Discrete Time Dynamic Traffic Assignment Models with Lane Reversals for Evacuation Planning

In an event of a natural or man-made disaster, an evacuation is likely to be called for to move residents away from potentially hazardous areas. Road congestion and traffic stalling is a common occurrence as residents evacuate towns and cities for safe refuges. Lane reversal, or contra-flow, is a remedy to increase outbound flow capacities from disaster areas which in turn will reduce evacuation time of evacuees during emergency situations. This thesis presents a discrete-time traffic assignment system with lane reversals which incorporates multiple sources and multiple destinations to predict optimal traffic flow at various times throughout the entire planning horizon. With the realization of lane reversals, naturally the threat of potential head-on collisions emerges. To avoid the occurrence of such situations, a collision prevention constraint is introduced to limit directional flow on lanes based on departure time.;This model belongs to the class of dynamic traffic assignment (DTA) problems. Initially the model was formulated as a discrete-time system optimum dynamic traffic assignment (DTA-SO) problem, which is a mixed integer nonlinear programming problem. Through various proven theorems, a linearized upper bound was derived that is able to approximate the original problem with very high precision. The result is an upper bound mixed integer linear programming problem (DTA-UB). The discrete-time DTA model is suitable for evacuation planning because the model is able to take care of dynamic demands, and temporal ow assignment. Also, simultaneous route and departure is assumed and an appropriate travel time function is used to approximate the minimum and maximum travel time on an arc.;This thesis discusses the different attributes that relates to Dynamic Traffic Assignment. DTA model properties and formulation methodology are also expounded upon. A model analysis that breaks down each output into individual entities is provided to further understand the computational results of small networks. A no reversal DTA-UB model (NRDTA-UB) is formulated and its computational results are compared to DTA-UB. Through the extensive computational results, DTA-UB is proven to obtain much better results than NRDTA-UB despite having longer solving time. This is a step toward realizing the supremacy of having lane reversals in a real-life evacuation scenario

Mixed Integer Programming Approaches to Novel Vehicle Routing Problems

This thesis explores two main topics. The first is how to incorporate data on meteorological forecasts, traffic patterns, and road network topology to utilize deicing resources more efficiently. Many municipalities throughout the United States find themselves unable to treat their road networks fully during winter snow events. Further, as the global climate continues to change, it is expected that both the number and severity of extreme winter weather events will increase for large portions of the US.We propose to use network flows, resource allocation, and vehicle routing mixed integer programming approaches to be able to incorporate all of these data in a winter road maintenance framework. We also show that solution approaches which have proved useful in network flows and vehicle routing problems can be adapted to construct high-quality solutions to this new problem quickly. These approaches are validated on both random and real-world instances using data from Knoxville, TN.In addition to showing that these approaches can be used to allocate resources effectively given a certain deicing budget, we also show that these same approaches can be used to help determine a resource budget given some allocation utility score. As before, we validate these approaches using random and real-world instances in Knoxville, TN.The second topic considered is formulating mixed integer programming models which can be used to route automated electric shuttles. We show that these models can also be used to determine fleet composition and optimal vehicle characteristics to accommodate various demand scenarios. We adapt popular vehicle routing solution techniques to these models, showing that these strategies continue to be relevant and robust. Lastly, we validate these techniques by looking at a case study in Greenville, SC, which recently received a grant from the Federal Highway Administration to deploy a fleet of automated electric shuttles in three neighborhoods

Modeling Lane-based Traffic Flow In Emergency Situations In The Presence Of Multiple Heterogeneous Flows

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\u27s. 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

