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.


Spatio-temporal prediction

Traffic flow prediction

Distance metric


Balázs Varga

Chalmers, Elektroteknik, System- och reglerteknik

Mike Pereira

Chalmers, Elektroteknik, System- och reglerteknik

Balázs Adam Kulcsár

Chalmers, Elektroteknik, System- och reglerteknik

Luigi Pariota

Tamas Peni

IEEE Transactions on Intelligent Transportation Systems

1524-9050 (ISSN)

STOchastic Traffic NEtworks (STONE)

Chalmers AI-forskningscentrum (CHAIR), -- .

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





Transportteknik och logistik

Sannolikhetsteori och statistik


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