Abstract is missing.
- Human Mobility Prediction Challenge: Next Location Prediction using Spatiotemporal BERTHaru Terashima, Naoki Tamura, Kazuyuki Shoji, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi. 1-6 [doi]
- Modeling and generating human mobility trajectories using transformer with day encodingAkihiro Kobayashi, Naoto Takeda, Yudai Yamazaki, Daisuke Kamisaka. 7-10 [doi]
- GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT)Aivin V. Solatorio. 11-15 [doi]
- Large-Scale Human Mobility Prediction Based on Periodic Attenuation and Local Feature MatchXiaogang Guo, Guangyue Li, Zhixing Chen, Huazu Zhang, Yulin Ding, Jinghan Wang, Zilong Zhao, Luliang Tang. 16-21 [doi]
- Personalized human mobility prediction for HuMob challengeMasahiro Suzuki, Shomu Furuta, Yusuke Fukazawa. 22-25 [doi]
- Estimating future human trajectories from sparse time series dataRyo Koyama, Meisaku Suzuki, Yusuke Nakamura, Tomohiro Mimura, Shin Ishiguro. 26-31 [doi]
- Multi-perspective Spatiotemporal Context-aware Neural Networks for Human Mobility PredictionChenglong Wang, Zhicheng Deng. 32-36 [doi]
- Cell-Level Trajectory Prediction Using Time-embedded Encoder-Decoder NetworkTaehoon Kim, Kyoung-Sook Kim, Akiyoshi Matono. 37-40 [doi]
- Forecasting Urban Mobility using Sparse Data: A Gradient Boosted Fusion Tree ApproachHaoyu He, Xinhua Wu, Qi Wang. 41-46 [doi]
- Batch and negative sampling design for human mobility graph neural network trainingJiaxin Du, Xinyue Ye. 47-50 [doi]