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

This 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.