Gaussian Process Regression for Tail Vehicle Departure Time Prediction at Signalized Intersections Using UAV...

Extensive research has been conducted on vehicle queuing and dissipation near signalized intersections. However, existing prediction methods for vehicle departure time primarily rely on assumptions of steady-state homogeneous traffic flow, utilizing shockwave theory and vehicle kinematic modeling. These methods encounter challenges in addressing traffic uncertainties during queue formation and dissipation, particularly in scenarios involving multiple lanes. This paper introduces a novel approach by leveraging unmanned aerial vehicle (UAV) trajectory data to construct fleet state features and proposes a prediction method for tail vehicle departure time based on Gaussian process regression. The objective of this method is to optimize the green light crossing time window and eco-driving trajectory for connected vehicles at signalized intersections. The findings reveal that the departure time of the tail vehicle within a specified distance adheres to a Gaussian process, demonstrating the applicability of Gaussian process regression for departure time prediction modeling. The effectiveness of the proposed method was validated using a field-measured dataset collected from three typical multi-lane signalized intersections. Notably, compared to four benchmark models (linear regression, decision trees, multilayer perceptron neural networks, and eXtreme Gradient Boosting-XGBoost), the mean absolute percentage error (MAPE) was reduced by an average of 5.146% on the test set under a random 70/30 split. Additionally, a robustness assessment demonstrates that the proposed model performs well, albeit slightly less effectively than the XGBoost model. We emphasize that the conclusions are drawn for the studied intersections; generalization to unseen intersections requires further validation with cross-site data.