The ability to model the disruptions of adverse events on various systems, such as infrastructural and social, is an important tool to assessing these systems’ resilience. While previous research on system resilience concentrated on physical infrastructure such as transportation systems, two recent research topics include social resilience and dependencies across many infrastructure systems. For example, transportation is dependent on such systems as power, communications, and the workforces that are key to restoring these infrastructure systems. This dissertation contains three disruption modeling studies that have followed the evolution of resilience research over the past decade from physical systems to interrelated topics. The first study uses mesoscopic traffic simulation to evaluate seismic risk of potential travel time increases from earthquake damage to bridges in a roadway network. This analysis successfully obtained system risk curves of network-wide travel time increases. The second study shifts focus towards workforces that participate in restoring infrastructure systems. It identifies transportation and communications workers and calculates these workers’ exposure to the Peak Ground Accelerations (PGAs) of a 7.8 magnitude Southern California scenario earthquake. Indeed, for this scenario, transportation workers are exposed to statistically significant higher PGAs than non-transportation workers, and communication workers to significantly lower PGAs. The third study proposes a model for the travel time of information along communication-equipped vehicles physically traveling in a network. Vehicles are sampled as equipped vehicles, then their trajectories are analyzed to (1) estimate equipped vehicle link flow and turning movement counts and (2) estimate the frequency of equipped vehicles encountering each other on links and at nodes. This study compares two scenarios: the baseline scenario and a work zone scenario that corresponds to a bridge being damaged in the network. It is hypothesized that there would arise a difference in expected path travel times when (1) the representation of a specified subpath within the sample is increased and (2) when vehicles are routed along currently unused subpaths. This dissertation concludes with a discussion of the contributions of all three studies, as well as suggestions for future work.

Local transportation agencies typically rely on traditional travel demand forecasting models that focus on highway and roadway improvements to optimize vehicular traffic.  These models are not equipped to evaluate active transportation strategies which align with current State of California policies such as reducing vehicle miles traveled to cut greenhouse gas emissions and fostering active transportation modes.  In this context, ITS at UC Irvine (ITS Irvine) was invited by Orange County Transportation Authority (OCTA) to propose, develop, and apply an approach to better model active transportation. This report represents the first phase of this work, which is a review of the recent literature on how to model demand for active transportation and an examination of OCTAM’s (OCTA’s own regional travel demand model) Active Transportation (AT) modeling tool to evaluate its potential for modification or incorporation into a new active transportation model.

The following observations/suggestions are offered in this report: First, that OCTAM AT does not include variables that could impact people’s decision to leave their vehicles at home in favor of transit. Second, a number of conditions need to be jointly met for people to walk or bike. Third, OCTAM AT does not capture residential self-selection, which could be important here as people who do not plan to walk/bike self-select into car-oriented neighborhoods.