Assessing and Improving the Operational Resilience of a Large Highway Infrastructure System to Worst-Case Losses

The article of record as published may be found at http://dx.doi.org/10.1287/trsc.2017.0749This paper studies the resilience of the regional highway transportation system of the San Francisco Bay Area. Focusing on peak periods for commuter traffic, traffic patterns are computed from a model that includes nonlinear increases in travel times due to congestion and reflects actual travel demands as captured by U.S. Census demographic data. We consider the consequences associated with loss of one or more road, bridge, and/or tunnel segments, where travelers are allowed to reroute to avoid congestion or potentially not travel at all if traffic is bad. We use a sequential game to identify sets of road, bridge, or tunnel segments whose loss results in worst-case travel times. We also demonstrate how the model can be used to quantify the operational resilience of the system, as well as to characterize trade-offs in resilience for different defensive investments, thus providing concise information to guide planners and decision makers.Defense Threat Reduction AgencyOffice of Naval Research and the Air Force Office of Scientific ResearchGrant HDTRA1-10- 1-008

Data-centric methods for optimization and pattern discovery in networked systems

In this thesis, we examine two data-driven solutions to problems in operational networks. The first problem is concerned with assessing the resilience of the US air transportation network from an operational perspective. As a complex network comprising over 5,000 public airports and countless interfaces with other transportation systems, the impact of any disruption in the air network undoubtedly extends to other inter-connected economic and functional domains. Our solution to the resilience assessment problem is a tri-level optimization program that is able to simulate worst-case disruptions in the air network as well as propose the optimal ways to mitigate their effects. These mitigation steps take the form of investment recommendations for the air routes that are in most need of augmentation by other high-speed transportation modes. Our methodology and results for this application are explained in detail in Chapter 3. The second problem discussed in this thesis is centered on identifying design patterns in architecture graph representations of operational systems. Design patterns have been well documented and researched in software systems as a valuable design tool since the nineties. However, their usage has not been significantly expanded beyond software architectures, and their discovery methods have generally remained structured and supervised. We propose an end-to-end, unsupervised graph generation and pattern identification framework that is able to find unknown and potentially useful patterns in architecture graphs using machine learning. Our method is not limited to software systems, and is designed to be able to make possible pattern predictions even with a single architecture graph input. We detail our framework and experimental results in Chapter 4. Organizationally, Chapter 1 of the thesis starts with an introduction to network theory and graph representations, Chapter 2 provides background on network optimization and graph machine learning, and Chapter 5 concludes the thesis with our final thoughts and future research directions