Monthly Archives: October 2022

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New SIGSPATIAL Paper Is Online – “Improving next location prediction”

Category : Uncategorized

We are delighted to announce that our paper “How do you go where? Improving next location prediction by learning travel mode information using transformers” by MIE Lab members Ye Hong, Henry Martin, and Martin Raubal is now online at arXiv and will be presented at ACM SIGSPATIAL, November 1–4, 2022, Seattle, WA, USA conference.

In this work, we propose a transformer decoder-based neural network to predict the next location an individual will visit based on her historical locations, time, and travel modes. In particular, we design an auxiliary task to jointly predict the next travel mode, with the aim of guiding the learning process of the network. We conduct extensive experiments on two real-world GPS tracking datasets and conclude that considering additional aspects of travel behaviour significantly increases the performance of next location prediction. The overall architecture of the proposed model is shown below.