Multi-view Spatiotemporal Traffic Prediction Using Evolutionary-Optimized RNN-GCN Networks

Recent advancements in traffic forecasting leverage spatiotemporal features through recurrent and graph neural networks, showing promising results. However, these approaches typically require extensive manual hyperparameter tuning, rely on a single data view, and often overlook the dynamic nature of traffic patterns, which change at every timestamp. Analyzing spatial interactions among traffic nodes is critical for improving accuracy, yet adaptive graph convolutional networks that respond to these dynamic interactions remain underutilized. Recent advances in bio-inspired algorithms, such as particle swarm optimization (PSO), have demonstrated their power in optimizing and automating model generation and parameter tuning processes. These algorithms can systematically explore a wide range of model configurations to identify optimal strategies, thereby improving learning and data view identification. Specifically, bio-inspired algorithms enhance the adaptive capabilities of traffic forecasting models by dynamically adjusting to changing traffic patterns and sensor data. To address these challenges, this study presents two novel bio-inspired traffic forecasting models designed to improve forecasting accuracy and facilitate more efficient traffic management. The proposed framework presents two primary model variants: (1) PSO-optimized Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models for temporal forecasting, and (2) an integrated PSO-optimized GCN-LSTM-BiLSTM model that incorporates both spatiotemporal features and historical data for comprehensive prediction. This framework highlights the flexibility of our evolutionary optimization methodology while addressing the limitation of single-view analysis, which fails to capture the evolving nature of traffic patterns, leading to suboptimal predictions. The present approach automates hyperparameter selection and dynamically weights temporal, bidirectional, and spatial views, enabling robust adaptation to real-time traffic fluctuations. Extensive experiments on PeMSD07 and PeMSD08 datasets demonstrate the framework’s effectiveness: the optimized LSTM and Bi-LSTM models achieved accuracies of 99.21% and 99.15%, respectively, while the integrated spatiotemporal model attained a mean average error of 0.1948, outperforming existing baselines. The results confirm that PSO-driven automation and adaptive multi-view analysis significantly improve prediction accuracy and robustness, offering a scalable solution for intelligent traffic management systems.