A Speed-Masked Transformer Model for Traffic Speed Prediction

A correct prediction of traffic speed can reasonably plan resources in traffic area, avoid congestion and reduce probability of accidents. In this paper, we propose a Speed-Masked Transformer (SMT) model with an improved decoder, which uses Speed-Masked module to mask the traffic speed data. Decoder structure is simplified and speed of historical moments is weighted by an exponential attenuation function. To verify the advantages of SMT model, datasets collected from interstate freeway in California are used to train and validate the prediction model. The SMT model is compared with several other traffic speed prediction models through simulation. Results show that the SMT model performs best in terms of speed prediction for long prediction horizon.