To meet the increasingly refined individual-level traffic management and travel service needs in the new era, a vehicle travel destination prediction method that comprehensively considers temporal and spatial correlation was proposed based on the traditional prediction method based on historical trajectories. Using data from video AI recognition and vehicle satellite positioning, the vehicle stopping points were identified to segment the vehicle's full-day travel trajectories and establish a historical vehicle travel trajectory database. By studying the temporal and spatial characteristics of vehicle travel, a calculation method for the temporal and spatial correlation of vehicle travel trajectories was proposed, and a vehicle travel destination prediction model was constructed using temporal and spatial correlation as weights. Taking the vehicle travel in Futian Central District of Shenzhen as an example, four typical vehicle travel trajectories including private cars and taxis were selected to establish a model prediction accuracy evaluation function. The prediction accuracy of travel destinations for different types of travel and different degrees of trajectory completion was analyzed and compared with the historical trajectory-based prediction method. The results show that the prediction accuracy of travel destinations for different types of vehicles is basically positively correlated with the degree of trajectory completion. When the trajectory completion rate reaches 80%, the accuracy of travel prediction basically reaches over 80%. Compared with the traditional prediction method based on historical trajectories, the prediction method considering temporal and spatial correlation has higher prediction accuracy, especially for taxis services with no fixed commuting travel characteristics. The prediction accuracy of travel destinations has been improved by more than 16%. The research results can better meet the needs of global traffic management.