Optimizing Shelter Site Locations in Residential Community: A GeoSimulation and Genetic Algorithm Approach

As the existing crowd evacuation model seldom considers the influence of crowd confusion degree on the evacuation results. This paper proposes evacuation entropy particle swarms optimization (E-PSO) crowd evacuation model. This model considers the impact of obstacles and the surrounding people on evacuees' evacuation behavior in the evacuation process. And the concept of evacuation entropy is introduced to quantitatively describe the degree of chaos in crowd movement and is used to correct the individual speed direction in the model. The model further considers the influence of individual panic psychology and the presence of leaders in evacuation. The simulation results show that the panic of evacuees will aggravate the degree of chaos on the event, but the addition of leaders will reduce the degree of confusion and improve the evacuation efficiency. At the same time, compared with the traditional PSO model, the modified PSO model with evacuation entropy can reduce the degree of confusion on the evacuation process, effectively avoid the occurrence of crowded stampede accident and improve the evacuation efficiency.

An agent-based modeling approach to collaborative classrooms evacuation process

Elevator evacuation has been considered in high-rise building evacuation in the world, especially in China. Elevator safety has been widely studied for this purpose and technical standards are also available in different countries. However, it is critical to understand human behaviors in elevator evacuation before elevators can be used in building evacuation. It is expected that social relation (family, friend, classmate, etc.) will play an important role on evacuation behaviors. However, researches are largely missing on social relation and its impacts on movement and behaviors of the evacuees. This paper aims to investigate the crowd evacuation considering social relation. An evacuation experiment is conducted in a 11-storey office building. Participants who take part in the experiment include individuals, families and lovers. Evacuation behaviors especially decision-making as well as important factors affecting evacuees’ choices are discussed. Movement characteristics of evacuees in the stair are also analyzed. It is concluded that family members will take actions, such as take elevators or stairs, together. Females and evacuees in poor condition prefer to take the elevator during evacuation. Many pairs or small groups may be formed owing to social relations. The groups take more time to make decision. The members in small groups may block the traffic and slow down the speed of the crowd. Evacuation efficiency changes greatly considering small group behaviors and social relations. Experimental results are helpful for determining the effective rules and regulations in elevator evacuation in high-rise buildings.

This paper investigates whether it is possible to determine shelter locations and congestion spots at the time of an event, by resorting to the recently anticipated use of mobile space data at the time of a disaster. This study focused on the earthquake and the resulting tsunami that occurred off the coast of Fukushima Prefecture on November 22nd, 2016. We verified whether it is possible to identify congestion spots and shelter locations by comparing with the results of a questionnaire survey conducted in a previous study on evacuation behavior at the time of the occurrence of earthquakes and tsunamis. As a result, we found that it is difficult to determine evacuation behaviors from the data, as raw mobile space data extracted several hours after the tsunami event only gave information on where spots’ populations ordinarily converge to. We were able to determine the locations where populations gather by taking into consideration time-based differences between the raw data obtained. It, however, proved difficult to obtain a good determination of congestion spots.

Understanding and representing how individuals behave in earthquake emergencies would be essentially to assess the impact of vulnerability reduction strategies on existing buildings in seismic areas. In fact, interactions between individuals and the scenario (modified by the earthquake occurrence) are really important in order to understand the possible additional risks for people, especially during the evacuation phase. The current approach is based on “qualitative” aspects, in order to define best practice guidelines for Civil Protection and populations. On the contrary, a “quantitative” description of human response and evacuation motion in similar conditions is urgently needed. Hence, this work defines the rules for pedestrians’ earthquake evacuation in urban scenarios, by taking advantages of previous results of real-world evacuation analyses. In particular, motion laws for pedestrians is defined by modifying the Social Force model equation. The proposed model could be used for evaluating individuals’ evacuation process and so for defining operative strategies for interferences reduction in critical urban fabric parts (e.g.: interventions on particular buildings, evacuation strategies definition, city parts projects).

The paper explores the usage of agent-based modeling in the context of large event halls evacuation during music festivals and cultural events. An agent-based model is created in NetLogo 6.2.2 for better representing the human behavior when involved in such situations. A series of characteristics have been set for the agents in order to preserve their heterogeneity in terms of speed, age, locomotion impairment, familiarity with the environment, evacuating with another person, choosing the closest exit or not, selecting the closest path to the exits. An “adapted cone exit” approach has been proposed in the paper in order to facilitate the guidance of the agents in the agent-based model to the closest exit and its advantages have been proved in comparison with the classical “cone exit” approach. Different evacuation scenarios have been simulated and analyzed for better observing the capabilities of evacuation modeling in the case of evacuation emergencies. Besides the overall evacuation time, an average evacuation time has been determined for the agents based on the individual evacuation time, which can be easily connected with a risk indicator associated to each situation. Due to the visual interface offered by the agent-based model, coupled with the evacuation indicators, the proposed model can allow the identification of the main factors that may contribute to a prolonged evacuation process (e.g. overcrowding at one of the exits, not choosing the appropriate door, evacuating with a friend/parent) and the potential measures to be considered for insuring a safe evacuation process.

