Impact of Augmenting GRU Networks with Iterative and Direct Strategies for Traffic Speed Forecasting

In this paper, we report experimental results from augmenting Recurrent Neural Networks (RNN) with multi-step-ahead strategies for traffic speed prediction. For multi-step-ahead time series forecasting, researchers have applied MIMO, recursive, and direct strategies to machine learning methods in other domains. We applied the recursive and direct strategies to the GRU networks for predicting multi-step-ahead traffic speed and compared the prediction errors with the GRU network without the strategies (i.e. MIMO strategy). Based on the results from the experiments, we found that the direct strategy and the MIMO strategy produce models with smaller error metrics as compared to the recursive strategy. The direct strategy is computationally very expensive, thus MIMO strategy i.e. the GRU architecture without any strategy, is our preferred recommendation.