  This paper proposes a dual-time-scale, day-to-day dynamic traffic assignment
model that takes into account variable message signs (VMS) and its interactions
with drivers' travel choices and adaptive learning processes. The within-day
dynamic is captured by a dynamic network loading problem with en route update
of path choices influenced by the VMS; the day-to-day dynamic is captured by a
simultaneous route-and-departure-time adjustment process that employs bounded
user rationality. Moreover, we describe the evolution of the VMS compliance
rate by modeling drivers' learning processes. We endogenize traffic dynamics,
route and departure time choices, travel delays, and VMS compliance, and
thereby captur their interactions and interdependencies in a holistic manner. A
case study in the west end of Glasgow is carried out to understand the impact
of VMS has on road congestion and route choices in both the short and long run.
Our main findings include an adverse effect of the VMS on the network
performance in the long run (the 'rebound' effect), and existence of an
equilibrium state where both traffic and VMS compliance are stabilized.
