Nathanael J. K. Brown

Approximate Solution Procedure for Dynamic Traffic Assignment

This paper proposes an approximate dynamic traffic assignment algorithm for the analysis of traffic conditions in large-scale road networks over several days. The time-dependent origin-destination trips are assumed to be known. A case study for evacuation of the New Orleans metropolitan area prior to the landfall of Hurricane Katrina is presented to test the efficiency and effectiveness of the proposed procedure. The model results are compared to the traffic counts collected during the evacuation and then further tested by the mesoscopic simulation-based model, DynusT. The study shows that the traffic pattern produced by the proposed procedure is a good approximation to traffic count data and that the algorithm provides a good approximation to the computations performed by DynusT.

Optimization-Based Probabilistic Consequence Scenario Construction for Lifeline Systems

The construction of a suite of consequence scenarios that is consistent with the joint distribution of damage to a lifeline system is critical to properly estimating regional loss after an earthquake. This paper describes an optimization method that identifies a suite of consequence scenarios that can be used in regional loss estimation for lifeline systems when computational demands are of concern, and it is important to capture the spatial correlation associated with individual events. This method is applied to a realistic case study focused on the highway network in Memphis, Tennessee, within the New Madrid Seismic Zone. This case study illustrates that significantly fewer consequence scenarios are needed with this method than would be required using Monte Carlo simulation.

Multi-objective optimization for bridge retrofit to address earthquake hazards

Protecting infrastructures against natural hazards is a pressing national and international problem. Given the current budgetary climate, the ability to determine the best mitigation strategies with highly constrained budgets is essential. This papers describes a set of computationally efficient techniques to determine optimal infrastructure investment strategies, given multiple user objectives, that are consistent with an underlying earthquake hazard. These techniques include: optimization methods for developing representative events to characterize the hazard and the post-event condition of infrastructure components, a simulation model to characterize post-event infrastructure performance relative to multiple user objectives, and a multi-objective optimization algorithm for determining protection strategies. They are demonstrated using a case study of the highway network in Memphis, Tennessee.

The Impact of False and Nuisance Alarms on the Design Optimization of Physical Security Systems

Despite the known degrading impact of high nuisance and false alarm rates (NAR/FAR) on operator performance, analyses of security systems often ignores operator performance. We developed a model to analyze the impact of nuisance alarm rates on operator performance and on overall system performance. The model demonstrates that current methods that do not account for operator performance produce optimistic estimates of system performance. As shown in our model, even low NAR/FAR levels and the associated alarm queueing effect can increase operator detect and response time, which in turn reduces the amount of time the response force has to interrupt the intruder. An illustrative analysis shows that alarm processing times can be higher than the assessment time due to queue wait times and that systems with only one or two operators can become overwhelmed as NAR increases, decreasing system performance.

Multi-layered security investment optimization using a simulation embedded within a genetic algorithm

The performance of a multi-layered security system, such as those protecting high-value facilities or critical infrastructures, is characterized using several different attributes including detection and interruption probabilities, costs, and false/nuisance alarm rates. The multitude of technology options, alternative locations and configurations for those technologies, threats to the system, and resource considerations that must be weighed make exhaustive evaluation of all possible architectures extremely difficult. This paper presents an optimization model and a computationally efficient solution procedure to identify an estimated frontier of system configuration options which represent the best design choices for the user when there is uncertainty in the response time of the security force, once an intrusion has been detected. A representative example is described.

Optimization of scenario construction for loss estimation in lifeline networks

Natural disasters have become a pressing national and international problem. Population growth, aging infrastructure, and climate change suggest that mounting losses will continue into the foreseeable future, hence mitigation and response planning is of increasing importance. The conduct of studies to support this type of regional planning often requires an estimation of the impacts of a single earthquake scenario on a region. This paper describes a method to identify a set of consequence scenarios that can be used in regional loss estimation for lifeline systems when computational demands are of concern, and the spatial coherence of individual consequence scenarios is important. This method is compared with Monte Carlo simulation.

A Stochastic Programming Approach to the Design Optimization of Layered Physical Protection Systems

The performance of many of the technologies used in physical protection systems that guard highvalue assets are heavily influenced by weather and visibility conditions as well as intruder capabilities. This complicates the already difficult problem of optimizing the design of multi-layered physical protection systems. This paper develops an optimization model for the automatic design of these systems with explicit consideration of the impact of weather and visibility conditions as well as intruder capabilities on system performance. An illustrative case study is provided.

A modeling framework for investment planning in interdependent infrastructures in multi-hazard environments.

Currently, much of protection planning is conducted separately for each infrastructure and hazard. Limited funding requires a balance of expenditures between terrorism and natural hazards based on potential impacts. This report documents the results of a Laboratory Directed Research&Development (LDRD) project that created a modeling framework for investment planning in interdependent infrastructures focused on multiple hazards, including terrorism. To develop this framework, three modeling elements were integrated: natural hazards, terrorism, and interdependent infrastructures. For natural hazards, a methodology was created for specifying events consistent with regional hazards. For terrorism, we modeled the terrorists actions based on assumptions regarding their knowledge, goals, and target identification strategy. For infrastructures, we focused on predicting post-event performance due to specific terrorist attacks and natural hazard events, tempered by appropriate infrastructure investments. We demonstrate the utility of this framework with various examples, including protection of electric power, roadway, and hospital networks.