In an adversarial setting, a transportation network's capacity is influenced by both the defender's protective measures and the attacker's actions, which include substituting targets and attack methods in response to security measures. Decision makers need a methodology that can capture the complex attacker–defender interactions and help them understand the overall effects on the transportation system, as well as the consequences of asset failure. This article presents such a methodology, which uses probabilities of target–attack method combinations that are degree of belief based and updated using Bayes' Theorem after evidence of the attack is obtained. Monte Carlo simulation generates the probability of link capacity effects by sampling from distributions of capacity reductions due to preevent security measures, substitutions, target failure, and postevent security measures. The average capacity reduction for a particular target–attack method combination serves as an input to the traffic assignment–simulation package DYNASMART-P to determine travel time effects. The methodology is applied to a sample network based on the northern Virginia area. Results based on notional data indicated that preevent security measures reduced attack probabilities, but in some cases increased the mobility consequences. Thus, decision makers must carefully evaluate the effects of their decisions.
