Figure 6 - uploaded by Panagiotis Fafoutellis
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depicts the 20 most related road sections (red) to the selected section, in terms of Pearson's Correlation (left) and Mutual Information (right). It seems that the two approaches capture different spatial patterns on the same dataset. The impacts of these differences should be further investigated in terms of prediction accuracy.
Source publication
Short-term traffic forecasting is a field of research that has always attracted significant attention. The recent introduction of Machine Learning techniques in traffic forecasting has broadened the researchers’ horizons, making fresher approaches possible. However, researchers should not disregard the importance of spatiotemporal relations of a ro...
