A Framework for Efficient Dynamic Routing under Stochastically Varying Conditions
Nikki Levering, Marko Boon, Michel Mandjes, Rudesindo N\'u\~nez-Queija

TL;DR
This paper presents a new stochastic routing framework that uses real-time ITS data and a Markov-modulated process to optimize vehicle routes under varying traffic conditions, improving travel time predictions.
Contribution
It introduces a novel stochastic process incorporating ITS information and develops the EDSGER* algorithm for real-time dynamic routing in complex networks.
Findings
EDSGER* algorithm effectively computes routes in real-time
The model captures both recurrent and non-recurrent congestion effects
Numerical tests show improved routing decisions using real data
Abstract
Despite measures to reduce congestion, occurrences of both recurrent and non-recurrent congestion cause large delays in road networks with important economic implications. Educated use of Intelligent Transportation Systems (ITS) can significantly reduce travel times. We focus on a dynamic stochastic shortest path problem: our objective is to minimize the expected travel time of a vehicle, assuming the vehicle may adapt the chosen route while driving. We introduce a new stochastic process that incorporates ITS information to model the uncertainties affecting congestion in road networks. A Markov-modulated background process tracks traffic events that affect the speed of travelers. The resulting continuous-time routing model allows for correlation between velocities on the arcs and incorporates both recurrent and non-recurrent congestion. Obtaining the optimal routing policy in the…
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Taxonomy
TopicsTransportation Planning and Optimization · Traffic Prediction and Management Techniques · Traffic control and management
