This study used focus groups and surveys to provide a comprehensive evaluation of the Regional Traveler Information Center (RTIC) at UMass Amherst. The evaluation was completed by obtaining the awareness, usage, and perceived effectiveness of RTIC’s information by residents in the Pioneer Valley. It was found that awareness of RTIC is limited due to its lack of advertisement. Usage is focused primarily on its webcams and advisory information. Surveys showed that participants perceive RTIC to be useful, even though they may never have seen the information before (the survey provided a chance for them to become familiar with the service). Revealed preference data were collected regarding the travelers' most memorable instances where real-time traffic information was provided. A binary logit model of a traveler's switch decision (route, departure time, mode, destination, trip cancellation, or combinations of them) with real-time traffic information was specified and estimated. It was found that travelers have an increasing tendency to switch away from the original option when the resulting delay caused by congestion increases. Receiving congestion and crash information also provided a tendency to take an alternative travel method. It was found that males tend to switch more often than females, and young individuals switch less often.

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