T-CONV: A Convolutional Neural Network For Multi-scale Taxi Trajectory Prediction
This work addresses precise destination prediction for taxi trajectories, benefiting intelligent location-based services like targeted advertising, and is incremental as it builds on existing CNN approaches by incorporating multi-scale analysis.
The paper tackles the problem of predicting taxi destinations by modeling trajectories as two-dimensional images and using multi-scale convolutional neural networks, achieving higher accuracy than state-of-the-art methods in experiments with real trajectory data.
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.