The number of devices connected to the Internet will increase exponentially by 2020, which is smoothly migrating the Internet from an Internet of people towards an Internet of Things (IoT). These devices can communicate with each other and exchange information forming a wide Wireless Sensor Network (WSN). WSNs are composed mainly of a large number of small devices that run on batteries, which makes the energy limited. Therefore, it is essential to use an energy efficient routing protocol for WSNs that are scalable and robust in terms of energy consumption and lifetime. Using routing protocols that are based on clustering can be used to solve energy problems. Cluster-based routing protocols provide an efficient approach to reduce the energy consumption of sensor nodes and maximize the network lifetime of WSNs. In this thesis, a single hop cluster-based network layer routing protocol, referred to as HRHP, is designed. It applies centralized and deterministic approaches for the selection of cluster heads, in relation to offer an improved network lifetime for large-scaled and dense WSN deployments. The deterministic approach for selecting CHs is based on the positive selection mechanism in the human thymus cells (T-cells). HRHP was tested over six different scenarios with BS position outer the sensing area, it achieved a maximum average of 78% in terms of life time. To further reduce energy consumption in WSN, a multi-hop algorithm, referred to as MLHP, is proposed for prolonging the lifetime of WSN. In this algorithm, the sensing area is divided into three levels to reduce the communication cost by reducing the transmission distances for both inter-cluster and intra-cluster communication. MLHP was tested over fourteen cases with different heterogeneity factors and area sizes and achieved a maximum of 80% improvement in terms of life time. Finally, a real-time and autonomous emergency evacuation approach is proposed, referred to as ARTC-WSN, which integrates cloud computing with WSN in order to improve evacuation accuracy and efficiency for smart buildings. The approach is designed to perform localized, autonomous navigation by calculating the best evacuation paths in a distributed manner using two types of sensor nodes (SNs), a sensing node and a decision node. ARTC-WSN was tested in five scenarios with different hazard intensity, occupation ratio and exit availability over three different areas of evacuation and achieved an average of 98% survival ratio for different cases.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Malgorzata J. Rys / The community of Manhattan, Kansas was recently chosen as the future site of the National Bio- and Agro-Defense Facility (NBAF). At this site, research of agricultural and animal diseases and pathogens will take place. Due to the fact that the site will be in close proximity to a university, as well as many residents, a risk assessment must be completed to determine whether or not the current road infrastructure would be sufficient for evacuating the city in the event of an emergency. It should be noted that while NBAF is a large concern for this report, risk management is important in other scenarios as well, such as natural disasters or chemical spills, and this information can be applied to such events. This paper discusses the creation and analysis of a discrete-event simulation using ARENA software. The simulation described several scenarios. They were a base case scenario with only campus traffic evacuating; a scenario in which campus and outside traffic evacuate; a case with increased outside traffic; a case in which a vehicle breaks down; a case which includes guardians of children attending campus childcare are re-routed to pick up their children before evacuating; a case which accounts for reduced traveling speeds due to cell phone usage; and a case which closes a direction outside of Manhattan due to wind direction. Such simulations are an ideal performance measure of traffic flow under certain conditions due to the fact that physical resources are not needed to make a realistic comparison between each of them. Each of the situations described above were compared based on percentage of traffic leaving Manhattan and arriving at a defined safe zone each hour. Based on the findings, those involved with disaster management planning can determine if the percentages of vehicles leaving the system per hour are acceptable. They should be evaluated against potential spread rates of diseases to ensure that all residents may evacuate without the danger of becoming infected. For applications outside of NBAF, the results give insight into the degree of change in evacuation percentage that changes within the system may cause, and change any routing accordingly.

