Data-driven distance metrics for kriging - Short-term urban traffic state prediction
Artikel i vetenskaplig tidskrift, 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

Författare

Balázs Varga

Budapesti Muszaki es Gazdasagtudomanyi Egyetem

Mike Pereira

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Université de recherche Paris Sciences et Lettres

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

Luigi Pariota

Universita degli Studi di Napoli Federico II

Tamas Peni

SZAMITASTECHNIKAI ES AUTOMATIZALASI KUTATOINTEZET

IEEE Transactions on Intelligent Transportation Systems

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

Vol. 24 6 6268-6279

STOchastic Traffic NEtworks (STONE)

Chalmers AI-forskningscentrum (CHAIR), -- .

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

Styrkeområden

Transport

Ämneskategorier

Matematik

Transportteknik och logistik

Infrastrukturteknik

Sannolikhetsteori och statistik

Reglerteknik

Datorseende och robotik (autonoma system)

DOI

10.1109/TITS.2023.3251022

Mer information

Senast uppdaterat

2023-08-04