Gridlock Prediction using a Gated Recurrent Neural Network for Smart Transport Systems

Traffic forecasting is a crucial problem in urban transportation, affecting various aspects of daily life such as mobility, traffic management, and road safety. In recent times, the use of machine learning models, particularly Recurrent Neural Networks (RNNs), has gained increasing popularity for solving this problem. Among RNNs, the Gated Recurrent Unit (GRU) has been widely used due to its good performance and computational efficiency. This paper will investigate the application of GRUs to traffic forecasting. The model is trained on historical traffic data, and its predictions are compared to the actual traffic flow to evaluate its performance. Experimental results demonstrate that GRUs perform well in traffic forecasting, with accuracy comparable to other RNN models, while having a much lower computational cost. The outcomes emphasize the value of good feature engineering and the use of domain-specific knowledge to further enhance the functionality of GRU-based models. In conclusion, this paper provides evidence that GRUs are a promising tool for traffic forecasting and can provide valuable insights into future traffic patterns. The results of this paper have practical implications for urban planners, transportation managers, and policy makers in optimizing the use of transportation resources and improving traffic flow in urban areas.