Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems

Traffic flow prediction plays a crucial role in Intelligent Transportation Systems (ITS), as it substantially enhances traffic management efficiency, alleviates congestion, and improves road safety. Traditional models often face challenges in addressing the dynamic complexity of modern highway traffic, whereas multivariate machine learning models demonstrate superior predictive accuracy by leveraging diverse data sources. To address these limitations, this study proposes a traffic flow prediction framework based on sensor networks and multivariate machine learning techniques. Real-time vehicle data are collected using cameras deployed along highways, and key traffic parameters such as flow, density, and speed are precisely extracted using the YOLOv8 object detection model. Subsequently, five machine learning algorithms and three deep learning algorithms are employed to predict traffic flow. Building on this, we propose a hybrid model combining Gradient Boosting Regression (GBR) and Support Vector Regression (SVR), where the GBR component captures complex nonlinear patterns in traffic flow data, while the SVR component enhances the model’s generalization ability by optimizing predictions. Experimental results demonstrate that the proposed GBR-SVR model outperforms comparative methods in short-term traffic flow prediction, particularly under dynamic and complex traffic conditions. In conclusion, by integrating real-time data collection from cameras with the predictive advantages of machine learning models, this algorithm offers a more efficient and sustainable solution for urban traffic management.