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This paper presents a mass evacuation simulator capable to handle complex cognitive agents in large urban areas considering sub-meter details of the environment. Details of the evacuation simulation software in the context of dynamical systems provide a common ground for the comparison with other evacuation simulation tools and a software specifica...
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... represent local residents of the region of study. It is assumed that they know the surroundings and that they are able to plan their way to the nearest evacuation area. Fig. 1 provides a sketch on the implementation of a resident agent, f resident . Resident's think is composed by g find way out , g navigate , g find inter and g coll av . Act is composed of g execute actions and g update . s int is provided with a topological map of the environment, which is used for gathering past experiences, planning ...
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... fictional setting is a 6km  6:5km area of a coastal city in Japan, see Fig. 10. 40,000 evacuees are considered with the properties shown in Table 3. The tsunami arrival time is assumed to be 40 min as observed during the 2011 Great East Japan Earthquake and Tsunami which struck that region. The pre-evacuation time, the time it takes for an evacuee to start evacuating after the first earthquake shock is assumed to ...
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... robustness of the results is evaluated through the use of Monte-Carlo simulations. Fig. 11(a) shows the convergence of the standard deviation of the results with the number of draws/ ...
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... about 600 simulations the standard deviation has already converged this contrasts with the pedestrian-only simulations where convergence was achieved after 400 simulations, see Fig. 4. Fig. 11(b) shows the mean and the standard deviation of the results of the cumulative number of agents evacuated with 1000 MonteCarlo simulations. The zoomed in the box on the upper right corner shows a standard deviation of 0.42% in the throughput after 40mins and in the lower right corner 0.29% after 25mins. Additionally, Fig. 11(c) shows the ...
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... see Fig. 4. Fig. 11(b) shows the mean and the standard deviation of the results of the cumulative number of agents evacuated with 1000 MonteCarlo simulations. The zoomed in the box on the upper right corner shows a standard deviation of 0.42% in the throughput after 40mins and in the lower right corner 0.29% after 25mins. Additionally, Fig. 11(c) shows the convergence of a more sensitive measure, the number of agents evacuated at each 10 s interval. The rest of the graphs present a single simulation result or the mean of 100 simulations; 600 simulations are not conducted for each scenario to reduce the computational resources used for this ...
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... first set of simulations explore a scenario where anyone, irrespective of their physical abilities, is allowed to use vehicles. Fig. 12(a), compares the evacuation throughput with different percentages of evacuees using cars. It can be observed that as the percentage of evacuees using cars increases the total throughput after 40 min of evacuation decreases. There is an initial boost in the evacuation throughput especially during the first 25 min of ...
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... results obtained by restricting the use of cars can be seen in Fig. 12 (b). It can be seen that by applying this restriction an improvement in the total evacuation throughput of about 7% over the base scenario is achieved when allowing 25% of the population (prioritizing people in need) to use cars. Additionally, simulations with up to 50% of car usage under this policy show little impact on the total ...
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... scenario explores the case of vehicle users being persuaded to start to evacuate earlier. The aim is to mitigate the effect of the car queue formation. This is implemented by considering an earlier mean to the pre-evacuation time distribution of evacuees using cars. Fig. 13 shows that persuading the cars to evacuate earlier creates a longer lasting initial boost in the throughput with a marginal increase in the total throughput. Still, long car queue formations are ...
Citations
... are based on ABM to achieve more realistic evacuation simulations and can be run sequentially, with each simulation corresponding to a different evacuation scenario [30, 31,32]. The work in [30] simulates the evacuation of 300 agents from a building on fire. ...
... They simulate a maximum of 951 occupants. The work in [32] simulates 145 the evacuation of 57,000 agents from a coastal city in Japan. The authors do not provide technical details about the software implementation, neither the platform used to run the experiments. ...
... We assume that they know their surroundings and can plan their way to the nearest safe area because they are periodically trained throughout evacuation drills coordinated by public organizations. Visitor agents represent people who have restricted information about the environment [32]. Because of this, our model assumes that they do not know the escape routes. ...
