High-performance traffic volume prediction: An evaluation of RNN, GRU, and CNN for accuracy and computational trade-offs

Abstract

Predicting urban traffic volume presents significant challenges due to complex temporal dependencies and fluctuations driven by environmental and situational factors. This study addresses these challenges by evaluating the effectiveness of three deep learning architectures—Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—in forecasting hourly traffic volume on Interstate 94. Using a standardized dataset, each model was assessed on predictive accuracy, computational efficiency, and suitability for real-time applications, with Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), R2 coefficient, and computation time as performance metrics. The GRU model demonstrated the highest accuracy, achieving a MAPE of 2.105%, RMSE of 0.198, and R2 of 0.469, but incurred the longest computation time of 7917 seconds. Conversely, CNN achieved the fastest computation time at 853 seconds, with moderate accuracy (MAPE of 2.492%, RMSE of 0.214, R2 of 0.384), indicating its suitability for real-time deployment. The RNN model exhibited intermediate performance, with a MAPE of 2.654% and RMSE of 0.215, reflecting its limitations in capturing long-term dependencies. These findings highlight crucial tradeoffs between accuracy and efficiency, underscoring the need for model selection aligned with specific application requirements. Future work will explore hybrid architectures and optimization strategies to enhance further predictive accuracy and computational feasibility for urban traffic management.