As the urban population rises, so does the pressure on the city’s transportation system. Most of the existing methods for passenger destination selection focus on processing the historical behaviors and travel trajectories of passengers. However, the existing methods face the generalization issue, the trained model cannot be applied to predict destinations in new metropolitan areas as the destination information is totally unseen and different from the training sets. To deal with the issue faced in IEEE BigData Cup 2022 – Trip Destination Prediction, in this work, we present a hybrid method. The main idea of our method is four-fold. The first is to implement the gravity model to capture human mobility between zones. The second contains two novel features to depict zones, including human traffic flow and feature class ratio. The third is to initialize the destinations in the new metropolitan area using the origin zones of multi-trip individuals. The last is to perform the nearest-neighbor search on both individuals and trips. The final destination prediction is produced by combining the gravity model and the nearest-neighbor search. Performance comparison reported by the competition leaderboard exhibits the superiority of our hybrid method, which also brings us to the fifth place in the competition.