Publications such as the Highway Capacity Manual (HCM) (1) contain guidelines,
concepts, and procedures for computing the quality of service and capacity of various highway
facilities. HCM is an excellent resource for a sketch evaluation and high-level analysis of quality
of service on planned or existing roadway infrastructure. This tool provides a set of analytical
methods and practices including a logical methodology for assessing transportation facilities, but
is limited in its ability to analyze effects such as oversaturation, queue spillback, dynamic
routing, or peak spreading.
Other prevalent traffic analysis tools include microscopic traffic simulation models.
Microscopic models simulate the movement of individual vehicles based upon car-following,
lane-changing, and gap-acceptance theories. These models are often used to analyze various
geometric design configurations, to evaluate and optimize localized individual intersections, and
to analyze the interactions of multiple modes of transportation including cars, transit, rail, and
pedestrians.
Newer microscopic models are route based, meaning vehicles select a route at departure
and follow that route with or without further update along the journey during simulation. Most
microscopic simulation models provide various ways by which a vehicle’s route at departure or
en route is selected or updated. Each approach is linked to a distinct route choice behavior and,
while such flexibility can be of great convenience to the modeling work at hand, one need to
realize the underlying route-choice behavior assumption associated with each method, as well as
the impact of analysis outcomes depending upon which of the different available mechanisms is
chosen.
For example, the one-shot (noniterative) assignment-simulation approach is commonly
used in some microsimulators, in which vehicles departing at different times are given a route
that is periodically updated in simulation based on instantaneous travel times—snapshot travel
time measured at the time that the routes are generated without considering congestion during
subsequent time periods. Such an assignment can be regarded as if travelers strictly follow some
types of pretrip route guidance. Some microsimulation models allow the en route vehicles to
update their routes based on the updated shortest route generated at a later time. This feature also
implies a route choice behavior that strictly follows the en-route route guidance. While these two
route choice behaviors exist in reality, it is important to realize that the majority of travelers may
choose a route that leads to the minimal experienced travel time instead of minimal instantaneous
travel time. The experienced travel time needs to be evaluated after the fact, by which point the
traffic condition along the entire journey is revealed and experienced. In other words, choosing a
minimal experienced travel time route at departure involves anticipation of future traffic
condition along the journey. This anticipation is usually formed by learning from prior
experience (e.g., try different routes). To account for this learning process, an iterative
algorithmic process is needed. Such an iterative process reflects the learning and adjustment in
route choice from one iteration to the next until the traveler cannot find a route with a shorter
experienced travel time. More details are provided in the section on Instantaneous and
experienced travel times.