuating.

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Is the timescale also a differentiating factor in WE evacuations?

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But after a certain amount of time I guess everyone left the building or at least started evacuating.

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Is the time scale also a differentiating factor in Wii evacuations?

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Yeah, I think so.

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So one key difference is and you already mentioned that is that in a building evacuation you hear the fire alarm and then it's go time right.

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Ideally, and unless it's an announced drill, no one expects a fire alarm to go off right.

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The difference for wildfires most commonly it's a seasonal phenomenon, so people are aware.

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Residents are commonly aware of the risks of that wildfires might occur.

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In North America there's something called fire weather index, so there's some public awareness that there's an increased risk of wildfires.

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And also you have a little bit more lead way to evacuate.

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So, for example, if there's a wildfire in a proximity, you maybe you don't get an immediate evacuation order, but you might get a notice that, okay, maybe you will have to evacuate soon.

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So there's a little bit.

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The timescale is a little bit different.

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I think what's very interesting for us as researchers is that to see.

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So we come from the building evacuation side and we understand pretty well how people move around during evacuations and how they make decisions in buildings.

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What is really interesting to find out is whether those lessons translate to the WUI community.

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And how about safety?

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If I'm evacuating a building, building I'm evacuating people to safety, which usually is outside the building or, you know, a designed area where people should gather.

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If I'm evacuating a hospital, I'm sometimes doing a horizontal evacuation no neighboring fire compartment.

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They're safe when they're, when they're in that space.

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Like, where do you evacuate those people in?

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We even how far you have to take them away to provide them safety, because, I mean, the fire is a transient phenomenon that, in a way, is chasing them right.

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Yeah, well, and that's the challenge right, because we have seen in the past in different events that, for instance, people were initially evacuated in an area or in a shelter and so on, and then that very area became also threatened by the wildfire.

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So I would say that the challenge with the scale is not only the size in terms of area and number of people involved, but it's also to define in itself what's the threatened area, because this is kind of dynamic, so it's a much more dynamic process than what we are used to in buildings, where pretty much we know what are the spatial boundaries of the scenario that we're dealing with, while instead, in a wildfire, we don't even know necessarily to begin with where, let's say, our evacuation ward finish.

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And this of course has implications in design policies, even for all of us that use models and so on, because it's one of those key decisions that you need to take where to evacuate people, and it's not obvious.

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Yeah, so the question is often like what is a place of safety right?

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And that's a broad idea and it can really change dynamically, as Enrico mentioned.

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So one big challenge in wildfires is that smoke travels really far right and that can impact, for example, the vulnerable populations differently than people who don't have certain risk factors.

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If we already use the example of a building pre-evacuation time distributions, how much from the human behavior we know in the building context, how much of that translates to the setting of a wildfire evacuation?

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Are these like completely different processes?

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You're studying human behavior, so I wonder how much of your expertise from tunnels, for example, enrico, how much of that translates to we?

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When it comes to fundamental theories on human behavior in fire.

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We don't have to reinvent the wheel when we move from tunnels or buildings to wildfires but, as mentioned, the fact that the temporal and the spatial scale change bring in other challenges.

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So, for instance, in buildings we don't have this problem of deciding the mode we evacuate with right, so it's pretty much everyone on foot and while this is another challenge which brings in many other questions regarding human behavior, because model choice is a big, let's say, topic of research on its own, when it comes to emergencies, many aspects which are very specifically linked to the larger scale and different type of scenarios that need dedicated research and in fact many of us that move from buildings to the wildfire scale, let's say, had to focus a lot on reading up also what is done in research in the transportation world and so on.

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Fortunately, I consider myself lucky because I come from the transport world, and so on.

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Fortunately, I consider myself lucky because I come from the transport world.

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Then I went into building and I came back into transportation, modeling and understanding all the factors linked to, let's say, people taking decision at that kind of scale.

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So so we develop these models.

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So we have researchers in the building evacuation field, have developed these models and theories to try to understand human behavior in fire and, I think, applying those.

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It's an open question like to what degree those models and theories are useful and representative for the wildfire context, and so one example is a common phenomenon that we observe in building evacuations is movement to the familiar right, so that we often observe that when there's a fire drill that people tend to evacuate via the routes that they know right.

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So often we see people just evacuating towards, not towards the nearest exit but to the main entrance of a building, and what's really interesting to see is what we don't know if that is also applicable for the Wi-Fi context.

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And how about challenges Like when we design buildings?

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There are those, let's say, group behaviors.

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People try to pick up their children from the kids play areas and stuff like that.

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I guess in the community evacuation you see the same things, but on a much greater scale.

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How is this social context in which people are captured by the fire, how that changes the onset of the evacuation itself?

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It must be much more chaotic, right.

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Yeah, imagine people are at work, right, and then you receive an evacuation notice and then you know, like, maybe your kids are at school, your partner is somewhere else, maybe you have relatives in town, so that changes many things right.

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And yeah, this is just as Enrico said before.

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This just increases the dynamic nature of those scenarios.

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And I mean and this is reflecting the way we started this because I mean there is a whole field you know, my modeling soul comes here there is a whole field of what they call activity models, activity-based models, which are not just going from point a to point b but very similar to what we have in the buildings, that we call behavioral itineraries, so like going from point a, b to b, b to c and c to d.

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But I mean this probably plays an even more important role when it comes to wildfire evacuation because, again, the scale is larger.

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This can have a larger impact on evacuation flows and so on and the whole evacuation process can be affected.

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Yeah, the scale is larger.

