Enhancing Traffic Speed Prediction Accuracy: The Multialgorithmic Ensemble Model With Spatiotemporal Feature Engineering

Abstract

Accurate traffic speed prediction is crucial for efficient traffic management and planning in urban areas. Traditional traffic prediction models often fall short due to their inability to capture the complex and dynamic nature of traffic flow. There is a need for more advanced models that can effectively handle dynamic traffic conditions. This study introduces the multialgorithmic ensemble model (MAEM), a novel framework designed to improve traffic speed prediction accuracy by integrating graph neural networks (GNNs), bidirectional gated recurrent units (Bi-GRUs), and long short-term memory (LSTM) networks, to effectively analyze the spatiotemporal characteristics of the traffic network. The methodology involves constructing a virtual graph based on road segment correlations and applying a combination of spatial and temporal feature extraction techniques. The model is further enhanced with an attention mechanism to focus on critical time intervals. The dataset used for this study consists of one-year aggregated probe vehicle traffic data of 4788 road segments in the City of Hamilton, Ontario. The results demonstrate significant performance, achieving the mean absolute percentage error (MAPE) of 3.5% and root-mean-square error (RMSE) of 2.4 km/h, indicating the potential of the proposed framework to significantly enhance traffic speed prediction accuracy and provide a reliable tool for urban traffic management and planning.