Trip destination prediction based on a deep integration network by fusing multiple features from taxi trajectories

Trip destination prediction plays an important role in exploring urban travel patterns. Accurate prediction can improve the efficiency of traffic management and the quality of location-based services. Here, a deep learning structure that contains three components: travel information extraction, classification learning mechanism, and output module is proposed. Three types of information (the partial trajectory of on-going trips, historical trajectories, and related external information) are extracted in the first component. Then, the classification learning mechanism chooses different methods (i.e. Long Short-Term Memory network and Embedding technology) according to the characteristic of variables. Finally, an output layer that integrates the prior information about destinations is constructed. Two open-source trajectory datasets are used to validate the effectiveness of the proposed model. Results show that the proposed model outperforms benchmark models using only part of the information or using all of the information but ignore the classification learning mechanism. The performance of the proposed model under different call types and travel durations is further explored. The result of this study will help understand travel behaviour in urban cities.