Evacuation plans in seacoast areas are essential for conducting people to secure zones in a timely manner. Typically, evacuation plans are based on the experience of previous evacuation drills, which are expensive processes that require coordination, planning and the collaboration of different institutions and people. During evacuation drills it is difficult to obtain all the data required to analyze the situation and additionally, it is difficult to detect all possible threatening situations. Computer simulations can be used to run evacuation models for evaluating different evacuation scenarios. However, developing realistic simulations is a complex task. Moreover, large simulation models considering many thousands of people demand a high computational cost and thereby, the simulation of different evacuation plans can become a highly time-consuming task. In this work, we present an approach to model and simulate the behavior of people in mass evacuations of seacoast areas. Our proposal aims to improve the computational efficiency of the calculations performed without compromising the quality of results by means of parallel computing. The simulation model divides the geographic area in cells of fixed sizes. Then, to reduce the amount of calculations performed in each simulation timestep, for each simulated agent we compute a mobility model by considering only the agents placed in the closest neighboring cells. The proposed simulation model achieves realistic results by combining geographic data, public census data, the density of the population, the surrounding view of each person and disaggregation by age groups. This reduces the error in decision making and allows a proper estimation of the distance of groups of people that cannot arrive at safe areas. The respective simulator has been implemented using agent-based programming in C++ and OpenMP. The simulation model was evaluated by performing experimentation on actual data collected from the Chilean cities of Iquique and Viña del Mar, and the city of Kesennuma in Japan.
... For comparison, the same population size is considered for both scenarios, and the worst-case scenario for population n e = 15, 000 is selected. Compared to the daytime, due to many factors such as the low lighting conditions, being tired, etc. during the nighttime, evacuees may make different decisions and behave differently, e.g., more people have the following behavior (Jacob et al. 2014), late to start the evacuation, the pedestrian speed is slower (Aguilar et al. 2019), etc. Also, the population distribution across the three sub-areas (i.e., the beach, downtown, and residential area) would be different due to the difference of the time. ...
... Due to the poor visibility caused by the low lighting condition at night, the following differences are considered between the daytime scenario and Nighttime1: (1) the evacuee's sight distance (i.e., sight distance-evacuee in Table 4.2) decreases from 50 m to 30 m (Aguilar et al. 2019) and the distance within which the pedestrian can see the tsunami inundation (i.e., sight distancetsunami in Table 4.2) decreases from 200 m to 50 m; (2) the effect of the poor visibility is also modeled by decreasing the preferred pedestrian speed (i.e., v p 0 ) to 80% of that in daytime based on the study in Ouellette and Rea (1989). In the pedestrian speed-density model shown in Fig. 3.8, the three speeds, i.e., 0.75, 1.5, and 3.83 (unit: m/s) decrease to 0.6, 1.2, and 3.06 (unit: m/s); (3) the two scaling factors (scaling the preferred pedestrian speed in the pedestrian speed-density model, ...
... ;Aguilar et al. (2017Aguilar et al. ( , 2019). The reroute behavior due to the traffic congestion would in turn change the nearby traffic congestion level and further impact evacuees' traveling speeds; (3) pedestrians are considered to accelerate when the tsunami inundation comes closer. ...
Earthquake-induced tsunami can be very destructive involving significant loss of life. Evacuation to safety zones is regarded as one of the most effective ways to save lives from the tsunami strike due to the limited effectiveness of structural countermeasures. However, it is extremely challenging to successfully evacuate many people under the multi-hazard environment within a condensed time frame, especially under the near-field tsunami. Proper evacuation planning is crucial to support effective evacuation and reduce casualty. For effective evacuation planning, it is important to better understand the complex evacuation behavior for recommending proper response and behavior in an emergency. Also, it is important to have a clear picture of evacuation risk (e.g., measured in terms of expected casualty rate within a certain time frame) for informing policy and decision-making. Furthermore, it is important to identify effective pre-event mitigation strategies for effective risk reduction.
