The traffic network is subject to random disruptions, such as incidents, bad weather, or other drivers’ random behavior. A traveler’s route choice behavior in such a network is thus affected by the probabilities of such disruptions, his/her attitude towards risk, and real-time information on revealed traffic conditions that could potentially reduce the level of uncertainty due to the disruptions. As the road network’s performance is de-termined collectively by all travelers’ choices, it is also affected by these factors. This thesis features the development of a multi-class user equilibrium model based on hetero-geneous risk attitude distributions and a user equilibrium model based on various disrup-tion probabilities and information penetration rates that can be used to perform sensitivity analyses for a traffic network. The method of successive average (MSA) is used to solve for the equilibrium conditions. Laboratory experimental data are used to calibrate the risk attitude model. A sample sensitivity analysis is performed to show the disruption and in-formation penetration effects on network performance. Initial calibrations show promis-ing results for route flow predictions in a congested network with respect to heterogene-ous attitude. With respect to disruption probability and information access, having too v high information penetration will not improve the network’s performance, while having a small disruption probability can improve traffic conditions in the network