How do you go where? Improving next location prediction by learning
travel mode information using transformers
- URL: http://arxiv.org/abs/2210.04095v1
- Date: Sat, 8 Oct 2022 19:36:58 GMT
- Title: How do you go where? Improving next location prediction by learning
travel mode information using transformers
- Authors: Ye Hong, Henry Martin, Martin Raubal
- Abstract summary: We propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes.
In particular, the prediction of the next travel mode is designed as an auxiliary task to help guide the network's learning.
Our experiments show that the proposed method significantly outperforms other state-of-the-art next location prediction methods.
- Score: 6.003006906852134
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Predicting the next visited location of an individual is a key problem in
human mobility analysis, as it is required for the personalization and
optimization of sustainable transport options. Here, we propose a transformer
decoder-based neural network to predict the next location an individual will
visit based on historical locations, time, and travel modes, which are
behaviour dimensions often overlooked in previous work. In particular, the
prediction of the next travel mode is designed as an auxiliary task to help
guide the network's learning. For evaluation, we apply this approach to two
large-scale and long-term GPS tracking datasets involving more than 600
individuals. Our experiments show that the proposed method significantly
outperforms other state-of-the-art next location prediction methods by a large
margin (8.05% and 5.60% relative increase in F1-score for the two datasets,
respectively). We conduct an extensive ablation study that quantifies the
influence of considering temporal features, travel mode information, and the
auxiliary task on the prediction results. Moreover, we experimentally determine
the performance upper bound when including the next mode prediction in our
model. Finally, our analysis indicates that the performance of location
prediction varies significantly with the chosen next travel mode by the
individual. These results show potential for a more systematic consideration of
additional dimensions of travel behaviour in human mobility prediction tasks.
The source code of our model and experiments is available at
https://github.com/mie-lab/location-mode-prediction.
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