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Advanced traveler information systems (ATIS) attempt to provide drivers with data intended to help them make better travel decisions. Messages may be available to all drivers (for example by radio or television broadcasts) or only to some: for example, those who pass near a particular infrastructure (such as variable message signs or VMS) or who have special receivers in their vehicles. Drivers, of course, may react to the messages in any way they choose.

Driver information systems may be distinguished on the basis of the type of information they provide in messages. Static systems furnish information that changes only infrequently: for example, locations of and directions to trip attractions such as cultural centers or restaurants. Reactive systems estimate prevailing travel conditions from real-time measurements and provide messages directly based on these estimates: for example, information about current travel times. Predictive or anticipatory systems use real-time measurements to forecast travel conditions in the near-term future (up to a few hours), and present messages based on these predictions. As a result, a tripmaker can make a decision based on what conditions are likely to be at network locations at the time he/she will actually be there, rather than on (possibly very different) currently prevailing conditions.

The development of anticipatory route guidance (ARG) systems requires sophisticated algorithms that can simulate a dynamic traffic user equilibrium. As with other ATIS technologies, ARG would include a communications system that can transmit dynamic, shortest path traffic data to drivers -- but this shortest-path forecasting approach takes into account changes in driver behavior in order to avoid becoming a self-defeating prophecy.

This exploratory research project developed and evaluated a software system which explores the ARG problem from a fixed point perspective. A significant part of this research consisted of identifying the best algorithms for step size computation. Methods evaluated include: MSA (Method of successive averaging), Polyak iterate averaging method, and a variety of potential optimization line search methods.