AI-Based Decision Support System for Attenuating Traffic Congestion

The transportation industry and transportation infrastructure are undergoing a profound transformation due to advances in the development of artificial intelligence (AI) algorithms that are not just a concept of the future but a reality. Advanced algorithms, predictive systems, and intelligent automation contribute to optimizing logistics, reducing costs, increasing safety, and reducing traffic congestion. AI is also used to optimize routes by analyzing multiple variables, such as distance, traffic, time constraints, and user preferences, to generate optimal routes between departure and destination points. Route planning systems can be integrated with real-time data on traffic, planned or unforeseen events, and other conditions that may affect the trip. AI algorithms can use this data to adapt routes and estimated arrival times based on changes in traffic or other conditions. The purpose of this article is to develop a model for predicting traffic flows at intersections based on historical and real-time data. The focus is on the genetic algorithm used to optimize a Long Short-Term Memory (LSTM) encoder–decoder. Specifically, the research aims to determine how well the proposed model performs when the data is optimized using the genetic algorithm. The results obtained for the proposed GA-LSTM show an average TTS reduction of −18.7%, a maximum improvement of −27.3%, an RMSE of 0.003587, and an MSE of 0.00348 compared to traditional models used in real time for traffic management. Finally, the performance of GA-LSTM was compared with the results reported in the literature to demonstrate the usefulness of the proposed algorithm.