A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process

The disruption of critical components in a transportation network can bring about severe network performance degradation and requires a relatively long period to recover, which would lead to commuters’ day-to-day route choice adjustment. Under disruptions, there would be greater travel time variability (objective uncertainty) and travelers’ perception error uncertainty (subjective uncertainty) in the transportation network. However, no vulnerability analysis method in the literature can consider the day-to-day network performance fluctuation under uncertainties. In this paper, we develop a new day-to-day dynamic network vulnerability analysis approach that allows the consideration of day-to-day network performance fluctuation based on a new day-to-day dynamic model considering both objective travel time uncertainty and subjective perception error uncertainty. Compared to most existing day-to-day models that either adopt User Equilibrium (UE) or Logit-based route choice criterion, the new day-to-day model has two advantages: (1) the Weibit model is used to capture travelers’ subjective perception error uncertainty, which does not have the perfect information assumption in the UE model, or the identically distributed perception error assumption in the Logit model; and (2) the mean-excess travel time (METT) concept is used to capture the objective travel time uncertainty, which handles the inconsideration of travel time variability in most day-to-day models while remaining computational tractability. Based on the proposed day-to-day dynamic model, we develop a new component importance metric for network vulnerability analysis. This new metric characterizes the post-disruption day-dependent consequences to alleviate the limitation of only assessing the final static equilibrium consequence as in the existing studies of vulnerability analysis. Numerical examples are provided to demonstrate the features of the proposed day-to-day dynamic model and the new component importance metric, as well as their applicability in identifying the critical bridges in the Winnipeg network. The proposed approach provides a new decision support tool for planners and managers in assessing the consequences of disruptions, identifying the critical components, and determining the recovery schedules after disruptions.