Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing

1 code implementation • 17 Nov 2023 • Ruixing Zhang, Liangzhe Han, Leilei Sun, Yunqi Liu, Jibin Wang, Weifeng Lv

To tap into the rich information within population movement, based on the perspective that Regions Are Who walk them, we propose a large spatiotemporal model based on trajectories (RAW).

Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction

1 code implementation • 30 Jun 2022 • Liangzhe Han, Xiaojian Ma, Leilei Sun, Bowen Du, Yanjie Fu, Weifeng Lv, Hui Xiong

Traffic demand forecasting by deep neural networks has attracted widespread interest in both academia and industry society.

Graph Representation Learning

Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction

1 code implementation • 29 May 2022 • Ruixing Zhang, Liangzhe Han, Boyi Liu, Jiayuan Zeng, Leilei Sun

Last, an objective function is designed to derive the future OD demands according to the most recent node representations, and also to tackle the data sparsity problem in OD prediction.

Graph Learning Graph Representation Learning