Advanced Traffic Flow Optimization Using Hybrid Machine Learning and Deep Learning Techniques

Traffic congestion is a significant issue in many cities, especially during peak hours, leading to increased pollution, noise, and stress. To address this, experts are turning to advanced computational techniques such as machine learning and deep learning, which are more effective than traditional methods at processing large volumes of data and adapting to changes over time. Our study proposes using these advanced techniques to predict traffic flow at intersections, which can enhance traffic management by optimizing traffic light timing. We focus solely on predicting the number of vehicles passing through six intersections every five minutes over 56 days. In our research, we undertake two primary tasks: initially training our models using data from four intersections and subsequently incorporating data from all six intersections. In both scenarios, our advanced computational methods, as well as traditional ones, performed exceptionally well, achieving an R2 value of 0.95 and explaining a significant portion of the variance. Our findings indicate that employing these advanced techniques shows great potential for more effective urban traffic management. By accurately forecasting traffic flow, cities can take proactive measures to reduce congestion and enhance the quality of urban life. This approach marks a substantial advancement towards developing smarter and efficient