Important limitations exist in current research on the above aspects. Tsunami evacuation simulation using the agent-based model has been used to investigate the complex evacuation behavior; however, existing agent-based evacuation models usually neglect or simplify many important factors and/or mechanisms associated with the evacuation. The neglect or simplification would make the evacuation simulation less realistic and hence a good understanding of evacuation behavior challenging. For the quantification of tsunami evacuation risk, a systematic framework that can address complex evacuation models and uncertainty (including aleatory and epistemic uncertainties) models is needed; however, no such framework has been developed for the quantification of tsunami evacuation risk. Also, some important uncertainties such as that in the seismic damage to the bridge are usually neglected or the uncertainty quantification is simplified. In this case, it would be difficult to assess the evacuation risk accurately and provide a clear picture of the evacuation risk. For effective pre-event evacuation risk mitigation, the effectiveness of different mitigation strategies needs to be quantitatively evaluated to identify more effective strategies. However, the effectiveness of the mitigation strategy is usually evaluated more qualitatively than quantitatively. Furthermore, the evaluation is typically conducted without systematically considering various uncertainties, which makes the identified strategies not robust to uncertainties. In tsunami evacuation risk assessment and mitigation, risk evaluation using general stochastic simulation techniques (e.g., Monte Carlo simulation) typically entails significant computational challenges. Efficient algorithms are needed to alleviate such computational challenges and facilitate such tasks.
To bridge the above knowledge gaps, this research proposes a generalized framework for simulation-based tsunami evacuation risk assessment and risk-informed mitigation. The framework is built layer by layer through integrating tsunami evacuation simulation using agent-based modeling (ABM) technique, simulation-based evacuation risk assessment, sensitivity analysis of evacuation risk, and risk-informed evaluation of mitigation strategies. An improved agent-based tsunami evacuation model is developed for more realistic tsunami evacuation simulation by incorporating many of the typically neglected or simplified but important factors and/or mechanisms in the evacuation. Using the proposed agent-based evacuation model, a simulation-based framework is proposed to quantify the evacuation risk, in which various uncertainties (including aleatory and epistemic uncertainties) associated with the evacuation are explicitly considered and modeled by proper selection of probability distribution models. Sensitivity analysis of evacuation risk with respect to the epistemic uncertainty is performed, and the sensitivity information can be used to guide effective epistemic uncertainty reduction and hence for more accurate risk assessment. Also, sensitivity analysis is performed to identify critical risk factors, and the sensitivity information can be used to guide effective evacuation modeling and selection of candidate risk mitigation strategies. Risk-informed evaluation of different types of candidate mitigation strategies (including infrastructural and non-infrastructural strategies) is conducted to identify more effective strategies that are robust to uncertainties. Efficient sample-based approaches are developed to alleviate the computational challenges in evacuation risk assessment, sensitivity analysis, and risk-informed evaluation of mitigation strategies. As an illustrative example, the proposed framework is applied to tsunami evacuation risk assessment and risk-informed mitigation for the coastal community of Seaside, Oregon.
... Remote Sens. 2022,14, 3095 ...
Smart Cities already surround us, and yet they are still incomprehensibly far from directly impacting everyday life. While current Smart Cities are often inaccessible, the experience of everyday citizens may be enhanced with a combination of the emerging technologies Digital Twins (DTs) and Situated Analytics. DTs represent their Physical Twin (PT) in the real world via models, simulations, (remotely) sensed data, context awareness, and interactions. However, interaction requires appropriate interfaces to address the complexity of the city. Ultimately, leveraging the potential of Smart Cities requires going beyond assembling the DT to be comprehensive and accessible. Situated Analytics allows for the anchoring of city information in its spatial context. We advance the concept of embedding the DT into the PT through Situated Analytics to form Fused Twins (FTs). This fusion allows access to data in the location that it is generated in in an embodied context that can make the data more understandable. Prototypes of FTs are rapidly emerging from different domains, but Smart Cities represent the context with the most potential for FTs in the future. This paper reviews DTs, Situated Analytics, and Smart Cities as the foundations of FTs. Regarding DTs, we define five components (physical, data, analytical, virtual, and Connection Environments) that we relate to several cognates (i.e., similar but different terms) from existing literature. Regarding Situated Analytics, we review the effects of user embodiment on cognition and cognitive load. Finally, we classify existing partial examples of FTs from the literature and address their construction from Augmented Reality, Geographic Information Systems, Building/City Information Models, and DTs and provide an overview of future directions.