Large-scale theaters with dense personnel are novel and unique, but there are many potential hidden dangers. Once an unexpected accident or disaster occurs, it is easy to cause crowd panic, and leads to congestion of evacuation path, aggravation of evacuation difficulty, crowding, stampede, and other group injuries. Therefore, studying the architectural characteristics of large theaters and evacuation behavior under fire can provide reference for the evacuation of such buildings and the preparation of emergency plans. This study took a large theater that can accommodate more than 1500 people as the research object, built a 3D model based on BIM, used Pyrosim and Pathfinder simulation software to simulate the smoke diffusion and personnel evacuation in the case of the building fire, observed whether all personnel could evacuate within the safe evacuation time, and analyzed the existing problems in the evacuation process. According to the results, it was difficult to evacuate due to the high crowd density and complex composition of large theaters. In short, appropriately increasing the width of evacuation doors and ensuring the normal operation of firefighting facilities can ensure the safe evacuation of personnel.

We study the problem of selecting a set of shelter locations in preparation for natural disasters. Shelters provide victims of a disaster both a safe place to stay and relief necessities such as food, water and medical support. Individuals from the affected population living in a set of population points go to, or are transported to the assigned open shelters. We aim to take both efficiency and inequity into account, thus we minimize a linear combination of: (i) the mean distance between opened shelter locations and the locations of the individuals assigned to them; and (ii) Gini’s Mean Absolute Difference of these distances. We develop a stochastic programming model with a set of scenarios that consider uncertain demand and disruptions in the transportation network. A chance constraint is defined on the total cost of opening the shelters and their capacity expansion. In this stochastic context, a weighted mean of the so-called ex ante and ex post versions of the inequity-averse objective function under uncertainty is optimized. Since the model can be solved to optimality only for small instances, we develop a tailored Genetic Algorithm (GA) that utilizes a mixed-integer programming subproblem to solve this problem heuristically for larger instances. We compare the performance of the mathematical program and the GA via benchmark instances where the model can be solved to optimality or near optimality. It turns out that the GA yields small optimality gaps in much shorter time for these instances. We run the GA also on Istanbul data to drive insights to guide decision-makers for preparation.

We present a Genetic Algorithm (GA) tool that uses Sequential Approximate Optimization (SAO) to calibrate Agent-Based Models (ABMs). The SAO/GA searches through a user-defined set of input parameters to an ABM, delivering values for those parameters so that the output time series of an ABM match the real system's time series to certain precision. SAO/GA calculates a meta-model of the real and ABM's time series and optimizes that model. This allows SAO/GA to stabilize the ABM's time series and assure a higher probability of convergence, even under highly variable ABM's outputs. The results show that SAO/GA exhibits a higher convergence probability, but requires a rather long computational time to reach the stopping condition, although that long time is not so excessive to preclude SAO/GA practical use.

Landowner characteristics influence his/her willingness to change landuse practices to provide more or less environmental benefits. However, most studies of agricultural/environmental polices identify landowners as homogenous. And, the primary cause of failure of many environmental and other polices is the lack of knowledge on how humans may respond to polices based on changes in their behavior (Stern, 1993). From socioeconomic theory and empirical research, landowners can be identified as individuals who make agricultural landuse decisions independently based on their objectives. Identifying possible classes of landowners, assessing how each would potentially respond to policy alternatives, and the resulting pattern of land uses in a watershed or a riparian corridor would be very useful to policy makers as they evaluated alternatives. Agricultural landscapes are important producers of ecosystem services. The mix of ecosystem services and commodity outputs of an agricultural landscape depends on the spatial pattern of land uses emerging from individual land use decisions. However, many empirical studies show that the production of ecosystem services from agricultural landscapes is declining. This is consistent with research conducted over the last few decades showing there is a narrow range of social circumstances under which landowners are willing to make investments in the present to achieve public benefits in the future through investing in natural capital resulting in public goods which are frequently produced as ecosystem services. In this study an agent-based model within a watershed planning context is used to analyze the tradeoffs involved in producing a number of ecosystem services and agricultural commodities given price and policy scenarios while assuming three different types of agents in terms of their goals. The agents represent landowners who have been divided into a number of different groups based on their goals and the size of their farm operations. The multi-agent-based model is developed using a heuristic search and optimization technique called genetic algorithm (GA) (Holland), which belongs to a broader class of evolutionary algorithms. GAs exhibit three properties (1) they start with a population of solution, (2) they explore the solution space through recombination and mutation and (3) they evaluate individual solutions based on their appropriate fitness value(s), for example given profit maximizing agents this would be gross margin. A GA is a heuristic stochastic search and optimization method, which works by mimicking the evolutionary principles and chromosomal processing in natural genetics. The three economic agents that are modeled are based on variations in their objective functions and constraints. This study will help in identifying the tradeoffs associated with various agents in the provision of ecosystem services and agricultural commodities. The agent model developed here will help policy and decision maker identify the various agents within the watershed and assess various policy options based on that information. The study will also help to understand the interaction and feedback between the agents and their environment associated with various policy initiatives. The results of the study indicate that the agent model correctly predicts the actual landuse landcover map by 75 percent. Key words: Multifunctional agriculture, Agent based modeling, Genetic Algorithm.