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The challenges I hope we highlighted the differences.

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I'm doing this because you know a lot of listeners in here are fire safety engineers and I am truly convinced that in a decade, two decades, a wildfire engineer will be a job that will be a well-paid job and will be in high need.

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So I'm trying to convince my fellow listeners that it's worthwhile your time to invest in knowledge in this area, that that's why topics like this are brought to the Fire Science Show, not just because they're very interesting.

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Now I'm really interested in how do we study this?

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There are wildfires occurring every now and then.

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Today in the morning there was a wildfire evacuation in the media.

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How are you learning or how are you gathering information to fuel your future models and future predictions and optimizations of community evacuations?

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How are we studying this right now?

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There is a wide range of methods that are used nowadays to study wildfire evacuation, and, you know, people like me and Max have been exploring some of those.

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There are other research that look at others, but I mean evacuation drills are done also at community scale, not just at the building scale.

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We can talk about, for instance, a recent experience that we had in the community of Roxborough Park in Colorado, where we had the opportunity to observe a community evacuation drill.

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The logistics, the work, the preparation that goes into this it's massive compared to what you have in a building, and for us, though, it's this kind of opportunities that are goldmine when it comes to data, because then we can deploy everything that we can, from drones to traffic counters, observers Even we developed an app just to track how people respond.

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So this is the kind of data that we are interested in to try to understand how people will react to this type of emergencies, and that's a drill scale.

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Another avenue is from real-world scenarios, like, for instance, when it comes to traffic.

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There are databases of traffic data, which include data from emergency scenarios, so we have been looking.

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One of my PhD students, arthur Royer, has been looking into, for instance, extracting data related to wildfire, evacuation from a traffic database, for instance in California.

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So in some of those very large evacuation scenarios that happen in California, so in some of those very large evacuation scenarios that happen in California.

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But there are many more avenues, and the one that's definitely Max can give more insights in is, for instance, vr and the use of driving simulations, because that's very powerful to collect data on human behavior when driving.

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Yeah, sure.

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So I think Enrico hit the nail on the head when he said that we use many methods and we're not doing that because we'd like to try out all different kinds of data sources, although that is fun and interesting and scientifically interesting and engaging.

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But the problem is that it's very hard to get reproducible, valid, reliable and generalizable data from Wi-Fi evacuations, also from building evacuations.

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I think the only example where we have very good data that is from the field and also experimentally valid is from tunnel evacuations, because this is the only environment where the real world is completely controllable and artificial, which is great for researchers, but it's also true for the rest.

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But for everything else, we always need to find this balance between what we call ecological validity and experimental control.

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So say, for example, we go to an observation from a past real world event this is the real thing, right?

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It has the highest ecological validity that you can imagine.

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However, it's very hard to first of all understand whether or not these findings from this one specific event can be generalized to other types of view, and what are those factors that make it not translatable or translatable.

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The other thing that's very hard to do is to identify cause and effect right, because we don't have controlled conditions.

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So what we then do?

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We move into the laboratory.

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In the laboratory, everything is very controlled.

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A very extreme case of that is virtual reality, where everything that the participant sees is defined and controlled by the experimenter.

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So this is very useful because we can record very detailed data and also do very precise experimental controls, which means we can really tease apart why people behave in a certain way.

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However, all of our participants are completely aware that they're participating in an experiment or that they are in a laboratory.

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So that reduces the ecological validity right and all the data sources that we're using, like VR lab experiments, surveys, gps data, traffic data from databases, social media data they all have to have this trade-off right that we need to find the balance between ecological validity and experimental control.

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You said social media.

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How do you utilize social media in this context?

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So some really interesting work that I think initiated out of hurricane evacuation, I think somewhere out of the University of Florida I'm not 100% sure about that when they look, for example.

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So people post things on social media right, and these things often leave traces in the public domain.

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So, for example, you can look into, say, in a specific you can either look into the location of a specific user so that you can tell there's something about movement patterns, or you can also look into the content that is being posted right.

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So or you can also look into the content that is being posted right, so that can tell you something about how, for example, information travels during a disaster.

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Correct me if I'm wrong, but I think milestones in evacuation, in understanding human behavior in evacuation, were stuff like MGM Grant World Trade Center, when a significant service was carried out with the survivors of those fires and a lot of knowledge was gathered after the fire by talking to people.

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Is this also your use in this context or here the scale is again too big for that.

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No, no, definitely we can use also so-called qualitative data.

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So, interviewing people, this is something that we do routinely.

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Just to give an example, recently I've been involved in a project funded by the eu civil protection which was looking at tourist behavior in wildfires because, you know, they are particularly vulnerable groups because maybe they don't speak the language, maybe they're not fully familiar with the environment.

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So this was a project together with the upc, el Elsa Pastor in Barcelona, and basically we went on site in areas where they had wildfires in the border between France and Spain, so in Catalonia, and there we had the chance to interview people, both managers of facilities that were evacuated and people that experienced evacuation, to ask about their actions, how they behave during the event.

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And you know, the goal is to unpack, for instance, specific behavior of particular groups.

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I have a very interesting slash, sad episode on one of these interviews, when I had one of the managers of a tourist facility that told me you know, some of the tourists, while we were fighting the fire that was really at the border of our property, were more worried about having breakfast, because sometimes the risk perception is very different when it comes to people that are familiar with the environment and people that are completely unfamiliar with the risk and with the situation.

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So it is definitely important to bring in also qualitative data to the picture.

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So this is definitely something that researchers like me.

