no code implementations • 22 Apr 2024 • Kang Luo, Yuanshao Zhu, Wei Chen, Kun Wang, Zhengyang Zhou, Sijie Ruan, Yuxuan Liang
Trajectory modeling refers to characterizing human movement behavior, serving as a pivotal step in understanding mobility patterns.
no code implementations • 5 Nov 2023 • Qianxiong Xu, Cheng Long, Ziyue Li, Sijie Ruan, Rui Zhao, Zhishuai Li
To address this issue, we first present a novel Increment training strategy: instead of masking nodes (and reconstructing them), we add virtual nodes into the training graph so as to mitigate the graph gap issue naturally.
no code implementations • 20 Sep 2021 • Tianfu He, Guochun Chen, Chuishi Meng, Huajun He, Zheyi Pan, Yexin Li, Sijie Ruan, Huimin Ren, Ye Yuan, Ruiyuan Li, Junbo Zhang, Jie Bao, Hui He, Yu Zheng
People often refer to a place of interest (POI) by an alias.
1 code implementation • 5 Feb 2020 • Kun Ouyang, Yuxuan Liang, Ye Liu, Zekun Tong, Sijie Ruan, Yu Zheng, David S. Rosenblum
To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors.
1 code implementation • 6 Feb 2019 • Yuxuan Liang, Kun Ouyang, Lin Jing, Sijie Ruan, Ye Liu, Junbo Zhang, David S. Rosenblum, Yu Zheng
In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations.
Ranked #2 on Fine-Grained Urban Flow Inference on TaxiBJ-P4