... Therefore, only 3 models in this category propose to simulate both car and pedestrian mobility. Thus, Aguilar et al. (2019) propose to represent both pedestrian and car traffic using a model based on the calculation of a collision-free velocity along a graph. Few details are given about the precise calculation of this velocity and the model used. ...
At a time when the impacts of climate change and increasing urbanization are making risk management more complex, there is an urgent need for tools to better support risk managers. One approach increasingly used in crisis management is preventive mass evacuation. However, to implement and evaluate the effectiveness of such strategy can be complex, especially in large urban areas. Modeling approaches, and in particular agent-based models, are used to support implementation and to explore a large range of evacuation strategies, which is impossible through drills. One major limitation with simulation of traffic based on individual mobility models is their capacity to reproduce a context of mixed traffic. In this paper, we propose an agent-based model with the capacity to overcome this limitation. We simulated and compared different spatio-temporal evacuation strategies in the flood-prone landlocked area of the Phúc Xá district in Hanoi. We demonstrate that the interaction between distribution of transport modalities and evacuation strategies greatly impact evacuation outcomes. More precisely, we identified staged strategies based on the proximity to exit points that make it possible to reduce time spent on road and overall evacuation time. In addition, we simulated improved evacuation outcomes through selected modification of the road network.
... However, unlike in computer science where the computation of an optimal path relies on having a complete representation of the environment, human wayfinding in unfamiliar buildings involves a gradual discovery of the space from an ego-centric perspective, one step at a time [26][27][28]. This process forces one to navigate on the basis of partial information, and subsequently become prone to erroneous decisions [8,[28][29][30]. Consequently, occupants who perform wayfinding in unfamiliar buildings often present substantial deviations from the shortest or fastest route [8,17,31]. ...
... In contrast, cognitive modeling seeks to model the process of wayfinding by modeling the interplay between cognitive processing mechanisms (i.e., knowledge in the head, [16]) and information in the environment (i.e., knowledge in the world, [16]). In the context of agent-based modeling in social science and cognitive robotics these models are referred to as 'cognitive agents' [58,59], whereas in computer graphics and artificial intelligence they are often referred to as 'intelligent agents' [30,60]. In social science, the motivation for developing cognitive agents is to formalize theories and test existing and novel hypotheses [30], which usually involves comparing agents' behavior against human observations [61]. ...
... In the context of agent-based modeling in social science and cognitive robotics these models are referred to as 'cognitive agents' [58,59], whereas in computer graphics and artificial intelligence they are often referred to as 'intelligent agents' [30,60]. In social science, the motivation for developing cognitive agents is to formalize theories and test existing and novel hypotheses [30], which usually involves comparing agents' behavior against human observations [61]. In robotics, cognitive models are implemented in cognitive robots to support wayfinding in real-world environments, facing the need to simulate wayfinding in an accurate manner given a dynamic and complex environment [62]. ...
Current approaches to simulate occupants' wayfinding in AEC mostly employ direct routing algorithms that assume global knowledge of the navigation environment to compute a shortest path between two locations. This simplification overlooks evidence concerning the role of perception and cognition during wayfinding in complex buildings, leading to potentially erroneous predictions that may hinder architects' ability to design wayfinding by architecture. To bridge this gap, we present a novel simulation paradigm entitled Cognitive Occupancy Modeling in BIM to simulate wayfinding by means of a vision-based cognitive agent and a semantically-enriched navigation space extracted from BIM (Building Information Modeling). To evaluate the predictive power of the proposed paradigm against human behavior, we conducted a wayfinding experiment in Virtual Reality (VR) with 149 participants, followed by a series of simulation experiments with cognitive and direct routing agents. Results highlight a significant correspondence between human participants' and cognitive agents' wayfinding behavior that was not observed with direct routing agents, demonstrating the potential of cognitive modeling to inform building performance simulations in AEC.