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I know Erika Kuligoski nowadays these days she's doing a very large data collection in Australia regarding wildfire evacuation.

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So qualitative.

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In general, they often get a bad rap because everyone is aware of the biases that are in these data, but they're extremely useful for a number of things.

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So they, for example, if you talk to stakeholders, you can ask the question, so you can ask people who are very familiar with the local context, just, for example, as the tourist manager that Enrico just managed.

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This is information that you otherwise wouldn't be able to get right, and you can ask people why they did certain things right, and then you know that there might be bias in there.

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But if you have sufficient amounts of data, it can give you an idea about certain patterns, right, and this is very useful to generate, then hypotheses that you can then test with objective data.

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So it's extremely useful.

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And it's also not trivial, right, so you have to be able to.

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So there's extremely useful and it's also not trivial, right, so you have to be able to.

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So there's a lot of work that goes into developing a good survey, a good interview, so that you are able to extract the information that you need with the precision and objectivity that is necessary.

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You got my interest very high with that drill, Enrico, and we will go there, but I still need to clear one more thing about data collections.

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What kind of data are you trying to distill from that, the decision-making process?

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You mentioned multiple times the modes of transfer, so I guess that's in your interest.

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Are you also creating some stuff like fundamental diagrams that we would have?

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Actually, all that we know about pedestrian dynamics and pedestrian evacuation fundamental diagrams arrived much before in the world of transport.

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The element that is completely missing and that is where we really started doing research a couple of years ago is to how can we create a fundamental diagram, so a relationship between speed and vehicle speed and traffic density when we have smoke, because the way people drive in low visibility is possibly very different than when the visibility is perfect, especially when you have an emergency.

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So the me and max have been putting together were exactly on using driving simulators to try to understand how people drive when they are in this kind of low visibility scenarios.

00:23:27.464 --> 00:23:30.345
And you know it's not a yes, no problem, no.

00:23:30.345 --> 00:23:37.604
So you need to vary the visibility, vary the boundary conditions of the scenarios and see how the behavior change.

00:23:37.604 --> 00:23:52.724
So one of the goals of what we are doing now in the most recent data collection that we did both in Canada and NRC at Lund here in Sweden, is to develop a new fundamental diagram which includes visibility.

00:23:52.724 --> 00:24:08.269
So reduce visibility due to wildfire smoke A bit like it exists for pedestrian evacuation in buildings, where we know that you know from the historical gene data to many of the following experiments that have been done to understand how people walk in smoke.

00:24:08.269 --> 00:24:11.224
So this is where we are and this is where we know we have a gap.

00:24:11.224 --> 00:24:24.515
So it's a couple of years that we looked into this, but now, as I said, we're very close to produce a first fundamental diagram which can consider the impact of wildfire smoke on Brian.

00:24:24.855 --> 00:24:27.494
And it's quite interesting just for a little bit of context.

00:24:27.674 --> 00:24:34.183
So obviously there's research on driving behaviors, for example through fog, but fog scatters light differently than smoke does.

00:24:34.375 --> 00:24:40.528
So there are the perceptual properties of driving through smoke are different than through fog.

00:24:40.674 --> 00:24:42.923
And the other thing is that we know from basic research on.

00:24:43.796 --> 00:24:48.567
So what smoke does is that it's essentially what you see a little bit blurry, right.

00:24:48.567 --> 00:25:11.385
But basic research that in effect is that perceived speed of motion, so things passing by you, can be reduced if things get a little bit blurry, so that if they translate that into driving, that means if visibility is just a little bit reduced, maybe people will be driving actually fast because they think or they perceive themselves to be driving a little bit slower than they actually are.

00:25:11.385 --> 00:25:20.247
So we don't know the answer to that yet, but it's not immediately straightforward that, like as that, you know, like reduced visibility has to mean that people will immediately drive slower.

00:25:20.247 --> 00:25:28.666
Another really interesting I think that and that contributes to that fundamental diagram, is that we are really interested in how much space do people leave to the car driving in front of them.

00:25:28.666 --> 00:25:41.048
One of the key questions for the study that we conducted both at NRC and at Lund University is the headway that people leave to the car driving in front of them as a function of visibility.

00:25:41.694 --> 00:26:11.875
It's not trivial because we know, for instance, when we look at real-world data, it was at the beginning a bit counterintuitive, but we saw, for instance, that even with good visibility conditions during a vacation, some, overall the cars tend to slow down because we argue that maybe they're afraid to go into an accident, and the consequences of going into an accident during a wildfire are much higher than what you will have in a normal situation.

00:26:11.875 --> 00:26:30.585
So we need to unpack and understand how those variables have an influence against each other, because you know, on one hand you have maybe the urgency of the evacuation scenario, on the other hand, you have the visibility, which is reduced, and you have all this change in perception on while you drive.

00:26:30.585 --> 00:26:32.147
So it's tricky.

00:26:32.147 --> 00:26:35.398
It's not a simple question that we want to answer.

00:26:35.779 --> 00:26:36.642
Is this a bottleneck?

00:26:36.642 --> 00:26:40.568
Are roads having not enough capacity to support massive evacuations?

00:26:41.015 --> 00:27:05.701
Unfortunately, we see too often that sometimes it takes too long to evacuate communities, and I mean there is also the fact that sometimes parts of the road network become unavailable because of the risk linked to the presence of the wildfire, which means that maybe an already reduced capacity becomes even smaller, and then you have queuing.

00:27:05.701 --> 00:27:13.463
And we know that very often the worst consequences of wildfires are linked to delayed evacuations.

