IODRNN - Incremental output decomposition for a valid traffic flow prediction with GNSS data

Abstract

Traffic flow prediction, a crucial application of intelligent transportation systems (ITS), has become an increasingly prevalent research topic. However, existing models that achieved high prediction accuracy on selected metrics may suffer from time delay in prediction curves which has been rarely explored. These models may produce seemingly accurate but invalid predictions by merely tracking and replicating previous true values. To address this anomaly, we propose a highly interpretable prediction mechanism, the Incremental Output Decomposition Recurrent Neural Network (IODRNN). We also introduce a new metric called Shift Divergence Difference (SDD) to assess the degree of latency of the overall sequence and evaluate the effectiveness of IODRNN in reducing the delay phenomenon. Our experimental results using real-world GNSS traffic data show that IODRNN has the smallest degree of latency and improves MAE and RMSE by 16.8% and 17.4% on average, respectively, over most contrast models. Our study presents an effective approach to evaluate prediction latency, ensuring validity and robustness in traffic prediction.