Investigation of Route Guidance Generation Issues by Simulation with DynaMIT

1. Introduction By providing tripmakers with information about travel options and so allowing them to make better travel decisions, Advanced Traveler Information Systems (ATIS) promise to enhance the utilization of existing network infrastructure and to help manage congestion. Of particular interest here are route guidance systems, which inform road users about traffic network conditions (link traversal times, queue lengths, etc.) and/or suggest a path to follow from their current position to their ultimate destination. We are specifically interested in anticipatory route guidance, in which real-time traffic measurements are combined with other data to make short-term predictions of road network traffic conditions, and these predictions are the basis of the guidance information that is provided to drivers. Anticipatory route guidance is likely to be more effective than route guidance based on historical or on current traffic conditions because it accounts for the probable evolution of traffic conditions over time and throughout the network, and it bases the information provided to a driver on the traffic situation that is forecast to prevail at network locations at the time the driver will actually arrive there. Simulation experiment results and theoretical analyses of small-scale networks have generally confirmed this expectation Any method for generating anticipatory route guidance must confront the following fundamental issue: anticipatory guidance is derived from predictions of future conditions, but these conditions will themselves be affected by drivers' reactions (whatever they may be) to the guidance that they receive. It is therefore necessary to ensure that anticipatory guidance is consistent—in other words, that the forecasts on which the guidance is based are the same, within the limits of modeling accuracy, as the outcome that is predicted to result when drivers react to the guidance. Clearly, adequate treatment of route guidance generation requires explicit incorporation of information as a problem component: applicable models must take account of the way in which information is disseminated by the communication system, and of the way in which this information is reacted to by drivers. Furthermore, route guidance generation requires an algorithmic approach capable of addressing the complexities which information introduces into dynamic network modeling. To be useful in an operational environment, a route guidance generation method must also confront a number of practical issues. Perhaps the most difficult of these is the time constraint: guidance generation may involve very considerable amounts of computation which must be completed quickly and accurately enough for the results to …