00:27:13.463 --> 00:27:14.948
That can occur in this scenario.

00:27:15.776 --> 00:27:24.670
So this is where drills come back in and, for example, some communities have dedicated evacuation routes that are only available when there's an evacuation.

00:27:24.670 --> 00:27:29.446
So those communities exist and, in theory, take pressure off the road network.

00:27:29.446 --> 00:27:33.375
However, that only works if people are actually using them right.

00:27:33.375 --> 00:27:46.247
And this goes back to this movement to the familiar that I mentioned earlier is that we need to understand how willing and likely people are to use familiar that I mentioned earlier is that we need to understand how willing and likely people are to use roads that they are not familiar with, for example.

00:27:46.674 --> 00:27:50.202
Another strategy which is adopted sometimes is lane reversal.

00:27:50.202 --> 00:27:58.178
So since everyone has to get out, even if you have two-way roads, you basically flip one of the two lanes so that you increase the capacity.

00:27:58.178 --> 00:28:04.762
But again, this involves a few traffic management issues that have to be planned and solved beforehand.

00:28:04.762 --> 00:28:09.846
It's not straightforward to change the direction of a lane in traffic.

00:28:10.295 --> 00:28:23.028
Yeah, and I can imagine you may have rescue services trying to use the opposite way, going into the fire, but in the same way as you would have firefighters climbing up the staircase when you evacuate, which is a similar problem.

00:28:23.028 --> 00:28:28.647
What, max, you've said about the familiarity, and I was thinking in my head about a scenario of evacuating myself.

00:28:28.647 --> 00:28:38.962
I'm driving through unfamiliar roads all the time because Google Maps is pointing me to ridiculous detours when I'm trying to reach my parents in the mountains, and every single time it's a different road.

00:28:38.962 --> 00:28:45.226
But I have a high trust in the capability of Google Maps not killing me, which perhaps is a bit too naive.

00:28:45.226 --> 00:28:48.942
But I wonder, in a mass evacuation you probably can also lose those services.

00:28:48.942 --> 00:28:54.365
So people are pretty much technologically blind as well, may not have access to those technologies.

00:28:54.365 --> 00:29:00.948
In that case, they can only follow the guidance from the rescue services and then perhaps the other ones around them.

00:29:00.948 --> 00:29:04.244
So that must be also a stressful factor in this process.

00:29:04.734 --> 00:29:07.275
I think it's important for planning ahead, right.

00:29:07.275 --> 00:29:09.423
So this is something I came up with.

00:29:09.423 --> 00:29:17.284
I can't remember who said it, but the cost of automation is the loss of skill, right, and this applies exactly to what you just described.

00:29:17.284 --> 00:29:22.567
We rely so much on these navigation-assisted devices because they're amazing, right, so they're so good.

00:29:22.567 --> 00:29:30.240
Like, if I drive from Ottawa to Prince Edward Island, where my in-laws live it's a 14-hour drive, I don't even have to think about it.

00:29:30.240 --> 00:29:42.949
So when you use that, all of a sudden there's this vacuum that emerges for both the end user, but also, maybe, for the people who need it, for the emergency response teams who need to know where people are, where people are driving.

00:29:43.295 --> 00:29:51.327
Especially in remote locations or, even worse, as I mentioned before, in locations where people are not familiar at all with the environment.

00:29:51.327 --> 00:30:03.469
I mean, if you think about Europe, you know Southern Europe is filled up with tourists in the summer and the areas where wildfires are often overlap with this, and you know many.

00:30:03.469 --> 00:30:11.595
Often nowadays people don't need to plan all the details of the routes beforehand because they over-rely on technology.

00:30:11.595 --> 00:30:21.432
So I think this is definitely something to consider and that's why, in general, the planning of evacuation has to take this into account.

00:30:21.432 --> 00:30:30.078
So we need to have contingency plans so that, if the technology fails, we have mechanisms to support those that need to evacuate.

00:30:30.605 --> 00:30:31.547
I like this direction.

00:30:31.547 --> 00:30:34.770
I mean, I like to distill the practical takeaways.

00:30:34.770 --> 00:30:59.372
Of course, simply studying the human evacuation at large is fascinating, but here you're distilling very important bits of knowledge the preparedness, contingency plans, different strategies, the things that may actually make the situation worse, like the loss of visibility that if it takes you six hours to evacuate a community at perfect visibility, it might take you 12 because people start driving slower right.

00:30:59.372 --> 00:31:06.454
So it's critical to have this knowledge beforehand so we can actually react to those tragic situations.

00:31:06.454 --> 00:31:12.107
And unfortunately we have cases in which hundreds of people die or dozens of people die in a single WE event.

00:31:12.107 --> 00:31:15.270
Hundreds of people die or dozens of people die in a single wee event.

00:31:15.270 --> 00:31:19.075
You've mentioned drills, so give me the juicy details.

00:31:19.075 --> 00:31:22.719
Tell me, how does one prepare for a community evacuation drill?

00:31:22.719 --> 00:31:25.842
What do you look for and how did it go?

00:31:25.842 --> 00:31:28.307
Perhaps let's start with the planning.

00:31:28.307 --> 00:31:30.814
How far ahead actually do you have to plan such a massive event?

00:31:30.814 --> 00:31:32.057
How many people are needed?

00:31:32.484 --> 00:31:33.529
So it takes a lot of time.

00:31:33.529 --> 00:31:39.394
As a research team team, we only are a very small part of this whole exercise, really.

00:31:39.394 --> 00:31:45.135
That, like from the implementation side, the really heavy lifting is done by the local community and the local emergency.

