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The timely and secure evacuation of an urban residential community is crucial to residents’ safety when emergency events happen. This is different to evacuation of office spaces or schools, emergency evacuation in residential communities must consider the pre-evacuation time. The importance of estimating evacuation time components has been recogniz...
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... goal is to predict based on the classification models with the variable . The training and classification process of the RF algorithm includes three steps shown in Figure 2: ...
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... rejecting the unqualified questionnaires which lacked logicality and content integrity, 379 valid questionnaires were obtained, including 192 interviewed questionnaires and 187 electronic questionnaires. The data acquired from these questionnaires were set as the original dataset of RF (Figure 2) to predict the pre-evacuation times. The pre-evacuation behaviors were categorized into six classes in this study (Table 3). ...
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... The response of the local authorities and first responders is also considered important in determining the behavior of residents during an evacuation. Pre-evacuation warnings and explicit instructions from emergency personnel can help evacuees make thoughtful decisions about the fire risk and safely depart the affected area [ [15], [16]]. Additionally, the population's demographics, including household size, income, education level, ownership of a car or home, ethnicity, and previous experience with mass evacuations, can have a significant impact on the evacuation rate [17], [18] In the context of wildfire evacuations, the integration of connected and autonomous vehicles (CAVs) represents a promising advancement that can significantly enhance the evacuation process. ...
This research addresses the urgent need for enhanced emergency evacuation strategies in the MPC region during natural disasters, particularly wildfires, by capitalizing on data from CAVs. Leveraging a dataset from connected vehicles, the study evaluates driving behavior and traffic conditions during wildfire evacuations, providing crucial insights for disaster response. Furthermore, it investigates the role of CAVs in disaster management and assesses public attitudes toward their integration in a medium-sized metropolitan area with cold weather. This research offers a data-driven foundation for optimizing emergency evacuation plans and underscores the potential of CAVs in improving disaster response, highlighting the importance of public perception in realizing this potential
... The response of the local authorities and emergency responders is also considered crucial in defining the evacuation behavior of the residents. It has been observed that the issuance of pre-evacuation warnings and clear instructions from emergency officials can help evacuees to make contemplative decisions about the fire risk and leave the affected area safely [31][32][33][34][35][36]. The traffic infrastructure and population density of the area impacted by the fire are also regarded as significant to the evacuation effort. ...
... Evacuation time is an important index in quantifying pedestrian evacuation efficiency [31,32]. The main quantitative methods can be divided into two categories at present: mathematical models and computer simulations [33]. ...
Pedestrian evacuation is an important measure to ensure disaster safety in central retail districts, the efficiency of which is affected by the synergy of road network planning indices. This paper established the typical forms of central retail district (CRD) road networks in terms of block form, network structure and road grade, taking China as an example. The experiment was designed using the orthogonal design of experiment (ODOE) method and quantified the evacuation time under different road network planning indices levels through the Anylogic simulation platform. Using range and variance analysis methods, the synergy of network density, network connectivity, road type and road width on pedestrian evacuation efficiency were studied from the perspectives of significance, importance and optimal level. The results showed that the type of CRD will affect the importance of network planning indices, and that the network connectivity is insignificant (P 0.477/0.581) in synergy; networks with wide pedestrian primary roads (30.1~40.0 m), medium secondary roads (3.1~5.0 m/side) and high density (11.0~13.0 km/km2) have the highest evacuation efficiency. From the perspective of evacuees, this paper put forward urban design implications on CRD road networks to improve their capacity for disaster prevention and reduction.
... The response of the local authorities and first responders is also considered important in determining the behavior of residents during an evacuation. Pre-evacuation warnings and explicit instructions from emergency personnel can help evacuees to make thoughtful decisions about the fire risk and safely leave the affected area [13,14]. Additionally, the population's demographics, including the size of the household, income, level of education, ownership of a car or home, ethnicity, and previous experience with mass evacuations can have a significant impact on the evacuation rate [15,16]. ...
With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.
... For example, Bernardini et al. (2019) had a chance to analyze videotapes of historical earthquakes in three different countries (New Zealand, Italy and Japan) to capture real-life human behaviors. However, as real-world data in this context is very limited, many studies used hypothetical surveys, controlled experiments and self-reporting [33,34] and technologies such as serious games [35]. ...
Earthquakes can cause severe damage to structural and non-structural elements of buildings; consequently, they pose high risks to human lives. To mitigate such risks, attention has been paid to enhancing the indoor environment for increased building safety. Yet little effort has been made to assess a building occupants' evacuation behaviors in response to damage to the indoor environment. This paper addresses this issue with a novel simulation framework that couples human behaviors with changes to the indoor building environment during post-earthquake evacuation. In particular, we present a building information modelling (BIM)-based prototype that simulates seismic damage to the non-structural indoor elements and visualizes its impacts on evacuation using a color-coded heat map. The simulated damage is then used as input to an agent-based model for post-earthquake evacuation. Using a probabilistic method to assess the non-structural elements' damage states, we are able to evaluate the impact of indoor damage on the evacuation process. We performed a trial of our prototype for a hypothetical earthquake in an educational building. The results revealed how the average evacuation time would increase as the earthquake intensity increases (from 38.6 s for the no-damage scenario to 122.9 for the highest-damage scenario). The proposed prototype has the potential to be joined with other tools, such as finite-element-based simulation, to incorporate structural analysis as well. Planners and designers can explicitly use our model's output to analyze the post-earthquake evacuation with the indoor non-structural damage to assess different building design geometries that increase the chances of a suitable evacuation process.
