Groupwise evacuation with genetic algorithms

In a crisis situation on board a ship, it can be of the utmost importance to have the passengers safely evacuate to the lifeboats in an efficient manner. Existing methods such as marked escape routes, maps and so on are not optimal as pre-planned escape routes may become heavily congested by passengers. The closest lifeboat is not always feasible as lifeboat capacity can be exceeded. Considering that some evacuees are strongly affiliated and would like to evacuate together as a group, it all becomes a very difficult problem to solve. Sub-problems have been modelled, but no existing model combines all of these aspects into account. We proceed by modelling the area to be evacuated as a time-expanded graph, assuming that future development in hazard severity is known in the form of a survivability percentage for each node. Then we apply a multi-objective genetic algorithm with five different fitness functions that use heuristics to maximize overall survivability and reduce the total egress time if possible. A method has been developed to pick the best evacuation plan out of the pool of potential solutions returned by the genetic algorithm. The solution is compared with Dijkstra’s algorithm and randomly generated paths. Experiments are conducted using these algorithms for both predefined and randomly generated graphs using different parameters. In the tested random graph, the genetic algorithm gives on average 24% better survivability and 3 times better grouping Random algorithms. A fixed network with a known solution was solved 100%. This genetic algorithm can be used to generate better routing plans that utilizes multiple evacuation routes and lifeboats while taking into account groups, resulting in smoother evacuations which can save more lives

Method to extract difficult-to-evacuate areas by using tsunami evacuation simulation and numerical analysis

Extracting the area where people have difficulty evacuating (hereafter difficult-to-evacuate areas, DEA) when tsunamis hit after an earthquake is important for effective disaster mitigation measures. The DEA was conven-tionally extracted by simply considering the walking speed, distance to the evacuation destination, and time needed for evacuation after considering the estimated tsunami inundation area. However, evaluating the DEA from such a simple scheme is insufficient because the behavior of residents and the road conditions to the evacuation destinations after an earthquake are not properly reflected in the scheme. In this study, agent-based tsunami evacuation simulations that can reflect the behavior of residents and real -time changes in the situation were conducted in Zihuatanejo, Guerrero, Mexico. It is a prime sightseeing destination under the high risk of megathrust events in the Guerrero Gap. First, by checking the simulation images at the tsunami arrival time, bottleneck locations were identified, and five additional models with different measures for the bottleneck locations were constructed and tested to find the best model with 195 casualties. Then, focusing on the best model, three indices for the casualties were proposed to extract the DEA effectively and quantitatively, and numerical analyses using the three indices was conducted. Finally, the subdistrict in the center of the target area (subdistrict 5) was quantitatively found to be the district that should be given the highest priority for measures. Moreover, an example model with a new measure in subdistrict 5 was validated to have 101 casualties. The key points for applying the proposed method for extraction of DEA in other areas are summarized

Tsunami evacuation model for Sumner, Christchurch, New Zealand

Sumner, a coastal suburb located to the south-east of Christchurch, New Zealand, is highly exposed to a number of tsunami hazards. In tsunami mitigation plans, evacuation plays a crucial role in saving human lives, especially for communities located in low-lying coastal areas. The aim of this thesis is to enhance the methodological basis for development of tsunami evacuation plans in Sumner. To achieve this, a numerical simulation output of far-field tsunami impacts in Sumner was used to establish the maximum likely inundation extent and flow depth. This, together with population census data and daily activity patterns specified for the study area, established the spatio-temporal basis for characterising population exposure to the tsunamic hazard. A geospatial evacuation analysis method (Least Cost Path Distance), augmented with variable population exposure and distributed travel speeds, was used to characterise spatial variation in evacuation times and the corresponding numbers of evacuees and vehicles. Three ‘extreme’ end-member scenarios were utilised to address possible evacuation methods; all pedestrians evacuated to 20 metres elevation, all pedestrians to bus stops for evacuation using public transport, and all people evacuated using private vehicles. This thesis has made a methodological contribution to tsunami evacuation simulation by characterising variable spatio-temporal population exposure, and incorporating terrain properties into population and vehicle movements. The methods are equally applicable to other locations, to other hazards, and for both pre- and post-disaster evacuation analyses

Evaluation of software tools in performing advanced evacuation analyses for passenger ships