00:31:45.135 --> 00:31:49.335
So, and the community in roxborough park, they can't be praised enough for doing this.

00:31:49.335 --> 00:31:57.351
We've been looking for communities to do these types of drills and they are the only community that we are aware of who does these types of drills, and they are the only community that we are aware of who does these types of drills.

00:31:57.806 --> 00:32:03.712
Maybe there are other communities that are doing community evacuation drills, but these are the only ones that we are aware of.

00:32:03.712 --> 00:32:06.289
So the community that we observe is not a large community.

00:32:06.289 --> 00:32:18.150
These are maybe a thousand homes and maybe in a larger metro area, but it's compared to what the actual evacuations that we see in the news these days, it's a relatively small exercise Nonetheless.

00:32:18.150 --> 00:32:19.874
It's a massive undertaking, right?

00:32:19.874 --> 00:32:31.576
So we have to involve a number of different stakeholders, which takes a lot of effort to coordinate, and it often falls on individuals who are just willing to go the extra mile, right?

00:32:31.576 --> 00:32:42.244
And, yeah, the people in Roxborough Park and the Office of Emergency Management there they can't be praised enough for increasing the preparedness of their community by conducting these drills.

00:32:42.646 --> 00:32:50.885
And you know, we were a quite large team of researchers looking into this because, as I said, I'm pretty sure that there are maybe other communities that do this around the world.

00:32:50.885 --> 00:33:07.877
But to agree on having researchers being on site and looking what you do Because you know I totally understand that this is like an important commitment and if something goes wrong it's quite sensitive to have so many people looking at what you're doing.

00:33:07.877 --> 00:33:12.717
But I mean, we had NFPA that helped us coordinate in this with Amanda Kimball.

00:33:12.717 --> 00:33:17.913
That was really, you know, our link in the US with the Fire Protection Research Foundation.

00:33:17.913 --> 00:33:22.892
There was a team there at the NRC in Canada, In Lund, along with me.

00:33:22.892 --> 00:33:34.993
There were a couple of people that helped out to prepare for the data collection and also data analysis beforehand, because that's the thing you want to know beforehand what kind of data you can get hold of.

00:33:34.993 --> 00:33:41.494
In this way, you can optimize the data collection and something will always not go as you hope.

00:33:41.494 --> 00:33:49.048
I don't know whatever A battery in a drone that doesn't function as it should, or a traffic counter that malfunctioned.

00:33:49.048 --> 00:33:52.332
So you need to think about the kind of things that can go wrong.

00:33:52.332 --> 00:33:56.590
Time-wise, Max is correct if I'm wrong, but I think we plan this more than one year ahead.

00:33:57.153 --> 00:34:05.013
Yeah, we started looking into this one year before and, you know, started thinking about because there is all aspects also linked to ethics.

00:34:05.684 --> 00:34:16.393
Ideally, you know, we just install an app on everyone's phone, we track the GPS data, we get all the personal information and this will be probably the dream of a researcher.

00:34:16.545 --> 00:34:26.277
But real life tells us that this will not go through because of all the ethical concerns and the rules that we need to follow when we do this type of research.

00:34:26.706 --> 00:34:51.605
And that's why it's always a dialogue between the community and the researchers into what we can collect, what they feel, let's say, happy with being collected and, at the same time, let's say, giving something back, because it's not just us taking the data, but, down the line, we also want to provide information about how the event went and what can be improved, what lessons are learned.

00:34:51.605 --> 00:34:59.974
So the role for us as researchers is, of course, to collect the data that we can use in models, that we can use to understand your behavior.

00:34:59.974 --> 00:35:16.432
But I mean we also have this kind of societal role in which we want to give back to those communities to try to identify, for instance okay, where was a bottleneck in the evacuation, how this could be improved, what other issues there were, for instance, in communicating the alert, and things like this.

00:35:16.432 --> 00:35:21.016
So this is the type of exercise that we wish there would be more and more of this.

00:35:21.016 --> 00:35:30.476
I would say this community in Colorado was the first one that was very, very generous in allowing us to be observing data from their drill.

00:35:31.105 --> 00:36:12.856
And this buy-in of the community is really critical, right, and this is why we go through these rigorous steps, with our one thing to get the buy-in from the community or from the people who organize it, but we also need to convince individual participants to share the information with us, right, and this is something that's very important to us, and it's also something that we are required to do by the ethical cause of conduct that exists.

00:36:13.686 --> 00:36:21.331
So you mentioned that the community planned this, so it was, in a way, known to the community when it will happen, or was it like an announced drill?

00:36:22.086 --> 00:36:22.730
How does it look like?

00:36:22.730 --> 00:36:29.050
So this was an announced drill, so people could sign up for this drill to participate in that drill.

00:36:29.050 --> 00:36:39.634
We didn't tell them the exact time of day when it would happen, but they knew the date of the drill and obviously we know that announced drills are different than unannounced drills.

00:36:39.634 --> 00:36:46.791
But to get again, to get the spine and to be able to organize this, it was just more feasible to do an announced drill.

00:36:46.791 --> 00:36:55.795
Keep in mind only training and assessing the people who live there, the residents, but also the procedures for, for example, the first responders, right.

00:36:55.795 --> 00:37:05.195
So for that it is often go to, for this it's just more feasible to do this announced drill and I think, all in all, this can be.

00:37:05.195 --> 00:37:05.465
So.

00:37:05.505 --> 00:37:18.335
We haven't looked in the data into too much detail, but we already know that we can get some really valuable insights from that of things, as in regard, things will go wrong, both from the data collection side of things but also for the evacuation side of things, right?

