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We investigate algorithms to find short paths in spatial networks withstochastic edge weights. Our formulation of the problem of finding short pathsdiffers from traditional formulations because we specifically do not make twoof the usual simplifying assumptions: (1) we allow edge weights to bestochastic rather than deterministic; and (2) we do not assume that globalknowledge of a network is available. We develop a decentralized routingalgorithm that provides en route guidance for travelers on a spatial networkwith stochastic edge weights without the need to rely on global knowledge aboutthe network. To guide a traveler, our algorithm uses an estimation functionthat evaluates cumulative arrival probability distributions based on distancesbetween pairs of nodes. The estimation function carries a notion of proximitybetween nodes and thereby enables routing without global knowledge. In testingour decentralized algorithm, we define a criterion that allows one todiscriminate among arrival probability distributions, and we test our algorithmand this criterion using both synthetic and real networks.

Decentralized Routing on Spatial Networks with Stochastic Edge Weights