In recent years information systems are being designed to assist
people to make more efficient travel choices under conditions of
growing uncertainty. Although travel demand modelers have analyzed
the response to real-time information, usually this has been done
under the questionable assumptions of rational decision-making. In
reality, people can make completely different choices when their
decisions are based on information or on experience. Improving
behavioral assumptions could well increase the realism of transport
demand models. This book describes an experimental approach to
study route choice with real-time information. It provides both
researchers and practitioners with valuable knowledge on the roles
of information and learning in travel behavior and how statistics
and state-of-the art discrete choice models can be applied to
analyze and model travel behavior in risky and uncertain
environments. This book will be especially useful to transportation
policy makers and analysts, researchers and students.
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