As safety regulations for passenger ship design continue to advance, so does the need for evacuation analysis tools to simulate the evacuation process. Currently the IMO requires an evacuation analysis for all new passenger ships in one of two ways: a simplified analysis or an advanced analysis. The simplified analysis takes a macroscopic view of the problem, treating the evacuees as particles in a fluid, flowing to their muster stations through corridors and doors as if they were pipes and valves. On the other hand, the advanced analysis takes a more microscopic approach, treating each evacuee as an individual with their own behaviour and decision making. However, as crowd simulation on passenger ships is a relatively young field of study, there is no clear consensus on the best way to perform this advanced analysis and therefore the guidelines are left more open ended. Consequently, there are several software suites that perform the analysis in different ways. This study aims to evaluate and better understand two different software packages, Evi and Pathfinder, which are capable of performing an advanced evacuation analysis. To do this, the same evacuation scenario on the same Main Vertical Zone (MVZ) of a RoPax ferry was simulated on both software in order to see how the differences in approaching the modelling affected both the numerical results and the user experience, including the time taken to build and run the analysis. These results were further compared with those obtained from a simplified analysis. Despite differences in how the reaction times were distributed, the total completion times measured were very similar, falling within the acceptance criteria set for this study. However, the user experience is where the largest differences between the two software became apparent. While Pathfinder had a more feature-rich toolset to build the geometry, the fact that Evi is purpose built to perform evacuation analyses of passenger ships is apparent in its preset IMO cases and batch running capabilities, providing a clear time advantage in performing the task

The development of a scenario independent method for evaluating the evacuation complexity of a building

Over the past two decades, more than 30 evacuation models have been developed to reproduce people’s movement patterns in evacuation. However, evacuation models cannot assess whether one building is better than another in regards to evacuation wayfinding. There exist techniques that attempt to compare different buildings for evacuation complexity. However, these graph measures are primarily used to measure the relative accessibility of different locations in a spatial system and were not generated for the purpose of comparing the complexity of different buildings. Currently only one method exists, Donegan’s method [DT98] [PD96] [DT99], which can be applied to compare building for evacuation ability. However, this technique is severely limited to specific building layouts and only considers connectivity. Taking the Donegan’s method as a first step, this thesis extends this algorithm to obtain a new Distance Graph Method, which considers travel distance as well as being able to be applied to graphs with circuits. Then a further building complexity measures is presented, the Global Complexity (PAT) method. This is shown to be a valid measure which considers additional important factors such as wayfinding time, travel distance and the areas of compartments. The Distance Graph Method and Global Complexity (PAT) methods are based on a room graph representation which does not have the descriptive power to describe the actual routes taken during the wayfinding process. To resolve this drawback a further method is presented which utilises a ‘route-based graph’ that has the ability to represent the real route that an evacuee will take during the wayfinding process. Furthermore the Distance Graph Method and Global Complexity (PAT) methods assume a “worst state” calculation for the nodal information. This means for buildings with more than one exit these methods calculate a global building complexity according to a mathematical formula, which considers all exits separately. To address these problems, the final method, Complexity Time Measure, is presented, which is based around a number of wayfinding behaviour rules over a ‘route-based graph’ representation. This addresses the question: ‘If an occupant is positioned at a random location within a building, on average how long does the occupant need to spend to find an available exit?’ Hence, provides a means to compare complex buildings, with circuits, in relation to evacuation capability

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Modeling of Consolidation by Household for Emergency Evacuation Events

Evacuation studies have grown in importance over the years as a number of recent emergencies, natural and man-made, have raised the general level of awareness about public responses to the threat or actual occurrence of disasters. An accurate prediction of the rates of evacuation and estimate of the time required to clear a risk area are important planning tools that can mitigate the consequences of an emergency situation. Traditional evacuation models are predicated on the assumption that everyone would seek the quickest or shortest route to safety, given a life-threatening situation. Observations, however, show that a large percentage of the population does not seek the quickest route to safety. Parents may move toward dangers to pick up their children from schools. Persons at work may go back home to pick up dependent family members, pets, and personal effects before evacuation begins in earnest. Incorrect assumptions of evacuee behaviors could lead to measures that negatively impact the traffic flow during evacuation. One effective method to evaluate different evacuation strategies is the use of simulation. Most established simulation models, however, are not built to take the underlying drivers' social behavior into considerations. In this study, we develop a computerized tool for modeling evacuation dynamics with household consolidation, and then incorporate it into a traffic-simulation software platform. This tool will allow a percentage of the population to consolidate as a family before they evacuate. After that, a study is conducted to explore the consolidation by household in a network under various demand levels. A mathematical model is presented to capture the underlying relationships among the network components. Next, the traffic volumes entering and leaving the network are investigated to highlight some recommendations about the appropriate implementation of contraflow or staged evacuation strategies. To help decision makers have a better understanding of the evacuation traffic patterns, this study also examined the influences from spatio-temporal information such as the information dissemination delay, the evacuees' preparedness time, the numbers and locations of shelters in a network, and demographical information like the number of vehicles in a family. The proposed research will allow planners to study more realistically the effects of evacuation strategies. The results of studying such household by consolidation behavior are (1) evacuation times are significantly longer compared to the assumption of evacuees taking the shortest route away from danger in low/average demands; (2) with heavy demand, low consolidation rates can produce long evacuation times due to the rapid development of congestion at the network exits; (3) with heavy demand, high consolidation rates could delay the turning point to reverse the inbound lanes to outbound in a contraflow operation; (4) the sequencing of converting inbound lanes to outbound in a contraflow operation should start at the outermost links and work inward, due to extra bi-directional traffic on the network engaged in consolidation activities; (5) information delays and evacuees' preparedness as a family, coupled with the family consolidation behavior, are important parameters to the evacuation performance; (6) information on demographics and geography also has an important impact on the network evacuation efficiency and evacuees' social behaviors; more specifically, the evacuation performance is very sensitive to the number of shelters in the network

