Abstract
This dissertation proposes a theoretical methodology and practical data collection approach for modeling enroute driver behavior and explaining drivers’ decisions to divert and acquire real-time traffic condition information. Limited real-world implementation of Advanced Traveler Information Systems (ATIS) technologies has made it difficult to analyze the potential impact on driver behavior. It is contended here that in-laboratory experimentation with interactive simulation can provide a novel and effective approach to data collection and driver behavior analyses. The theoretical framework is based on conflict assessment and resolution theories and describes changes in enroute behavior as a response to drivers’ perceived inability to achieve travel objectives. Conflict is modeled as a latent theoretical concept that describes increased frustration and anxiety experienced by drivers when expected conditions are deteriorating and the desired travel objectives may not be achieved. Motivation to decrease conflict provides the impetus for drivers to adapt enroute behavior by diverting, acquiring additional information, or revising the travel objectives. A case study to examine special event traffic was conducted and several modeling techniques were used to systematically evaluate enroute behavior and the potential impacts of ATIS. Data collection is accomplished through FASTCARS, a computer-based interactive simulation designed to simulate driver decisions and emulate ATIS technologies. Initial empirical results from the analyses are presented to verify the theoretical formulation and modeling strategies.