Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems

Over the past decade, major cities have faced significant traffic congestion, accidents, and pollution due to increased vehicle usage, urbanization, and migration. An Intelligent Transportation System (ITS) can enhance transportation planning and alleviate congestion. ITS utilizes traffic prediction models to help prevent traffic bottlenecks, improve mobility and safety, and reduce environmental impacts. However, developing these models involves several challenges, including understanding spatiotemporal nonlinearities, making accurate predictions, minimizing prediction time, and reducing model complexity. Many existing approaches integrate Convolutional Neural Networks (CNNs) and variants of Recurrent Neural Networks (RNNs) to analyze spatially correlated traffic data over time. Nevertheless, these hybrid models often require significant storage space, contain numerous learnable parameters, and involve extensive training, validation, and testing times. To address these challenges, we propose a novel methodology that combines a genetic algorithm (GA) with Random Forest Cross-Validation (RF-CV) to evaluate input features and select the most relevant subset. Additionally, we developed a Multi-Objective Genetic Algorithm (MOGA)-enhanced RNN model to optimize hyperparameters and achieve accurate traffic speed predictions. Our proposed methodology balances the trade-offs between prediction accuracy, model size, and computational efficiency by identifying an optimal set of relevant features and hyperparameters. We evaluated our model using the Performance Measurement System (PeMS)-10 dataset and compared its performance against baseline and advanced models from existing literature. Our model achieved a Mean Absolute Error (MAE) of 0.028993, an $R^{2}$ score of 0.999490, and training, validation, and testing times of 81.64 seconds, 0.15 seconds, and 0.18 seconds, respectively. Additionally, the model size was 203,118 bytes, with 14,617 parameters. A comprehensive comparative study demonstrates that our approach outperforms state-of-the-art models in both prediction accuracy and computational efficiency.