The past few years have witnessed an impressive progress in the capabilities of travel information services. It is expected that in a few years, travelers will be constantly informed, pre-trip as well as en-route, about their optimal departure time, route and transport mode. The information is based on a careful monitoring of the transport network as well as travelers??? personal preferences and schedules. Traffic jams, train delays and the like will no longer be unpleasant surprises. Unfortunately, our knowledge concerning how travelers will respond to this stream of increasingly advanced information seriously lags behind these technological advances themselves. A number of important questions have not been addressed adequately yet: To what extent do travelers actually use information available to them? Are they able to deal in an intelligent way with the immense complexity that is associated with travel in nowadays dense and multimodal transport networks? This dissertation answers these and other questions by integrating theories from the fields of microeconomics, psychology, marketing and transportation into mathematical models of traveler behavior. Subsequently, a computer-based travel environment is developed that simulates actual travel situations (involving for example time pressure, traffic jams and train delays). By observing the behavior of hundreds of participants to an experiment using the artificial travel environment, a unique dataset is obtained. Advanced econometrical analyses of the data show that the developed theoretical models form an adequate description of actual traveler behavior. And more importantly, they suggest that travelers are pretty good at dealing intelligently with complex travel situations and sophisticated information services. (Author/publisher)
Abstract