In recent years, natural, man-made and technological disasters have been increasing in magnitude and frequency of occurrence. Terrorist attacks have increased after the September 11, 2001. Some authorities suggest that global warming is partly the blame for the increase in frequency of natural disasters, such as the series of hurricanes in the early-2000's. Furthermore, there has been noticeable growth in population within many metropolitan areas not only in the US but also worldwide. These and other facts motivate the need for better emergency evacuation route planning (EERP) approaches in order to minimize the loss of human lives and property. This research considers aspects of evacuation routing never before considered in research and, more importantly, in practice. Previous EERP models only either consider unidirectional evacuee flow from the source of a hazard to destinations of safety or unidirectional emergency first responder flow to the hazard source. However, in real-life emergency situations, these heterogeneous, incompatible flows occur simultaneously over a bi-directional capacitated lane-based travel network, especially in unanticipated emergencies. By incompatible, it is meant that the two different flows cannot occupy a given lane and merge or crossing point in the travel network at the same time. In addition, in large-scale evacuations, travel lane normal flow directions can be reversed dynamically to their contraflow directions depending upon the degree of the emergency. These characteristics provide the basis for this investigation. This research considers the multiple flow EERP problem where the network travel lanes can be reconfigured using contraflow lane reversals. The first flow is vehicular flow of evacuees from the source of a hazard to destinations of safety, and the second flow is the emergency first responders to the hazard source. After presenting a review of the work related to the multiple flow EERP problem, mathematical formulations are proposed for three variations of the EERP problem where the objective for each problem is to identify an evacuation plan (i.e., a flow schedule and network contraflow lane configuration) that minimizes network clearance time. Before the proposed formulations, the evacuation problem that considers only the flow of evacuees out of the network, which is viewed as a maximum flow problem, is formulated as an integer linear program. Then, the first proposed model formulation, which addresses the problem that considers the flow of evacuees under contraflow conditions, is presented. Next, the proposed formulation is expanded to consider the flow of evacuees and responders through the network but under normal flow conditions. Lastly, the two-flow problem of evacuees and responders under contraflow conditions is formulated. Using real-world population and travel network data, the EERP problems are each solved to optimality; however, the time required to solve the problems increases exponentially as the problem grows in size and complexity. Due to the intractable nature of the problems as the size of the network increases, a genetic-based heuristic solution procedure that generates evacuation network configurations of reasonable quality is proposed. The proposed heuristic solution approach generates evacuation plans in the order of minutes, which is desirable in emergency situations and needed to allow for immediate evacuation routing plan dissemination and implementation in the targeted areas.

Throughout United States and world history, disasters have caused not only significant loss of life, property but also enormous financial loss. The tsunami that occurred on December 26, 2004 is a telling example of the devastation that can occur unexpectedly. This unexpected natural event never happened before in this area. In addition, there was a lack of an emergency response plan for events of that magnitude. Therefore, this event resulted not only in a natural catastrophe for the people of South and Southeast Asia, but it is also considered one of the greatest natural disasters in world history. After the giant wave dissipated, there were more than 230,000 people dead and more than US$10 billion in property damage and loss. Another significant event was the terrorist incident on September 11, 2001 (commonly referred to as 9/11) in United States. This event was unexpected and an unnatural, i.e., man-made event. It resulted in approximately 3,000 lives lost and about US$21 billion in property damage. These and other unexpected (or unanticipated) events give emergency management officials short- or no-notice to prevent or respond to the situation. These and other facts motivate the need for better emergency evacuation route planning (EERP) approaches in order to minimize the loss of human lives and property in short- or no-notice emergency situations. This research considers aspects of evacuation routing that have received little attention in research and, more importantly, in practice. Previous EERP models only either consider unidirectional evacuee flow from the source of a hazard to destinations of safety or unidirectional emergency first responder flow to the hazard source. However, in real-life emergency situations, these heterogeneous, incompatible flows occur simultaneously over a bi-directional capacitated lane-based travel network, especially in short- and no-notice emergencies. After presenting a review of the work related to the multiple flow EERP problem, mathematical formulations are presented for the EERP problem where the objective for each problem is to identify an evacuation routing plan (i.e., a traffic flow schedule) that maximizes evacuee and responder flow and minimizes network clearance time of both types of flow. In addition, we integrate the general human response behavior flow pattern, where the cumulative flow behavior follows different degrees of an S-shaped curve depending upon the level of the evacuation order. We extend the analysis to consider potential traffic flow conflicts between the two types of flow under these conditions. A conflict occurs when flow of different types occupy a roadway segment at the same time. Further, with different degrees of flow movement flow for both evacuee and responder flow, the identification of points of flow congestion on the roadway segments that occur within the transportation network is investigated.

Natural or man-made disasters result in unfortunate events around the nation every year. Such extreme events necessitate the short-notice or no-notice evacuation of a large population from the stricken area. This research presents an optimization modeling technique to develop an evacuation plan for transit-dependent residents during no-notice disaster situation. The proposed plan relies on the application of existing public transit system of an urban area. The public transit routing plan (PTRP) problem is formulated as a mixed integer linear program. The PTRP identifies the optimal routes for transit vehicles to move evacuees from the danger zone to designated safe destinations. A heuristic TABU search algorithm is used to find high-quality solution in a reasonable amount of time. Finally, DYNASMART-P is used to evaluate the effectiveness of the developed PTRP. Numerical experiments are conducted using the traffic network of the city of Fort Worth, TX, to illustrate the proposed modeling technique.

A model for estimating travel time on short arterial links of congested urban networks, using currently available technology, is introduced in this thesis. The objective is to estimate travel time, with an acceptable level of accuracy for real-life traffic problems, such as congestion management and emergency evacuation. To achieve this research objective, various travel time estimation methods, including highway trajectories, multiple linear regression (MLR), artificial neural networks (ANN) and K –nearest neighbor (K-NN) were applied and tested on the same dataset. The results demonstrate that ANN and K-NN methods outperform linear methods by a significant margin, also, show particularly good performance in detecting congested intervals. To ensure the quality of the analysis results, set of procedures and algorithms based on traffic flow theory and test field information, were introduced to validate and clean the data used to build, train and test the different models.