Multi-Scale and Multi-Scope Convolutional Neural Networks for Destination Prediction of Trajectories

Precise destination prediction from partial trajectories have a huge potential impact on intelligent location-based approaches. Traditional prediction approaches, which treat trajectories as one-dimensional sequences and process them in a single scale, fail to capture diverse and rich two-dimensional patterns of trajectories in different spatial scales. Meanwhile, most models treat each portion of a trajectory equally in terms of contributing to final destination prediction. This is in conflict with our observation that there exists some albeit small local areas playing much more important roles for destination prediction than the others. To address these problems, we propose a novel prediction algorithm T-CONV, which models trajectories as two-dimensional images, and then feed them into a convolutional neural network (CNN) architecture to extract multi-scale patterns for precise destination prediction. Furthermore, we propose a method to extract regions with different relevance for final output of T-CONV, and further explore the local patterns of important regions by integrating multi-scope local-enhancement areas based on attention mechanism. The comprehensive experiments based on two large-scale real taxi trajectory datasets show that T-CONV can achieve higher accuracy than the state-of-the-art methods, demonstrating the strength of the multi-scale and multi-scope feature extraction mechanisms in trajectory mining.