Liangzhe Han — accepted AI-conference papers

2023

Generic and Dynamic Graph Representation Learning for Crowd Flow Modeling

Liangzhe Han, Ruixing Zhang, Leilei Sun, Bowen Du, Yanjie Fu, Tongyu Zhu

AAAI 2023technical

Many deep spatio-temporal learning methods have been proposed for crowd flow modeling in recent years. However, most of them focus on designing a spatial and temporal convolution mechanism to aggregate information from nearby nodes and historical observations for a pre-defined prediction task. Diffe…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2022

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

Ruixing Zhang, Liangzhe Han, Boyi Liu, Jiayuan Zeng, Leilei Sun

IJCAI 2022poster

Recent years have witnessed a rapid growth of applying deep spatiotemporal methods in traffic forecasting. However, the prediction of origin-destination (OD) demands is still a challenging problem since the number of OD pairs is usually quadratic to the number of stations. In this case, most of the…

Cited by 19 Source ↗PDF ↗code ↗Scholar ↗