Data-Driven Distance Metrics for Kriging-Short-Term Urban Traffic State Prediction

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 6; pp. 6268 - 6279
• Main Authors: Varga, Balazs, Pereira, Mike, Kulcsar, Balazs, Pariota, Luigi, Peni, Tamas
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Correlation; Deep learning; Detectors; Distance metric; Flow distribution; Hyperspaces; Kernel; Kriging; Measurement; Neural networks; Prediction algorithms; Spatio-temporal prediction; Traffic flow; Traffic flow prediction; Traffic volume
• ISSN: 1524-9050; 1558-0016; 1558-0016
• DOI: 10.1109/TITS.2023.3251022

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.