Online shortest paths with confidence intervals for routing in a time varying random network

TL;DR

This paper introduces an online shortest path algorithm for time-varying random networks, utilizing stochastic gradient descent and the Frank-Wolfe method to compute confidence intervals for routing in intelligent transport systems.

Contribution

It presents a novel online routing algorithm that combines stochastic gradient descent with the Frank-Wolfe method to estimate shortest paths with confidence intervals.

Findings

Enables confidence interval estimation for shortest paths.

Uses stochastic gradient descent for real-time routing.

Improves traffic management in intelligent transport systems.

Abstract

The increase in the world's population and rising standards of living is leading to an ever-increasing number of vehicles on the roads, and with it ever-increasing difficulties in traffic management. This traffic management in transport networks can be clearly optimized by using information and communication technologies referred as Intelligent Transport Systems (ITS). This management problem is usually reformulated as finding the shortest path in a time varying random graph. In this article, an online shortest path computation using stochastic gradient descent is proposed. This routing algorithm for ITS traffic management is based on the online Frank-Wolfe approach. Our improvement enables to find a confidence interval for the shortest path, by using the stochastic gradient algorithm for approximate Bayesian inference.