Decentralized Routing on Spatial Networks with Stochastic Edge Weights

TL;DR

This paper introduces a decentralized routing algorithm for spatial networks with stochastic edge weights, enabling route guidance without global network knowledge by estimating arrival probabilities based on local information.

Contribution

It presents a novel decentralized routing method that accounts for stochastic edge weights and does not require global network knowledge, unlike traditional approaches.

Findings

Algorithm effectively guides travelers in synthetic networks.

Algorithm performs well on real-world network data.

Estimation function accurately predicts arrival probabilities.

Abstract

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