Planning for emergency evacuation, and, more generally, for emergency movement involving both evacuation (egress) of occupants and ingress of first responders, presents important and challenging problems. A number of the current issues that arise during emergency incidents are due to the uncertainty and transiency of environmental conditions. In general, movement plans are formulated at building design-time, and those involved, such as building occupants and emergency responders, are left to adapt routing plans to actual events as they unfold. In the context of next-generation emergency response systems, it has been proposed to dynamically plan and route individuals during an emergency event, replanning to take account of changes in the environment. In this work, an emergency movement problem, the Maximal Safest Escape (MSE) problem, is formulated in terms that model the uncertain and transient environmental conditions as a flow problem in time-dependent networks with time-varying and stochastic edge travel-times and capacities (STV Networks). The objective of the MSE problem is to find flow patterns with the a priori maximal probability of successfully conveying all supply from the source to the sink in some given STV Network. The MSE and its deterministic counterpart are proved to be NP-hard. Furthermore, due to inherent complexity in evaluating the exact quality of candidate solutions, a simulation approximation method is presented based on well-known Monte-Carlo sampling methods. Given the complexity of the problem, and using the approximation method for evaluating solutions, it is proposed to tackle the MSE problem using a metaheuristic approach based on an existing framework that integrates Evolutionary Algorithms (EA) with a state-of-the-art statistical ranking and selection method, the Optimal Computing Budget Allocation (OCBA). Several improvements are proposed for the framework to reduce the computational demand of the ranking method. Empirically, the approach is compared with a simple fitness averaging approach and conditions under which the integrated framework is more efficient are investigated. The performance of the EA is compared against upper and lower bounds on optimal solutions. An upper bound is established through the “wait-and-see” bound, and a lower bound by a naıve random search algorithm (RSA). An experimental design is presented that allows for a fair comparison between the EA and the RSA. While there is no guarantee that the EA will find optimal solutions, this work demonstrates that the EA can still find useful solutions; useful solutions are those that are at least better than some baseline, here the lower bound, in terms of solution quality and computational effort. Experimentally, it is demonstrated that the EA performs significantly better than the baseline. Also, the EA finds solutions relatively close to the upper bound; however, it is difficult to establish how optimistic the upper bounds. The main approach is also compared against an existing approach developed for solving a related problem wrapped in a heuristic procedure in order to apply the approach to the MSE. Empirical results show that the heuristic approach requires significantly less computation time, but finds solutions of significantly lower quality. Overall, this work introduces and empirically verifies the efficacy of a metaheuristic based on a framework integrating EAs with a state-of-the-art statistical ranking and selection technique, the OCBA, for a novel flow problem in STV Networks. It is suggested that the lessons learned during the course of this work, along with the specific techniques developed, may be relevant for addressing other flow problems of similar complexity.

This thesis was motivated to simulate the evacuation traffic of Mississippi Stated University (MSU) main campus using the Path-Following logic of TSIS/CORSIM and to evaluate a set of traffic management plans. Three scenarios of traffic management plans were developed and tested. A NCT of 123 minutes was projected if evacuate without any plan. In comparison, under a pre-planned traffic management plan the NCT would decrease to 39 minutes. Further, if implement contra flow the NCT would reduce to 21 minutes. If even further adjust the signal timing plans at the university exits a NCT of 20 minutes would be achieved. The sensitivity analysis found that the NCT was sensitive to the CORSIM parameters of free flow speed, time to react to sudden deceleration of lead vehicle and the configuration of driver type, while the effects of discharge headway and start up lost time were not found to be significant.

No description available.

The numerous man-made disasters and natural catastrophes that menace major communities accentuate the need for proper planning for emergency evacuation. Transportation networks in cities evolve over long time spans in tandem with population growth and evolution of travel patterns. In emergencies, travel demand and travel patterns drastically change from the usual everyday volumes and patterns. Given that most US and Canadian cities are already congested and operating near capacity during peak periods, network performance can severely deteriorate if drastic changes in Origin-Destination (O-D) demand patterns occur during or after a disaster. Also, loss of capacity due to the disaster and associated incidents can further complicate the matter. Therefore, the primary goal when a disaster or hazardous event occurs is to coordinate, control, and possibly optimize the utilization of the existing transportation network capacity. Emergency operation management centres face multi-faceted challenges in anticipating evacuation flows and providing proactive actions to guide and coordinate the public towards safe shelters. Numerous studies have contributed to developing and testing strategies that have the potential to mitigate the consequences of emergency situations. They primarily investigate the effect of some proposed strategies that have the potential of improving the performance of the evacuation process with modelling and optimization techniques. However, most of these studies are inherently restricted to evacuating automobile traffic using a certain strategy without considering other modes of transportation. Moreover, little emphasis is given to studying the interaction between the various strategies that could be potentially synergized to expedite the evacuation process. Also, the absence of an accurate representation of the spatial and temporal distribution of the population and the failure to identify the available modes and populations that are captive to certain modes contribute to the absence of multimodal evacuation procedures. Incorporating multiple modes into emergency evacuation has the potential to expedite the evacuation process and is essential to assuring the effective evacuation of transit-captive and special-needs populations . This dissertation presents a novel multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. A multi-objective approach is used to optimize the multimodal evacuation problem. For automobile evacuees, an Optimal Spatio-Temporal Evacuation (OSTE) framework is presented for generating optimal demand scheduling, destination choices and route choices, simultaneously. OSTE implements Dynamic Traffic Assignment (DTA) techniques coupled with parallel distributed genetic optimization to guarantee a near global optimal solution. For transit evacuees, a Multi-Depots, Time Constrained, Pick-up and Delivery Vehicle Routing Problem (MDTCPD-VRP) framework is presented to model the use of public transit vehicles in evacuation situations. The MDTCPD-VRP implements constraint programming and local search techniques to optimize certain objective functions and satisfy a set of constraints. The OSTE and MDTCPD-VRP platforms are integrated into one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. A proof-of-concept prototype has been tested; it investigates the optimization of a multimodal evacuation of a portion of the Toronto Waterfront area. It also assesses the impact of multiple objective functions on emergency evacuation while attempting to achieve an equilibrium state between transit modes and vehicular traffic. Then, a large-scale application, including a demand estimation model from a regional travel survey, is conducted for the evacuation of the entire City of Toronto. This framework addresses many limitations of existing evacuation planning models by: 1) synergizing multiple evacuation strategies; 2) utilizing robust optimization and solution algorithms that can tackle such multi-dimensional non deterministic problem; 3) estimating the spatial and temporal distribution of evacuation demand; 4) identifying the transit-dependent population; 5) integrating multiple modes in emergency evacuation. The framework presents a significant step forward in emergency evacuation optimization.

