A Hybrid Deep Learning Model for Traffic Flow Prediction

Urbanization has led to a rise in traffic issues, which not only result in economic losses but also cause to environmental degradation, posing a threat to the quality of life for city residents. Identifying traffic issues and mitigating traffic congestion proactively is of utmost significance. Traffic flow prediction is analyzing historical and current traffic data to predict future traffic conditions. Precise predictions enable people to make suitable choices regarding travel routes and transportation methods. This reduces the duration of trip and enhances the level of satisfaction with traffic conditions. Traditional traffic prediction methods rely on statistical techniques or simple time series analysis. However, these analyses suffer to capture the complex spatial and temporal dependencies in traffic data. Recent advancements in artificial intelligence have enabled to create complex and data-driven prediction models. This paper introduces a novel hybrid model for predicting traffic flow using deep learning techniques. The performance of the proposed model was evaluated against some baseline algorithms on a real-world dataset. Regression metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE), R-Square (R²), and Root Mean Square Error (RMSE) were employed to compare the performances of the models. In the experiments, it has been seen that the proposed hybrid model outperformed other models, with 5.1834 MAE, 54.4060 MSE, 7.3760 RMSE and 0.9923 R2 values. The results emphasize the potential of deep learning methods in the domain of traffic prediction and offer direction for future investigations