  This paper presents a doubly dynamic day-to-day (DTD) traffic assignment
model with simultaneous route-and-departure-time (SRDT) choices while
incorporating incomplete and imperfect information as well as bounded
rationality. Two SRDT choice models are proposed to incorporate imperfect
travel information: One based on multinomial Logit (MNL) model and the other on
sequential, mixed multinomial/nested Logit model. These two variants, serving
as based models, are further extended with two features: bounded rationality
(BR) and information sharing. BR is considered by incorporating the
indifference band into the random utility component of the MNL model, forming a
BR-based DTD stochastic model. A macroscopic model of travel information
sharing is integrated into the DTD dynamics to account for the impact of
incomplete information on travelers' SRDT choices. These DTD choice models are
combined with within-day dynamics following the Lighthill-Whitham-Richards
(LWR) fluid dynamic network loading model. Simulations on large-scale networks
(Anaheim) illustrate the interactions between users' adaptive decision making
and network conditions (including local disruption) with different levels of
information availability and user behavior. Our findings highlight the need for
modeling network transient and disequilibriated states, which are often
overlooked in equilibrium-constrained network design and optimization. The
MATLAB package and computational examples are available at
https://github.com/DrKeHan/DTD

Physics and Society;Optimization and Control