Traffic congestion is a widespread epidemic that continually wreaks havoc in urban areas. Traffic jams, car wrecks, construction delays, and other causes of congestion, can turn even the biggest highways into a parking lot. Several congestion mitigation strategies are being studied, many focusing on micro-simulation of traffic to determine how modifying road structures will affect the flow of traffic and the networking perspective of vehicle-to-vehicle communication. Vehicle routing on a network of roads and intersections can be modeled as a distributed constraint optimization problem and solved using a range of centralized to decentralized techniques. In this paper, we present a constraint optimization model of a traffic routing problem. We produce congestion data using a sinusoidal wave pattern and vary its amplitude (saturation) and frequency (vehicle waves through a given intersection). Through empirical evaluation, we show how a centralized and decentralized solution each react to unknown congestion information that occurs after the initial route planning period.
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