00:37:18.335 --> 00:37:26.197
So just anecdotally, we noticed that some of the residents didn't receive the emergency notification although they had signed up for it.

00:37:26.197 --> 00:37:35.050
So because there was poor sales, nickelware sales signal where they happened to be, or some others started evacuating and left their community but then got lost in the neighboring community.

00:37:35.050 --> 00:37:48.775
So I mean this is all anecdotal, right, so we can't really tell yet if this is generalizable, but just from these little anecdotes you can see like this is really valuable information that we don't want to learn during an actual emergency.

00:37:48.775 --> 00:37:51.634
So for that this is really really valuable.

00:37:52.005 --> 00:37:53.873
And how big was the participation?

00:37:53.873 --> 00:37:56.092
Like did the entire community participate?

00:37:56.092 --> 00:37:56.454
I don't know.

00:37:57.572 --> 00:37:59.505
So this is, I think, about 10%.

00:37:59.505 --> 00:38:01.192
I don't have the exact numbers.

00:38:01.192 --> 00:38:09.637
So it's a community of roughly around 1,000 households and we roughly a little bit over 100 evacuated, which then translates into just under 200 vehicles.

00:38:10.125 --> 00:38:11.887
Okay, well, that's already a lot of vehicles.

00:38:12.487 --> 00:38:21.260
I just want to bring up that, you know, because the question of announced versus unannounced drill is something that often occurs for people like us that work in this domain.

00:38:21.260 --> 00:38:40.864
But I think it's important also to link what as much impact as other type of variables like, for instance, pre-evacuation.

00:38:40.864 --> 00:38:56.184
So, okay, if our main goal would be to study pre-evacuation alone, then yes, I will say probably an announced drill is not the optimal way of doing that, although, you know, for wildfire evacuation, often we don't really have any of that.

00:38:56.184 --> 00:38:58.634
So whatever data we have is better than no data.

00:38:59.005 --> 00:39:09.052
And Max already said that you also have some level of risk awareness, that the wildfire is nearby, that the fire index is high, that it's a dry season and so on.

00:39:09.052 --> 00:39:11.373
So it's not like you're surprised.

00:39:11.644 --> 00:39:17.615
Drills have two types of goals, right, so one, and depending on how you organize the drill, you can achieve one goal or the other.

00:39:17.615 --> 00:39:24.425
So the first goal of a drill is training right, you want to increase the preparedness of the building occupants or the residents, right.

00:39:24.425 --> 00:39:29.692
And the other one is assessment, where you want to figure out how the whole system works and where their weak points are.

00:39:29.692 --> 00:39:36.931
The training part of it you can do in an announced drill, right, because you can provide you know you can organize it a little bit better.

00:39:36.931 --> 00:39:45.199
You can still identify, provide people with feedback, you know, like maybe you know you didn't take the optimal route, or some things like that right.

00:39:45.199 --> 00:39:54.449
So that can be done in an announced drill and you can also still learn something about the performance of the overall system.

00:39:54.449 --> 00:40:06.773
So, yeah, I know that there's a discussion about the value of announced versus unannounced drills, but for this particular training scenario, I think there's definitely value in doing announced drills, particularly compared to doing nothing.

00:40:07.264 --> 00:40:14.849
And there is one more thing I would say that is an argument against unannounced drill in general, which is the so-called cry wolf effect.

00:40:15.070 --> 00:40:27.945
So you know the famous the boy who cries the wolf, the famous tale that we tell to our kids when you have many false alarms, actually you decrease the compliance to instruction.

00:40:28.585 --> 00:40:46.889
So I will argue that for a community it's actually better to train with announced drills, especially again when we look at those scales, because you don't feed that kind of cryo-wall effect but instead you just contribute to help preparedness.

00:40:46.889 --> 00:41:03.010
So this is like the kind of dilemma also as researchers again that we have because on one hand we want to improve the quality of our data, but on the other hand, I think the safety of each community and this applies also to building comes first.

00:41:03.010 --> 00:41:09.992
Because again, this we're talking about, this is very different than when we talk about experimental setups, where we put volunteers in a lab.

00:41:09.992 --> 00:41:14.514
They sign up for it from the beginning, they know that they are in a lab setup and so on.

00:41:14.514 --> 00:41:15.751
This is like on site.

00:41:15.751 --> 00:41:21.057
So we are going basically to the houses of people and to the places where people live.

00:41:21.057 --> 00:41:29.038
So the priority should remain the safety of people, not us researchers wanting to look them from the video source.

00:41:29.661 --> 00:41:32.389
So like people are generous with their time right.

00:41:32.389 --> 00:41:48.675
So if you ask someone hang on, like on this day we'll do a community evacuation pretty much any time during the day and we won't tell you when it happens and this person agrees to do that, that means that they like give you the whole day right, potentially to where they just have to participate in the drill.

00:41:48.675 --> 00:41:50.001
So it's well.

00:41:50.001 --> 00:41:50.563
This is great.

00:41:50.563 --> 00:41:56.114
For us as researchers, it's much more important that it's valuable to the people who participate in the drill.

00:41:57.041 --> 00:42:00.802
I think also in this discussion you can tie in the time factors In buildings.

00:42:00.802 --> 00:42:05.579
We're talking about our set times of minutes, you know, and announced versus unannounced.

00:42:05.579 --> 00:42:14.393
If people evacuate in one minute because they know they are supposed to evacuate at 5.03 versus people doing the pre-evac time, that can take 15 minutes.

