An Iterative Routing/Assignment Method for Anticipatory Real-Time Route Guidance

Abstract

Anticipatory route guidance in traffic networks is based on time-dependent fastest path calculation requiring forecasts of link travel time over a time horizon. These forecasts would be produced by a traffic assignment procedure, which must take into account the behavior of anticipatory vehicles seeking user-optimal route guidance. Thus a conceptual feedback loop occurs. We implement this feedback loop iteratively using simulation for the assignment phase. When the iteration terminates with a fixed-point assignment, user-optimality is achieved. We study the benefits accrued by individual anticipatory vehicles and the system as a whole, as a function of the proportion of vehicles which have anticipatory route guidance, i.e. the market penetration. We observe individual and system benefits at market penetrations up to 40% or higher.

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