Mid-Term Simulator
The mid-term (MT) level simulates daily travel at the
household and individual level. It is categorized as a mesoscopic simulator
since it combines activity-based microsimulator on the demand side with
macroscopic simulation at the supply side. Figure 4 presents the modeling
framework of the MT simulator implemented in SimMobility. Detail description of
each component of the MT model can be found in (26). The demand comprises two groups of behavior models: pre-day
and within-day. The pre-day models follows an enhanced version of econometric
Day Activity Schedule approach (presented in (27)) to decide an initial overall daily activity schedule of the
agent, particularly its activity sequence (including tours and sub-tours), with
preferred modes, departure times by half-hour slots, and destinations. This is
based on sequential application of hierarchical discrete choice models using a
monte-carlo simulation approach.
Figure. Framework of the
Mid-Term Simulator
As the day unfolds, the agents apply the within-day models
to find the routes for their trips and transform the activity schedule into
effective decisions and execution plans. Through the publish/subscribe
mechanism of event management, as mentioned above, agents may get involved in a
multitude of decisions, not constrained to the traditional set of destination,
mode, path and departure time depending upon their state in the event
simulation cycle. For example, the agent could reschedule the remainder of the
day, cancel an activity (or transfer it to another household member), re-route
in the middle of a trip (including alighting a bus to change route), or run an
opportunistic activity, like shopping while waiting. The supply simulator
follows the dynamic traffic assignment(DTA) paradigm as used previously in
DynaMIT (5), including bus and
pedestrian movements. Particularly for public transport, MT model allows for
bus (and subway) line scheduling and headway based operations are currently
being implemented. We also explicitly represent on-road bus stops and bus bays
both at the mid-term and short-term, which allows for accurate estimation of
impacts of the bus operations on the road traffic. Within the MT simulator, the
interaction between the within-day and supply is responsible to bring the
system to consistency. In addition to this, a day-to-day learning module, which
feeds back network performance to the pre-day model, is introduced to update
agent’s knowledge (either as a calibration procedure or for a multiple day
simulation).