In-laboratory experimentation with interactive microcomputer simulation is a useful tool for studying the dynamics of driver behavior in response to advanced traveler information systems. Limited real-world implementation of these information systems has made it difficult to observe and study how drivers seek, acquire, process, and respond to real-time information. This paper describes the design and preliminary testing of an interactive microcomputer-based animated simulator, developed at the University of California, Irvine, to model pre-trip and enroute driver travel choices in the presence of advanced traveler information systems. The advantages of this simulator are realized in its versatility to model driver decision processing while presenting a realistic representation of the travel choice domain. Results from a case study revealed that increased driver familiarity with travel conditions and network layout reduces driver reliance on information systems and influences drivers diversion behavior.

The focus of this research effort is the study of driver behavior in the presence of real-time traffic condition information. The methodology adapted for this research involves three parts: development of a theoretical model for driver behavior under Advanced Traveler Information Systems (ATIS), interactive simulation experiments, data analysis and behavioral modeling. FASTCARS, an interactive computer-based simulator that has been developed for in-laboratory experimentation to gather data for estimating and calibrating predictive models of driver behavior under conditions of real-time information, is used in the project.

This paper proposes a theoretical methodology and practical data collection approach for modeling enroute driver behavioral choice under Advanced Traveler Information Systems (ATIS). The theoretical framework is based on conflict assessment and resolution theories popularized in psychology and applied to models of individual consumer behavior. It is posed that enroute assessment and adjustment is a reactionary process influenced by increased conflict arousal and motivation to change. When conflict rises to a level at which conflict exceeds a personal threshold of tolerance, drivers are likely to alter enroute behavior to alleviate conflict through either route diversion or goal revision. Assessment and response to conflict arousal directly relate to the driver's abilities to perceive and predict network conditions in conjunction with familiarity of network configurations and accessible alternate routes.

Data collection is accomplished through FASTCARS (Freeway and Arterial Street Traffic Conflict Arousal and Resolution Simulator), an interactive microcomputer-based driving simulator. Limited real-world implementation of ATIS has made it difficult to study or predict individual driver reaction to these technologies. It is contended here that in-laboratory experimentation with interactive route choice simulators can substitute for the lack of real-world applications and provide an alternate approach to data collection and driver behavior analysis. This paper will explain how FASTCARS is useful for collecting data and testing theories of driver behavior.

Increasing efforts in Advanced Traveler Information Systems has emphasized the need to develop more robust models of enroute driver behavior. The complexity in modeling driver behavior stems from the need to capture the day-to-day dynamics of choice, model diversion and active information acquisition, and account for individual preferences and needs. Previous papers by the authors discussed a conflict arousal methodology for modeling driver behavior in the presence of real-time information. It was posed that observed changes in enroute driver behavior, characterized by diversion or active information acquisition, are evoked through latent factors of conflict arousal and motivation. In-laboratory interactive simulation was used to collect enroute behavior data. This paper presents a structural equation approach for modeling enroute driver behavior and explaining causal factors of diversion and information acquisition as well as the interrelationship between these observed behaviors.

It has been contended that in-laboratory experimentation with interactive microcomputer simulation can substitute for the lack of real-world applications and provide a useful approach to data collection and driver behavior analysis. With the rapid development but limited real-world deployment of Advanced Traveler Information Systems, interactive simulation has quickly grown in popularity among researchers studying dynamic driver behavior. This paper discusses the development and implementation of FASTCARS (Freeway and Arterial Street Traffic Conflict Arousal and Resolution Simulator), an interactive microcomputer-based animated simulator designed for in-laboratory experimentation to assist in the estimation and calibration of predictive models of driver behavior under the influence of real-time information.

This paper discusses preliminary results from an in-laboratory experiment to study enroute driver behavior under ATIS. The case study was conducted using FASTCARS (Freeway and Arterial Street Traffic Conflict Arousal and Resolution Simulator), an interactive microcomputer-based travel choice simulator. The experiment was designed to both exhibit the value of using computer simulation for data collection and to explore factors that influence and induce changes in enroute driver behavior. A range of statistical methods were applied on both a driver and an enroute event basis to investigate underlying relationships between driver behavior and the selection and utilization of real-time information technologies. Logit models were developed for both primary and secondary diversion behavior, incorporating variables that capture the utility choice associated with enroute decision processes. Utility assessment modeling was performed to examine the potential benefits of in-vehicle navigation systems. The analyses suggest that driver familiarity both with travel conditions and network layout strongly influences driver behavior and need to acquire information. The initial results indicate that although real-time information acquisition is generally useful for clarifying drivers' perceptions of travel conditions and assisting with route choice decisions, the value of information acquisition may decrease among more experienced drivers.