Data-driven distance metrics for kriging - Short-term urban traffic state prediction
Journal article, 2023

Estimating traffic flow states at unmeasured urban locations provides a cost-efficient solution for many ITS applications. In this work, a geostatistical framework, kriging is extended in such a way that it can both estimate and predict traffic volume and speed at various unobserved locations, in real-time. In the paper, different distance metrics for kriging are evaluated. Then, a new, data-driven one is formulated, capturing the similarity of measurement sites. Then, with multidimensional scaling the distances are transformed into a hyperspace, where the kriging algorithm can be used. As a next step, temporal dependency is injected into the estimator via extending the hyperspace with an extra dimension, enabling for short horizon traffic flow prediction. Additionally, a temporal correction is proposed to compensate for minor changes in traffic flow patterns. Numerical results suggest that the spatio-temporal prediction can make more accurate predictions compared to other distance metric-based kriging algorithms. Additionally, compared to deep learning, the results are on par while the algorithm is more resilient against traffic pattern changes.

Distance metric

Spatio-temporal prediction

Traffic flow prediction

Kriging

Author

Balázs Varga

Budapest University of Technology and Economics

Mike Pereira

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Université Paris PSL

Balázs Adam Kulcsár

Chalmers, Electrical Engineering, Systems and control

Luigi Pariota

University of Naples Federico II

Tamas Peni

Institute for Computer Science and Control

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN) 1558-0016 (eISSN)

Vol. 24 6 6268-6279

STOchastic Traffic NEtworks (STONE)

Chalmers AI Research Centre (CHAIR), -- .

Chalmers, 2020-02-01 -- 2022-01-31.

Areas of Advance

Transport

Subject Categories

Mathematics

Transport Systems and Logistics

Infrastructure Engineering

Probability Theory and Statistics

Control Engineering

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/TITS.2023.3251022

More information

Latest update

8/4/2023 8