  In the area of urban transportation networks, a growing number of day-to-day
(DTD) traffic dynamic theories have been proposed to describe the network flow
evolution, and an increasing amount of laboratory experiments have been
conducted to observe travelers' behavior regularities. However, the
"communication" between theorists and experimentalists has not been made well.
This paper devotes to 1) detecting unanticipated behavior regularities by
conducting a series of laboratory experiments, and 2) improving existing DTD
dynamics theories by embedding the observed behavior regularities into a route
choice model. First, 312 subjects participated in one of the eight
decision-making scenarios and make route choices repeatedly in congestible
parallel-route networks. Second, three route-switching behavior patterns that
cannot be fully explained by the classic route-choice models are observed.
Third, to enrich the explanation power of a discrete route-choice model,
behavioral assumptions of route-dependent attractions, i.e., route-dependent
inertia and preference, are introduced. An analytical DTD dynamic model is
accordingly proposed and proven to steadily converge to a unique equilibrium
state. Finally, the proposed DTD model could satisfactorily reproduce the
observations in various datasets. The research results can help transportation
science theorists to make the best use of laboratory experimentation and to
build network equilibrium or DTD dynamic models with both real behavioral basis
and neat mathematical properties.
