Taxi-Passenger’s Destination Prediction via GPS Embedding and Attention-Based BiLSTM Model

The prediction of taxi-passenger’s destination with the partial GPS trajectory left by moving taxis is an important yet challenging research issue. The high uncertainty of human mobility and limited clue provided by the unfinished trajectory are two major barriers to developing effective predictors. In general, such a prediction task is often converted to the identification one among given candidate destinations. Hence, how to extract the discriminative knowledge from the partial trajectory becomes crucial. It is well-recognized that the sequence of visited locations by the taxi has inherent relationship with the heading destination. Inspired by the idea, we propose a novel approach that jointly combines the GPS embedding and attention-based BiLSTM techniques for the prediction of passenger’s destination. Specifically, we propose two GPS embedding methods to encode the geographic proximity and multi-scale spatiality of GPS points into embedding vectors, so as to reveal the spatial context of visited locations in the urban space. After converting GPS trajectories into embedding sequences, we further establish an attention-based dual BiLSTMs neural network to model the relationship between the heading destination and the bidirectional sequential context of visited locations. Meanwhile, the discriminative capability of visited locations in determining the destination can be captured by the attention mechanism. In addition, the OT (origin and time) information is aggregated into the neural network as auxiliary features. Stepping closer to smarter passenger services, rather than telling destinations in terms of drop-off clusters, our proposed model outputs the destinations in terms of historical passengers’ destination clusters. Finally, we evaluate the system performance based on two real large-scale datasets. Results show the superior performance of our proposed model.