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.
Accurate activity location prediction is a crucial component of many mobility applications and is particularly required to develop personalized, sustainable transportation systems. Despite the widespread adoption of deep learning models, next location prediction models lack a comprehensive discussion and integration of mobility-related spatio-temporal contexts. Here, we utilize a multi-head self-attentional (MHSA) neural network that learns location transition patterns from historical location visits, their visit time and activity duration, as well as their surrounding land use functions, to infer an individual's next location. Specifically, we adopt point-of-interest data and latent Dirichlet allocation for representing locations' land use contexts at multiple spatial scales, generate embedding vectors of the spatio-temporal features, and learn to predict the next location with an MHSA network. Through experiments on two large-scale GNSS tracking datasets, we demonstrate that the proposed model outperforms other state-of-the-art prediction models, and reveal the contribution of various spatio-temporal contexts to the model's performance. Moreover, we find that the model trained on population data achieves higher prediction performance with fewer parameters than individual-level models due to learning from collective movement patterns. We also reveal mobility conducted in the recent past and one week before has the largest influence on the current prediction, showing that learning from a subset of the historical mobility is sufficient to obtain an accurate location prediction result. We believe that the proposed model is vital for context-aware mobility prediction. The gained insights will help to understand location prediction models and promote their implementation for mobility applications.