3T-Net: Transformer Encoders for Destination Prediction

The need for accurate and timely destination prediction arises in many transportation applications. We formulate destination prediction as a multivariate time series classification problem, and leverage part of the core components of the Transformer network to build a new deep neural network model exclusively for this task. The key building block of our model consists of Two Towers of Transformer encoders, and we call it “3T-Net.” Through extensive comparison experiments on a simulated indoor trajectories data set, we show that 3T-Net performs better or close to other investigated state-of-the-art deep learning based models. Our model can also be used for outdoor destination prediction scenarios and more general multivariate time series classification problems.