In 2004 at least 230,000 people were victims of the Aceh tsunami disaster. To prevent the recurrence of many victims, the Aceh government held an evacuation exercise in 2008. To improve effectiveness dan reduce the cost reduction during evacuations drills, simulation is the best option. Agent-Based Modeling is a simulation program that was employed for tsunami evacuation in Aceh. This study on tsunami evacuation using agent-based modelling presented and evaluated the different control parameters that affect the evacuation rate. Evacuation scenario during day or night has different environmental, agent base, road modelling, and population approach. The Road Network Model has explained that to analyze the effect of agents in the evacuation process, resident agents are presumed to know the direction and shortest path to the nearest evacuation points. This simulation designed in Netlogo is also able to assess the congestion possibility on the road network. The road network emphasized the different scenarios to discover the possibility of congestion points. Nighttime is proven to be the best scenario for performing the evacuation in the simulation. The key reason to select the night scenario is to maximize the effects of an evaluation of the road network. In addition, simulation using night scenarios is also expected to raise people’s awareness.

Large scale disasters often create the need for evacuating affected regions to save lives. Disaster management authorities need evacuation simulation tools to assess the efficiency of various evacuation scenarios and the impact of a variety of environmental and social factors on the evacuation process. Therefore, sound simulation models should include the relevant factors influencing the evacuation process. More specifically, to be reliable in critical situations, evacuation simulations must integrate information on time-varying phenomena that can affect the evacuation process, such as the impact of meteorological conditions, road incidents, or other relevant events. Sensor networks constitute an efficient solution for gathering data on such events and feeding the evacuation simulation. However, the coupling of sensors with multi-agent simulation tools is not straightforward. In this chapter, we present a publish/subscribe system based on Event Calculus to support real-time multi-agent evacuation simulations. The system identifies relevant events from sensor data gathered through a Sensor Event Service that implements the OGC SWE standards and the Event Pattern Markup Language (EML). Then, the publish/subscribe system acts as a middleware between the Sensor data publishers and the multi-agent evacuation simulation through a Sensor Processing Service based on Event Calculus that infers the impact of events on the characteristics of the road network. The result is an evacuation simulation that can be deployed to assess various evacuation scenarios in real-time, during the crisis response.

This paper examines the use of evolutionary computation (EC) to find optimal solution in vehicle assignment problem (VAP). The VAP refers to the allocation of the expected number of people in a potentially flooded area to various types of available vehicles in evacuation process. A novel discrete particle swarm optimization (DPSO) algorithm and genetic algorithm (GA) are presented to solve this problem. Both of these algorithms employed a discrete solution representation and incorporated a min-max approach for a random initialization of discrete particle position. A min-max approach is based on minimum capacity and maximum capacity of vehicles. We analyzed the performance of the algorithms using evacuation datasets. The quality of solutions were measured based on the objective function which is to find a maximum number of assigned people to vehicles in the potentially flooded areas and central processing unit (CPU) processing time of the algorithms. Overall, DPSO provides an optimal solutions and successfully achieved the objective function whereas GA gives sub optimal solution for the VAP.KeywordsDiscrete Particle Swarm OptimizationEvacuation ProcessEvolutionary ComputationGenetic AlgorithmVehicle Assignment Problem

Sinabung is one of the volcanoes in Indonesia which commands government and societal attention since its first eruption in 2010. In contrast to other volcanoes in Indonesia, Sinabung volcano is still on the warning level up to and including the present time. According to the prior interview conducted to 14 participants from local government and local leaders in Medan and Karo, Indonesia, the long duration of warning level encourages some people to behave differently and become complacent since the 2010 eruption. People who initially trusted the government in the 2010 eruption currently doubt and distrust the government information and instruction. This study aims to compare the result from the prior interview and survey in order to categorize and cluster people regarding their trust behavior by using a Situational Judgment Test. This clustering result can be utilized as a foundation to build a conceptual model of trust behavior in Agent-Based Modeling and Simulation (ABMS) for further research and investigation.

The development of locomotor kinematics in neonatal rats: An agent-based modeling analysis in group and individual contexts