  To help mitigate road congestion caused by the unrelenting growth of traffic
demand, many transportation authorities have implemented managed lane policies,
which restrict certain freeway lanes to certain types of vehicles. It was
originally thought that managed lanes would improve the use of existing
infrastructure through demand-management behaviors like carpooling, but
implementations have often been characterized by unpredicted phenomena that are
sometimes detrimental to system performance. The development of traffic models
that can capture these sorts of behaviors is a key step for helping managed
lanes deliver on their promised gains. Towards this goal, this paper presents
an approach for solving for driver behavior of entering and exiting managed
lanes at the macroscopic (i.e., fluid approximation of traffic) scale. Our
method is inspired by recent work in extending a dynamic-system-based modeling
framework from traffic behaviors on individual roads, to models at junctions,
and can be considered a further extension of this dynamic-system paradigm to
the route/lane choice problem. Unlike traditional route choice models that are
often based on discrete-choice methods and often rely on computing and
comparing drivers' estimated travel times from taking different routes, our
method is agnostic to the particular choice of physical traffic model and is
suited specifically towards making decisions at these interfaces using only
local information. These features make it a natural drop-in component to extend
existing dynamic traffic modeling methods.