... For example, Japan formulated the Law on Support for the Reconstruction of the Lives of the Affected in 1998, while China enacted the Destructive Earthquake Emergency Regulation in 1995. In its current stage, research is mainly focused on the location, allocation, accessibility, and evaluation of emergency shelters and the prediction and simulation of evacuation behaviours [14][15][16][17]. ...
... Also, a method is unable to increase the accuracy of the model. The approach has the drawback of classifying objects using a hyperplane, which is only partially effective [13,14]. The hyperplane is accurate only for classifying sample data into 2 classes. ...
... Step 7: Returning to Step 1, the optimal evacuation path of the next passenger m to be evacuated is calculated cyclically, and the evacuation path of passenger m is recalculated each time based on the spatiotemporal congestion on the evacuation path of m − 1 passengers according to equation (1) until all passengers are evacuated. Te fow chart of the time-varying network algorithm is shown in Figure 3 [27]. ...
As a highly dense area for passengers, the airport departure hall is particularly characterized by the easy gathering of passengers, irregularity, and difficulty in command, which has become a high incidence of emergencies. For the evacuation problem in case of emergencies, the vast majority of passengers randomly choose the shortest path from the exit to escape, which will lead to congestion at some nodes and prolonging of the evacuation time. Therefore, this study proposes a better evacuation method considering the dynamic change in the spatiotemporal congestion of passengers. First, this paper constructs an evacuation network diagram for the departure hall of the Shenzhen Bao’an International Airport terminal building, simplifies the evacuation process into point-to-point evacuation, and proposes a time-varying network algorithm to optimize the emergency evacuation of the airport. Second, on the premise of obtaining the number and dynamic distribution of passengers in each area in real time, a linear programming model with the shortest evacuation time as the optimization objective is established, and the current optimal evacuation path of passengers is dynamically calculated. Finally, the two evacuation methods, random evacuation and evacuation, based on a time-varying network algorithm are simulated and analyzed by AnyLogic software. The results show that the evacuation scheme based on the time-varying network algorithm can effectively relieve the passenger congestion of some evacuation paths and safety exits. The evacuation time is reduced by 74 s, the evacuation efficiency is improved by 14%, passenger congestion at some evacuation routes and safety exits can be effectively relieved, and the global optimization of the emergency evacuation of passengers is achieved.
... Furthermore, it ignores the effect of complex structure plan environments with multiple rooms and doors and the characteristics of people's movements, such as whether they are attempting to evacuate as individuals or in groups. Paper [7] uses the random forest algorithm to predict people's pre-evacuation time in urban residential communities using data from 379 valid questionnaires. ...
The behavior of people during an evacuation may have a significant impact on evacuation time, so it has been extensively studied using agent-based simulations. This paper aims to use machine learning for predicting agent evacuation time faster in advance, rather than waiting the entire simulation time. We use the well-known machine-learning polynomial regression as our prediction model, and linear regression and decision tree regression as our benchmark models. In order to generate a suitable dataset for training and validating our models, we automate the scenario-creation process from a single template scenario and the simulation output extraction process in the Vadere simulation framework. Our simulation experiments are carried out using the structure plan of the University of Münster’s administrative building, with up to 100 agents located in a source room as individuals and in groups, attempting to find the shortest path to an exit. We significantly improve evacuation prediction using machine learning regression models in agent-based
simulation experiments. Our polynomial regression model can predict evacuation time before the simulation begins, and the prediction results are close to the simulation results, with an average 𝑅2 score of 84%.
... As it is crucial to start evacuation at the right time to have a safe evacuation, and extant research on household evacuation time is limited, especially concerning a catastrophe like a flood, this study focuses on predicting the household's evacuation time and the parameters influencing the evacuation time. Further, many earlier studies on risk assessment and evacuation are modelled based on machine learning algorithms (Jiang et al., 2022;Rahman et al. 2021 a;Leon, Bian & Tang, 2021;Chen et al., 2019;Ghosh & Dey, 2021;Zhao, Yan & Yu, 2021;Zhao et al.2020). Therefore, this study also depends on machine learning algorithms for model building. ...
... The factors that account for people's estimation of risk and their response to evacuation include pets at home, gender dimensions, socio-economic status, level of education, clarity of evacuation messages, type of accommodation and peoples' trust in the public officials and system (Walch, 2018). Chen et al. (2019) used the random forest algorithm to build a predictive model to forecast the pre-evacuation time by integrating the residents' pre-evacuation behaviour data. Ghosh and Dey (2015) employed four different prediction models, including frequency ratio, fuzzy logic, logistic regression, and random forest, to investigate flood severity ratings that covered eight conditioning elements throughout the coastal tract. ...
... Previous researchers (Ghosh and Dey, 2015;Chen et al., 2019;Zhao et al., 2020) used mathematical techniques like frequency ratio, fuzzy logic, logistic regression, random forest etc. to predict the evacuation-prepetition time. However, there is a lack of agreement on which method is best for predicting evacuation preparation time, particularly in the case of floods. ...
Flooding is a significant hazard responsible for substantial damage and risks to human life worldwide. Effective emergency evacuation to a safer location remains a concern even though the crisis can be predicted and warnings were given. During a calamity, most residents cannot quickly and securely flee. As it is crucial to start evacuation at the right time to have a safe evacuation, this study focuses on a machine learning-based model for predicting a household's evacuation preparation time in the incident of a flood. The study is based on the data collected from flood-affected people from Kerala, India, through a questionnaire. The study indicates that people's demographic, geographical and behavioural aspects, awareness of natural hazards and management are the critical components for improved emergency actions. Further, the article also analysed the characteristics of the respondents and successfully created clusters in which the respondents broadly belong, which will help the rescue team operationalize the evacuation process.
