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ORCIDHuMob-Challengei@SIGSPATIAL 2023: Hamburg, Germany
- Proceedings of the 1st International Workshop on the Human Mobility Prediction Challenge, HuMob-Challenge 2023, Hamburg, Germany, 13 November 2023. ACM 2023
- Haru Terashima
, Naoki Tamura, Kazuyuki Shoji, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi:
Human Mobility Prediction Challenge: Next Location Prediction using Spatiotemporal BERT. 1-6 - Akihiro Kobayashi, Naoto Takeda, Yudai Yamazaki, Daisuke Kamisaka:
Modeling and generating human mobility trajectories using transformer with day encoding. 7-10 - Aivin V. Solatorio:
GeoFormer: Predicting Human Mobility using Generative Pre-trained Transformer (GPT). 11-15 - Xiaogang Guo, Guangyue Li, Zhixing Chen, Huazu Zhang, Yulin Ding, Jinghan Wang, Zilong Zhao, Luliang Tang:
Large-Scale Human Mobility Prediction Based on Periodic Attenuation and Local Feature Match. 16-21 - Masahiro Suzuki, Shomu Furuta, Yusuke Fukazawa:
Personalized human mobility prediction for HuMob challenge. 22-25 - Ryo Koyama, Meisaku Suzuki, Yusuke Nakamura, Tomohiro Mimura, Shin Ishiguro:
Estimating future human trajectories from sparse time series data. 26-31 - Chenglong Wang, Zhicheng Deng:
Multi-perspective Spatiotemporal Context-aware Neural Networks for Human Mobility Prediction. 32-36 - Taehoon Kim, Kyoung-Sook Kim, Akiyoshi Matono:
Cell-Level Trajectory Prediction Using Time-embedded Encoder-Decoder Network. 37-40 - Haoyu He, Xinhua Wu, Qi R. Wang:
Forecasting Urban Mobility using Sparse Data: A Gradient Boosted Fusion Tree Approach. 41-46 - Jiaxin Du, Xinyue Ye:
Batch and negative sampling design for human mobility graph neural network training. 47-50
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