00:42:14.393 --> 00:42:19.106
That that's like can make or break your evacuation exercise in here.

00:42:19.106 --> 00:42:24.885
I guess your time scales are also longer that allow for accommodate different behaviors and and different things.

00:42:24.885 --> 00:42:34.161
And, as you said, if you know what you're studying, you can isolate the variables that are impacted by the knowledge out of the variables that are just an outcome of people moving.

00:42:34.161 --> 00:42:35.443
So that's interesting.

00:42:35.443 --> 00:42:37.525
How about that mode of transport?

00:42:37.525 --> 00:42:39.025
You've mentioned that multiple times.

00:42:39.025 --> 00:42:45.373
Was this also a part of study, how people were choosing that or how they were using their vehicles and other tools to evacuate?

00:42:45.713 --> 00:42:50.757
No, so this was a community that was exclusively using their personal vehicles for evacuation.

00:42:50.757 --> 00:42:52.300
Personal vehicles, okay.

00:42:52.721 --> 00:42:59.188
You know there is this challenge, though we know that in a vacation, for instance, people do not want to leave vehicles behind.

00:42:59.188 --> 00:43:03.148
So if you have two cars when I take both of your cars you're not going to leave.

00:43:03.519 --> 00:43:08.500
So if you're two people at home, you're not going to leave one of the car behind knowing it's going to burn down.

00:43:08.500 --> 00:43:22.876
So when it comes to transportation choice, it's not only what mode you will use, but also basically how many vehicles your household will try to go to move with.

00:43:22.876 --> 00:43:29.942
Another example you know especially, you can think of people like trailers.

00:43:29.942 --> 00:43:40.170
We saw it when we look at real world data that the average length of vehicles in wildfires and evacuation were longer than what you have in normal routine traffic.

00:43:40.170 --> 00:43:50.458
Simply because if you have a camper or a trailer or something and fortunately the type of databases that are publicly available they give you this information You're not going to leave it there.

00:43:50.458 --> 00:43:55.550
You're going to try to carry as much stuff that you have with you to minimize your property loss.

00:43:56.273 --> 00:43:58.788
Okay, and my final question where does it go?

00:43:58.788 --> 00:44:02.451
Are you building a transportation model for evacuations?

00:44:02.451 --> 00:44:05.909
What's the future with this data that you now get?

00:44:05.909 --> 00:44:07.182
So the purpose?

00:44:07.262 --> 00:44:18.284
of this drill for a greater number of things that we wanted to achieve with this drill, right, so I think three things wanted to achieve with this drill.

00:44:18.284 --> 00:44:18.945
I think three things.

00:44:18.945 --> 00:44:24.403
The first thing was to test this movement with the familiar hypothesis, because the setup of the computer allowed that, to test that and the nature of the drill.

00:44:24.403 --> 00:44:38.248
The second thing is that we used a number of different data collection methods and we wanted to test, see how comparable these different data sources are from using an app an observer using a drone and self-reported data just to get a comparison.

00:44:38.460 --> 00:44:40.559
And the last thing is exactly what you just mentioned.

00:44:40.559 --> 00:44:47.891
So we are hopefully using this data from the community to inform the model development.

00:44:47.891 --> 00:45:07.148
So, for example, folks at Lund University and Ipswich College, they work with us on this modeling tool called MooNichi Very hard to pronounce with a German accent, but any type of computer simulation needs data for validation right, and hopefully the data that we're collecting, that we collected in that drill, can be used as a benchmark of certain things in our model.

00:45:08.021 --> 00:45:25.327
The mission, the long-term mission, is very much linked to what you said earlier to basically create wildfire safety engineers, because we will reach a point in which we can do full performance-based design of we communities.

00:45:25.909 --> 00:45:28.817
And to do that we need all the pieces of the puzzles.

00:45:28.876 --> 00:45:32.505
We need the fire models, of course, we need those for wildfire.

00:45:32.505 --> 00:45:39.786
We need the trigger boundary models, the work that Nick, guillermo, harry, the colleagues at Imperial are doing.

00:45:39.786 --> 00:45:48.427
But we also need to calculate our WR set and this is what we can do with the evacuation simulation tools.

00:45:48.427 --> 00:46:03.264
They will have to deal with different scales and different modes of transport, and this is what the efforts like Woonity are trying to do try to merge both what happens inside the buildings and also outside the building, on the road, and it's not easy.

00:46:03.264 --> 00:46:09.059
We know that we have many years ahead of us to make those tools valid and reliable.

00:46:09.059 --> 00:46:30.992
But I think this could be a game changer for the whole wildfire safety world, because it's obvious that the scale of the problem worldwide it's going to become bigger and bigger and you know, at some point I think the role of wildfire safety engineer will be even more common than the one on building fire safety engineering.

00:46:30.992 --> 00:46:32.025
It will take some years.

00:46:32.025 --> 00:46:36.447
It will not happen tomorrow, but we have all the tools to get there.

00:46:36.989 --> 00:46:40.487
If I could bet money, I would put a lot of money on that actually, Max.

00:46:40.768 --> 00:46:42.643
No, I was just about to say like.

00:46:42.643 --> 00:46:50.467
One thing that always strikes me about, particularly about the wide web, is how interdisciplinary it is, which makes it complex, right?

00:46:50.467 --> 00:47:04.755
So this is one of the barriers that we need to overcome is, is that like that, we are able to get the information from many different disciplines and bring them together, translate the jargons from each discipline into something that is commonly understood?

