The spatial-temporal prediction of transit speeds is of great importance today as it allows for the anticipation and mitigation of vehicular congestion, thereby improving traffic efficiency. In machine learning, models such as ConvLSTM or Transformers enable reasonable predictions at the spatio-temporal level. However, these models typically assume a square grid configuration, which can limit the use of more convenient configurations in transportation, such as hexagonal grids. We propose a ConvLSTM neural network adapted to hexagonal grid sequences for transit speed prediction, incorporating a transformation of the hexagonal input to allow the use of standard spatial temporal architectures based on square grids. This work validates the proposed model through experiments comparing our approach with baseline methods using traffic data from freight transportation in the Metropolitan Region of Santiago, Chile. The results indicate that using hexagonal sequences improves the mean absolute error (MAE) in predicting freight traffic speeds by 2.7% compared to the base spatio-temporal ConvLSTM prediction model. For future work, we propose using larger databases and adapted transformers.