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.
STRelay: A Universal Spatio-Temporal Relaying Framework for Location Prediction over Human Trajectory Data
State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science, University of Macau（物联网智能城市国家重点实验室和计算机与信息科学系，澳门大学）；Beijing Normal University-Hong Kong Baptist University United International College（北京师范大学-香港 Baptist大学联合国际学院）
Exploiting Multi-modal Contextual Sensing for City-bus's Stay Location Characterization: Towards Sub-60 Seconds Accurate Arrival Time Prediction
Institute of Engineering & Management（工程与管理学院）；Indian Institute of Technology Kharagpur（印度理工学院克勒格布尔分校）；BCET Durgapur（杜尔加布尔BCET工程学院）；National Institute of Technology Durgapur（杜尔加布尔国家理工学院）
FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System
