Real-time traffic information provided by GDOT has proven invaluable for commuters in the
Georgia freeway network. The increasing number of Variable Message Signs, addition of
services such as My-NaviGAtor, NaviGAtor-to-go etc. and the advancement of the 511 traffic
information system will require the Traffic Management Center to provide more detailed and
accurate traffic information to an increasing number of users. In this context, the ability to
forecast traffic conditions (both in space and time) would augment the services provided by
NaviGAtor by allowing users to plan ahead for their trip. Forecasts built into the estimation
model will make the travel-time estimates more accurate by reducing the use of stale data.
Additionally, spatial forecast can help GDOT provide reliable information in areas with
temporary outages in coverage; e.g. outages due to detector or cameras malfunction.
The vast majority of real-time travel-time estimation algorithms proposed in the literature are
based on data mining techniques. Unfortunately, this approach is unable to produce reliable
forecasts because it does not take into account traffic dynamics (e.g., via a simulation model).
In Germany, a simulation-based forecast system is in place at most metropolitan areas, with
very favorable user impacts. Although successful, the German example is based on a type of
simulation model (a Cellular Automata model) that has critical drawbacks: difficulty of
calibration, inability to incorporate different user classes (e.g., cars and trucks), and inadequate
capability of replicating detailed traffic dynamics on freeways. The model proposed in this
study overcomes these drawbacks by incorporating the latest advances in traffic flow theory and
simulation.
This study demonstrated the use of a simulation based framework to make short-term traveltime
predictions in real-time. The results show that sufficiently accurate 5-minute and 10-
minute predictions can be made using this framework. The lessons learned from the study
stresses that it is critical to adequately calibrate the simulation model and for this purpose it is
essential to accurately calibrate the vehicle detection sensors. Currently, the simulation is
manually initiated each time a new OD matrix becomes available. For a seamless
implementation, the initiation process needs to be automated. In future studies the researcher
would like to automate the simulation to run continuously by getting sufficient predictions from
a run, pausing the simulation until the next OD update is available, and updating the OD flows
and initial queues. When incidents occur, the corresponding lane blockage can be incorporated
in the simulation before predictions are made.