00:47:04.755 --> 00:47:10.666
I don't know, like I'm a psychologist, so, like I think you know, we need to understand human behavior and the decision making that people make.

00:47:10.666 --> 00:47:12.858
We need to understand the traffic patterns.

00:47:12.858 --> 00:47:14.222
We need to understand the fire movement.

00:47:14.222 --> 00:47:16.266
We need to understand the traffic patterns.

00:47:16.266 --> 00:47:17.128
We need to understand the fire movement.

00:47:17.128 --> 00:47:19.474
We need to understand how complex systems dynamically evolve.

00:47:19.474 --> 00:47:21.460
We need to be able to optimize those systems.

00:47:21.519 --> 00:47:54.431
So these are all really complex questions that can't be solved by one type of background alone, or it would require a completely new and unique background and, wojciech, my feeling is that also, let's say, from the funding agency's side, there is more and more willingness and awareness of the problem because we saw it over the years, we were funded by NIS, we were funded by the Canadian government in this project we have, you know, our collaborators got funding in the UK, in Imperial, or in Australia, with Erica Kouligos.

00:47:54.431 --> 00:48:06.427
So it's like it seems like for us researchers, there is in this space, there is more and more awareness of the importance of looking into the topic of wildfire safety.

00:48:07.009 --> 00:48:15.050
Perhaps it's not nice what I'm about to say, but you know, the wildfires became in some way a poster child of climate change.

00:48:15.050 --> 00:48:19.487
They are used as a direct evidence of climate change.

00:48:19.487 --> 00:48:27.510
You know, we have a rising threat from wildfire and the climate change research funding is already at high fault.

00:48:27.510 --> 00:48:33.224
So so, in a way, we're also to some extent benefited of this, of this how to call it trend.

00:48:33.224 --> 00:48:36.724
You know that there are trends in science and trends in funding, for sure.

00:48:36.724 --> 00:48:45.407
So so I I believe, in a way we've kind of entered the mainstream by being a part partly associated with climate change.

00:48:45.528 --> 00:48:47.572
I say this myself and yermo.

00:48:47.572 --> 00:48:50.846
We had, let's say, a tragic laugh at this.

00:48:50.846 --> 00:49:10.181
But you know, 10 years ago, when me and guillermo had the first chat about doing research on wildfire together, we said, okay, let's think about an evacuation scenario, 100 000 people and I always say this, this was seen as sci-fi just 10 just 10 years ago and now these things happen.

00:49:10.181 --> 00:49:12.425
So, uh, 100 000 people.

00:49:12.425 --> 00:49:15.552
I mean, with fort McMurray we had like almost 90,000.

00:49:15.552 --> 00:49:16.400
Great yeah.

00:49:16.481 --> 00:49:33.108
So these things happen, like in this context is that wildfire, at least in many parts of the world, have always been around and have been part of the natural ecosystems, and it's just now that they're becoming more and more of a challenge for, like, in the way we live.

00:49:33.108 --> 00:49:34.532
Is that is the new thing?

00:49:34.532 --> 00:49:37.726
Maybe, right, so they become, fires are becoming larger and more frequent.

00:49:37.726 --> 00:49:56.760
Um, yeah, but always been around, and I think the other thing that I that might be worth adding is that, you know, like, while this, this is really global, right, so there are wildfires in areas that never had never had real problems with wildfires in areas that had never had real problems with wildfires in the past.

00:49:56.760 --> 00:50:20.427
The nature of our project is that folks from Australia, north America, europe, from really diverse types of places, so there are generalizable patterns, but the wildfire itself is often always super important to understand the local context, and that's something that is really really challenging, because you can't really immediately translate the finding that you see from evacuating a community in Sweden to a community in Canada, for example.

00:50:20.920 --> 00:50:23.228
Okay, guys, I guess we'll end on this note.

00:50:23.228 --> 00:50:27.030
So any final words to the future wildfire engineers.

00:50:27.030 --> 00:50:30.963
Enrico, maybe you want to encourage people to study this Well.

00:50:32.132 --> 00:50:33.239
I have two messages.

00:50:33.802 --> 00:50:56.112
The first one if there is any community, if any fire managers I know your audience is very diverse, wojciech- If any fire manager or anyone that is interested in wildfires plans to do an evacuation drill at that scale, to reach out to people like me and Max, because we are ready to come there and record data, because it's not so easy to get hold of this data.

00:50:56.112 --> 00:51:18.648
And the second thing is to not be scared to those, let's say, the run big companies that work in the fire safety engineering domain to invest money in wildfire research and to invest resources, because I feel like that those companies and those organizations that will be ahead in this will have a tremendous advantage.

00:51:18.648 --> 00:51:27.978
In the moment we will have, wildfire safety engineering, as, let's say, will become the new default, along with building fire engineering.

00:51:28.461 --> 00:51:32.628
One last thing to add is that thinking of this as an interdisciplinary problem.

00:51:32.628 --> 00:51:44.927
It affects everyone many people, many disciplines, and that means working interdisciplinary, which can be challenging because you have to talk to people who have a different background than yourself, and that is challenging but also enriching.

00:51:45.307 --> 00:51:46.603
Fantastic Guys.

00:51:46.603 --> 00:51:48.309
You're doing a great job promoting this.

00:51:48.309 --> 00:51:50.204
You're doing fantastic research.

00:51:50.204 --> 00:51:53.608
Keep going and we will meet here again.

00:51:53.608 --> 00:51:54.804

161 - Community evacuation with Enrico Ronchi and Max Kinateder

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