  In the era of connected and automated mobility, commuters will possess strong
computation power, enabling them to strategically make sequential travel
choices over a planning horizon. This paper investigates the multiday traffic
patterns that arise from such decision-making behavior. In doing so, we frame
the commute problem as a mean-field Markov game and introduce a novel concept
of multiday user equilibrium to capture the steady state of commuters'
interactions. The proposed model is general and can be tailored to various
travel choices such as route or departure time. We explore a range of
properties of the multiday user equilibrium under mild conditions. The study
reveals the fingerprint of user inertia on network flow patterns, particularly
for foresighted commuters, causing between-day variations even at a steady
state. Furthermore, our analysis establishes critical connections between the
multiday user equilibrium and conventional Wardrop equilibrium.

Optimization and Control