Prioritizing Patients for Emergency Evacuation From a Healthcare Facility

The success of a healthcare facility evacuation depends on communication and decision-making at all levels of the organization, from the coordinators at incident command to the clinical staff who actually carry out the evacuation. One key decision is the order in which each patient is chosen for evacuation. While the typical planning assumption is that all patients are to be evacuated, there may not always be adequate time or resources available to move all patients. In these cases, prioritizing or ordering patients for evacuation becomes an extremely difficult decision to make. These decisions should be based on the current state of the facility, but without knowledge of the current patient roster or available resources, these decisions may not be as beneficial as possible. Healthcare facilities usually consider evacuation a last-resort measure, and there are often system redundancies in place to protect against having to completely evacuate all patients from a facility. Perhaps this is why there is not a great deal of research dedicated to improving patient transfers. In addition, the question of patient prioritization is a highly ethical one. Based on a literature review of 1) suggested patient prioritization strategies for evacuation planning as well as 2) the actual priorities given in actual facility evacuations indicates there is a lack of consensus as to whether critical or non-critical care patients should be moved away from a facility first in the event of a complete emergency evacuation. In addition, these policies are \u27all-or-nothing\u27 policies, implying that once a patient group is given priority, this entire group will be completely evacuated before any patients from the other group are transferred. That is, if critical care patients are given priority, all critical care patients will be transferred away from the facility before any non-critical care patient. The goal of this research is to develop a decision framework for prioritizing patient evacuations, where unique classifications of patient health, rates of evacuation, and survivability all impact the choice. First, I provide several scenarios (both in terms of physical processing estimates as well as competing, ethically-motivated objectives) and offer insights and observations into the creation of a prioritization policy via dynamic programming. Dynamic programming is a problem-solving technique to recursively optimize a series of decisions. The results of the dynamic programming provide optimal prioritization policies, and these are tested with simulation analysis to observe system performance under many of the same scenarios. Because the dynamic programming decisions are based on the state of the system, simulation also allows the testing of time-based decisions. The results from the dynamic programming and simulation, as well as the structural properties of the simulation are used to create assumptions about how evacuations could be improved. The question is not whether patient priorities should be assigned - but how patient priorities should be assigned. Associated with assigning value to patients are a variety of ethical dilemmas. In this research, I attempt to address patient prioritization from an ethical perspective by discussing the basic principles and the potential dilemmas associated with such decisions. The results indicate that an all-or-nothing, or a \u27greedy\u27 policy as discussed in the literature may not always be optimal for patient evacuations. In some cases, a switching policy may occur. Switching policies begin by evacuating patients from one classification and then switch to begin evacuations from the second patient class. A switch can only be made once; after a switch is made, all remaining patients from the new group should be evacuated. When there are no more patients of that group remaining in the system, the remaining patients from the class that was initially given priority should be evacuated. In the case of critical and non-critical care patients, switching policies first give priority to non-critical care patients. When the costs of holding patients in the system are not included in the models - and the decisions are just based on maximizing the number of saved lives - the switching policies may perform as good or better than the greedy policies suggested in the literature. In addition, when holding costs are not included, it is easier to predict whether the optimal policy is a greedy policy or a switching policy. Prioritization policies can change based on the utility achieved from evacuating individual patients from each class, as well as for other competing objective functions. This research examines a variety of scenarios - maximizing saved lives, minimizing costs, etc. - and provides insights on how the selection of an objective impacts the choice. Another insight of this research is how multiple evacuation teams should be allocated to patients. In the event that there is more than one evacuation team dedicated to moving a group of patients, the two teams should be allocated to the same patient group instead of being split between the multiple patient groups

The Evaluation of a Performance-Based Design Process for a Hotel Building: The Comparison of Two Egress Models

This work emphasizes the importance for egress model users to choose a model for each project with the appropriate input features and simulation capabilities. This report also gives model users a mechanism for choosing the appropriate model by providing a detailed egress model review (Chapter 2). Specifically this report focuses on the ability of two egress models, EXIT89 and Simulex, to simulate a high-rise hotel building evacuation. When EXIT89 and Simulex are used to 1) simulate the same design scenarios and 2) perform a bounding analysis of the hotel building, significant differences in egress times were identified. EXIT89's evacuation times were found to be 25-40% lower than Simulex for the design scenarios, attributed to differences in unimpeded speeds, movement algorithms, methods of simulating slow occupants, density in the stairs, and stair configuration input between the models. For the bounding analysis, EXIT89 produced maximum evacuation times 30-40% lower than Simulex