DEVELOPMENT OF A MIXED-FLOW OPTIMIZATION SYSTEM FOR EMERGENCY EVACUATION IN URBAN NETWORKS

In most metropolitan areas, an emergency evacuation may demand a potentially large number of evacuees to use transit systems or to walk over some distance to access their passenger cars. In the process of approaching designated pick-up points for evacuation, the massive number of pedestrians often incurs tremendous burden to vehicles in the roadway network. Hence, one critical issue in a multi-modal evacuation planning is the effective coordination of the vehicle and pedestrian flows by considering their complex interactions. The purpose of this research is to develop an integrated system that is capable of generating the optimal evacuation plan and reflecting the real-world network traffic conditions caused by the conflicts of these two types of flows. The first part of this research is an integer programming model designed to optimize the control plans for massive mixed pedestrian-vehicle flows within the evacuation zone. The proposed model, integrating the pedestrian and vehicle networks, can effectively account for their potential conflicts during the evacuation. The model can generate the optimal routing strategies to guide evacuees moving toward either their pick-up locations or parking areas and can also produce a responsive plan to accommodate the massive pedestrian movements. The second part of this research is a mixed-flow simulation tool that can capture the conflicts between pedestrians, between vehicles, and between pedestrians and vehicles in an evacuation network. The core logic of this simulation model is the Mixed-Cellular Automata (MCA) concept, which, with some embedded components, offers a realistic mechanism to reflect the competing and conflicting interactions between vehicle and pedestrian flows. This study is expected to yield the following contributions * Design of an effective framework for planning a multi-modal evacuation within metropolitan areas; * Development of an integrated mixed-flow optimization model that can overcome various modeling and computing difficulties in capturing the mixed-flow dynamics in urban network evacuation; * Construction and calibration of a new mixed-flow simulation model, based on the Cellular Automaton concept, to reflect various conflicting patterns between vehicle and pedestrian flows in an evacuation network

Optimal Route Planning with Mobile Nodes in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are a collection of sensor nodes that sense their surroundings and relay their proximal information for further analysis. They utilize wireless communication technology to allow monitoring areas remotely. A major problem with WSNs is that the sensor nodes have a set sensing radius, which may not cover the entire field space. This issue would lead to an unreliable WSN that sometimes would not discover or report about events taking place in the field space. Researchers have focused on developing techniques for improving area coverage. These include allowing mobile sensor nodes to dynamically move towards coverage holes through the use of a path planning approach to solve issues such as maximizing area coverage. An approach is proposed in this thesis to maximize the area of network coverage by the WSN through a Mixed Integer Linear Programming (MILP) formulation which utilizes both static and mobile nodes. The mobile nodes are capable of travelling across the area of interest, to cover empty ‘holes’ (i.e. regions not covered by any of the static nodes) in a WSN. The goal is to find successive positions of the mobile node through the network, in order to maximize the network area coverage, or achieve a specified level of coverage while minimizing the number of iterations taken. Simulations of the formulation on small WSNs show promising results in terms of both objectives

Decentralized approaches for admission, routing and flow problems

Online control of large dynamic networks is challenging because little data are in general available about the environment, because decentralized strategies have to be employed, which rely on the knowledge of local data only, and because faults can occur. In this regard, three problems are addressed in this thesis. The first problem concerns the scheduling of some requests of a limited resource occurring at different times, from a supplier with limited capacity. The goal is that of minimizing the average waiting time for these requests. The problem is formulated as an optimal control one, in which the control is the supply strategy, specified by some constraints, and the state variables are associated with the waiting times of the demands. The exact optimal problem requires mixed-integer linear programming; some relaxed versions are also formulated and, in particular, one of these is based on linear programming and efficiently provides some lower bound. Some online heuristics are analyzed, both centralized and decentralized, for which, in general, no a-posteriori optimality of the solution is obtained. The second problem is an agent-based minimum path one. Some tokens (agents) are injected in the network, in some source nodes, and must travel in the network to find an exit, a sink. A simple decentralized policy is proposed. This policy allows or denies the transitions of the tokens along the arcs on the basis of a simple local threshold mechanism. In particular, a transition occurs through a directed arc if the amount of tokens present in the origin node minus the amount tokens present in the end node exceeds the arc cost. Despite the very simple local mechanism, in the long run, all the injected tokens leave the network by the closest sink through the shortest path, although some tokens are, unavoidably, lost during the initial transient exploring phase. This issue can be avoided by enhancing the policy allowing the generation of some virtual tokens. Some constraints to the maximum number of transitions can also be imposed to all tokens. In fact, this is equivalent to applying the policy proposed for the unconstrained case to the so-called expanded network. The third problem considers flow networks with buffers in the nodes containing an amount of a continuous resource at a given level (state), which is transferred between nodes by controlled flows along the arcs. A decentralized control is formulated to meet a given flow demand, stabilize the system and asymptotically minimize the p-norm of the flow. This control is specified at the arc level and depends only on the value of p and on the difference of the states of the two arc's endnodes. After an initial transient, the unique optimal desired flow is obtained when 1 < p < ∞. When p = 1 sparsity of the solution tends to emerge, while when p = ∞ fairness is promoted; however, no optimality or uniqueness of solution is achieved in these two cases: suboptimal solutions can be obtained by applying the control with p → 1 and p → ∞, respectively. Enhancements can be applied to support uncontrollable flows governed by unknown dynamics depending on the buffer levels and buffer level control to a desired set-point. In this case, a decentralized proportional-integral control is adopted