Low-Rank Cyclostationarity Predictive Routing Is Almost as Good as Real-Time Data-based Routing

TL;DR

This paper introduces a low-rank cyclostationarity-based predictor for traffic routing that nearly matches real-time data-based routing performance, offering a computationally efficient alternative for offline transportation planning.

Contribution

The paper presents a novel low-rank spatiotemporal traffic predictor that achieves near real-time routing accuracy using offline data, improving transportation planning methods.

Findings

Average excess travel time less than 1.5 minutes

Predictor's tail matches near-real-time predictor

Effective over one year of traffic data

Abstract

Dynamic shortest-path routing, using real-time traffic data, enables path selection responsive to evolving conditions. Nevertheless, transportation planning tasks such as adaptive congestion pricing, fleet routing, and long-term operational decisions rely on offline traffic estimators. To address this problem, we develop a spatiotemporal predictor based on a low-rank decomposition of the traffic matrix and the temporal subspace coefficients. Using a recent large-scale measurement campaign over the Seoul road network, we show that our proposed predictor incurs an average excess travel time of less than 1.5 minutes. Moreover, our predictor's tail of the excess travel time distribution matches that of a near-real-time predictor. Results based on one year of traffic data are also demonstrated in simulations.