Personalized Destination Prediction Using Transformers in a Contextless Data Setting


Destination prediction is an important task where the primary goal is to correctly predict a user’s destination given an input movement trajectory. Intelligent machine learning models that learn from observed movement data and can automatically forecast destinations from partial query trajectories are of high interest as they can provide a plethora of benefits to both creators and consumers in various markets. In this work, we present a novel framework for tackling the problem of destination prediction in a contextless data setting where we solely learn from trajectory coordinate information. We propose a Transformer model to predict destinations from partial trajectories and we demonstrate its use on two datasets from different domains, including a simulated indoor dataset and an outdoor taxi trajectory dataset. Our proposed method improves upon the previous state-of-the-art LSTM and BiLSTM deep learning approaches in terms of accuracy and distance from true destinations