The numerous man-made disasters and natural catastrophes that menace major communities accentuate the need for proper planning for emergency evacuation. Transportation networks in cities evolve over long time spans in tandem with population growth and evolution of travel patterns. In emergencies, travel demand and travel patterns drastically change from the usual everyday volumes and patterns. Given that most US and Canadian cities are already congested and operating near capacity during peak periods, network performance can severely deteriorate if drastic changes in Origin-Destination (O-D) demand patterns occur during or after a disaster. Also, loss of capacity due to the disaster and associated incidents can further complicate the matter. Therefore, the primary goal when a disaster or hazardous event occurs is to coordinate, control, and possibly optimize the utilization of the existing transportation network capacity. Emergency operation management centres face multi-faceted challenges in anticipating evacuation flows and providing proactive actions to guide and coordinate the public towards safe shelters. Numerous studies have contributed to developing and testing strategies that have the potential to mitigate the consequences of emergency situations. They primarily investigate the effect of some proposed strategies that have the potential of improving the performance of the evacuation process with modelling and optimization techniques. However, most of these studies are inherently restricted to evacuating automobile traffic using a certain strategy without considering other modes of transportation. Moreover, little emphasis is given to studying the interaction between the various strategies that could be potentially synergized to expedite the evacuation process. Also, the absence of an accurate representation of the spatial and temporal distribution of the population and the failure to identify the available modes and populations that are captive to certain modes contribute to the absence of multimodal evacuation procedures. Incorporating multiple modes into emergency evacuation has the potential to expedite the evacuation process and is essential to assuring the effective evacuation of transit-captive and special-needs populations . This dissertation presents a novel multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. A multi-objective approach is used to optimize the multimodal evacuation problem. For automobile evacuees, an Optimal Spatio-Temporal Evacuation (OSTE) framework is presented for generating optimal demand scheduling, destination choices and route choices, simultaneously. OSTE implements Dynamic Traffic Assignment (DTA) techniques coupled with parallel distributed genetic optimization to guarantee a near global optimal solution. For transit evacuees, a Multi-Depots, Time Constrained, Pick-up and Delivery Vehicle Routing Problem (MDTCPD-VRP) framework is presented to model the use of public transit vehicles in evacuation situations. The MDTCPD-VRP implements constraint programming and local search techniques to optimize certain objective functions and satisfy a set of constraints. The OSTE and MDTCPD-VRP platforms are integrated into one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. A proof-of-concept prototype has been tested; it investigates the optimization of a multimodal evacuation of a portion of the Toronto Waterfront area. It also assesses the impact of multiple objective functions on emergency evacuation while attempting to achieve an equilibrium state between transit modes and vehicular traffic. Then, a large-scale application, including a demand estimation model from a regional travel survey, is conducted for the evacuation of the entire City of Toronto. This framework addresses many limitations of existing evacuation planning models by: 1) synergizing multiple evacuation strategies; 2) utilizing robust optimization and solution algorithms that can tackle such multi-dimensional non deterministic problem; 3) estimating the spatial and temporal distribution of evacuation demand; 4) identifying the transit-dependent population; 5) integrating multiple modes in emergency evacuation. The framework presents a significant step forward in emergency evacuation optimization.