... In this study, we demonstrate the efficiency and effectiveness of signs that indicate different directions depending on the location of a hazard during a fire evacuation. This research paves the way for future work on multi-user frameworks that can account for crowd dynamics in public spaces during large-scale disasters (Helbing et al., 2007;Aguilar et al., 2019). Crowds may increase the amount of time required for route computations as each individual agent attempts to optimize their own escape. ...
In the event of fires and other hazards, visual guidance systems that support evacuation are critical for the safety of individuals. Current visual guidances for evacuations are typically non-adaptive signs in that they always indicate the same exit route independently of the hazard’s location. Adaptive signage systems can facilitate wayfinding during evacuations by optimizing the route towards the exit based on the current emergency situation. In this paper, we demonstrate that participants that evacuate a virtual museum using adaptive signs are quicker, use shorter routes, suffer less damage caused by the fire, and report less distress compared to participants using non-adaptive signs. Furthermore, we develop both centralized and decentralized computational frameworks that are capable of calculating the optimal route towards the exit by considering the locations of the fire and automatically adapting the directions indicated by signs. The decentralized system can easily recover from the event of a sign malfunction because the optimal evacuation route is computed locally and communicated by individual signs. Although this approach requires more time to compute than the centralized system, the results of the simulations show that both frameworks need less than two seconds to converge, which is substantially faster than the theoretical worst case. Finally, we use an agent-based model to validate various fire evacuation scenarios with and without adaptive signs by demonstrating a large difference in the survival rate of agents between the two conditions.
Experiments as Code (ExaC) is a concept for reproducible, auditable, debuggable, reusable, & scalable experiments. Experiments are a crucial tool to understand Human-Building Interactions (HBI) and build a coherent theory around it. However, a common concern for experiments is their auditability and reproducibility. Experiments are usually designed, provisioned, managed, and analyzed by diverse teams of specialists (e.g., researchers, technicians, engineers) and may require many resources (e.g., cloud infrastructure, specialized equipment). Although researchers strive to document experiments accurately, this process is often lacking. Consequently, it is difficult to reproduce these experiments. Moreover, when it is necessary to create a similar experiment, the “wheel is very often reinvented”. It appears easier to start from scratch than trying to reuse existing work. Thus valuable embedded best practices and previous experiences are lost. In behavioral studies, such as in HBI, this has contributed to the reproducibility crisis. To tackle these challenges, we propose the ExaC paradigm, which not only documents the whole experiment, but additionally provides the automation code to provision, deploy, manage, and analyze the experiment. To this end, we define the ExaC concept, provide a taxonomy for the components of a practical implementation, and provide a proof of concept with an HBI desktop VR experiment that demonstrates the benefits of its “as code” representation, that is, reproducibility, auditability, debuggability, reusability, & scalability.
Typically, tsunami evacuation routes are marked using signs in the transportation network and the evacuation map is made to educate people on how to follow the evacuation route. However, tsunami evacuation routes are usually identified without the support of evacuation simulation, and the route effectiveness in the reduction of evacuation risk is typically unknown quantitatively. This study proposes a simulation-based and risk-informed framework for quantitative evaluation of the effectiveness of evacuation routes in reducing evacuation risk. An agent-based model is used to simulate the tsunami evacuation, which is then used in a simulation-based risk assessment framework to evaluate the evacuation risk. The route effectiveness in reducing the evacuation risk is evaluated by investigating how the evacuation risk varies with the proportion of the evacuees that use the evacuation route. The impacts of critical risk factors such as evacuation mode (for example, on foot or by car) and population size and distribution on the route effectiveness are also investigated. The evacuation risks under different cases are efficiently calculated using the augmented sample-based approach. The proposed approach is applied to the risk-informed evaluation of the route effectiveness for tsunami evacuation in Seaside, Oregon. The evaluation results show that the route usage is overall effective in reducing the evacuation risk in the study area. The results can be used for evacuation preparedness education and hence effective evacuation.
