  Precise destination prediction of taxi trajectories can benefit many
intelligent location based services such as accurate ad for passengers.
Traditional prediction approaches, which treat trajectories as one-dimensional
sequences and process them in single scale, fail to capture the diverse
two-dimensional patterns of trajectories in different spatial scales. In this
paper, we propose T-CONV which models trajectories as two-dimensional images,
and adopts multi-layer convolutional neural networks to combine multi-scale
trajectory patterns to achieve precise prediction. Furthermore, we conduct
gradient analysis to visualize the multi-scale spatial patterns captured by
T-CONV and extract the areas with distinct influence on the ultimate
prediction. Finally, we integrate multiple local enhancement convolutional
fields to explore these important areas deeply for better prediction.
Comprehensive experiments based on real trajectory data show that T-CONV can
achieve higher accuracy than the state-of-the-art methods.