Almost every year, Aboriginal communities are evacuated from northern regions of Canada to nearby cities because of threats due to forest fires and flooding. In this thesis, I present the perspectives of twenty members of the Hatchet Lake First Nation, who were evacuated from Wollaston Lake in northern Saskatchewan during the summer of 2011. My main research question is, how do residents of Wollaston Lake describe experiences of disruptions to well-being and distress during the evacuation and in the evacuation centers? My methods are qualitative, as I conducted open-ended interviews and participant observation while residing in the community for six weeks during the summer of 2012. Following the approaches of Geertz (2000), Garro (2000), and Mattingly (1998), I engaged in a narrative analysis of these data. Three main themes are evident in community members’ discussions of their experiences. First, participants focus on the ways that the fire and displacement disrupted the well-being of fellow community members and, to a lesser degree, their relationships with the land surrounding their town, and their roles within the community. Residents of Wollaston Lake portray a version of well-being that is rooted in the social, rather than individual, self. The second theme relates to family roles, as mothers, fathers, adult children, and guardians describe the various ways that these roles were disrupted during the fire and evacuation, and the distress elicited by these disruptions. These narratives are indicative of the discrepancies between the circumstances experienced during the fire and evacuation, and the values and behaviors that they associate with family roles. The third theme relates to expectations and blame, as community members recall the various ways that the evacuation failed to meet their expectations, and they attribute blame to those that they deem responsible for these inadequacies. Specifically, community members focus on expectations relating to the handling of the threat of fire, the organization of the evacuation, and their interactions with members of the host communities. These findings indicate the incongruities between current emergency management practices in Saskatchewan and the needs of this community. The implication of these findings is that, in order to minimize distress during future disasters, organizers must develop plans that account for the distinct social norms and vulnerabilities of the communities with which they work.

It has been shown that nearly one quarter of a population is affected by a disability which influences their interaction with the built environments, other individuals, and evacuation policies inhibiting their exit ability during an emergency evacuation. It is predicted that the number of individuals with a disability is on the rise. In the 21st century alone, there have been hundreds of events attributed to stampede or crowd crush, natural disaster, political revolt, terrorism, and other related emergencies. With an increase in the world's population, understanding emergency evacuations and how to best apply them is of growing importance. While research has investigated how crowds interact and evacuate, very little has been investigated in the impacts of how the disabled change an evacuation. While there are some beginnings to affect modeling with heterogeneous behaviors of disabled, little has been known in the analysis of crowds involving individuals with disabilities. There is a need to understand and model such interaction and how it impacts crowd movement. This dissertation implements and develops a novel video tracking system to study heterogeneous crowds with individuals with disabilities towards conducting a large-scale crowd experiment. A large-scale crowd experiment is conducted and the results are analyzed through a developed analysis graphical user interface for use with crowd dynamics experts. Preliminary results of the large-scale crowd experiment demonstrate differences in the velocities and overtaking perception of various groups with disabilities composed of the visually impaired, individuals with motorized and non-motorized wheelchairs, individuals with roller walkers, and individuals with canes or other stamina impairments. This dissertation uses these results to present a hybrid Social Force model that can capture the overall overtake behavior of the empirical data from our crowd experiments. Finally, future research goals are discussed in the eventual development of a Mass Pedestrian Evacuation system for crowds with individuals with disabilities. Lessons from this dissertation are discussed towards goals of crowd control.

Since Hurricane Katrina, extensive studies have been conducted aiming to optimize the transit vehicle routing in the event of an emergency evacuation. However, the vast majority of the studies focus on solving the deterministic vehicle routing problem that all the evacuation data are known in advance. These studies are generally not practical in dealing with real-world problems which involve considerable uncertainty in the evacuation data set. In this dissertation, a SmartEvac system is developed for dynamic vehicle routing optimization in emergency evacuation. The SmartEvac system is capable of processing dynamic evacuation data in real time, such as random pickup requests, travel time change, network interruptions. The objective is to minimize the total travel time for all transit vehicles. A column generation based online optimization model is integrated into the SmartEvac system. The optimization model is based on the following structure: a master problem model and a sub-problem model. The master problem model is used for routes selection from a restricted routes set while the sub-problem model is developed to progressively add new routes into the restricted routes set. The sub-problem is formulated as a shortest path problem with capacity constraint and is solved using a cycle elimination algorithm. When the evacuation data are updated, the SmartEvac system will reformulate the optimization model and generate a new routes set based on the existing routes set. The computational results on benchmark problems are compared to other studies in the literature. The SmartEvac system outperforms other approaches on most of the benchmark problems in terms of computation time and solution quality. CORSIM simulation is used as a test bed for the SmartEvac system. CORSIM Run-Time-Extension is developed for communications between the simulation and the SmartEvac system. A case study of the Hurricane Gustav emergency evacuation is conducted. Different scenarios corresponding to different situations that presented in the Hurricane Gustav emergency evacuation are proposed to evaluate the performance of the SmartEvac system in response to real-time data. The average processing time is 28.9 seconds and the maximum processing time is 171 seconds, which demonstrate the SmartEvac system’s capability of real-time vehicle routing optimization.