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 road network and classic statistical modeling, which also provide better interpretation. In this paper, we detect the spatiotemporal relationships of the extended 2nd ring road network of Xi’an, China using Pearson’s Correlation, Mutual Information and Dynamic Time Warping on the network’s speed time series. The first two give an indication of the spatial dependency between road sections by comparing their speeds’ contributions, while Dynamic Time Warping takes also into account the temporal evolution of the phenomenon. Results show that, although the first approach leads to an accurate Bayesian Network prediction model, the second one leads to an improved accuracy using the same modeling structure.