activity
20222026
most citedDynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand Prediction

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2026

SemPOI-RL: Aligning LLM Semantic Reasoning for Interpretable Out-of-Town POI Sequential Generation

Yunqi Liu, Yang Zhang, Ruixing Zhang +4

Large language models (LLMs) exhibit strong semantic reasoning and open-ended generation abilities, but aligning these abilities with structured sequential generation remains chall…

cs.CL2026

Intent Speaks Louder: Controllable User Simulation Beyond Response Imitation

Bo Wang, Ruixing Zhang, Yunqi Liu +4

User simulators are widely used as scalable environments for training and evaluating interactive assistants. Generating the next user turn is inherently one-to-many: the same profi…

cs.CV2026

Think over Trajectories: Leveraging Video Generation to Reconstruct GPS Trajectories from Cellular Signaling

Ruixing Zhang, Hanzhang Jiang, Leilei Sun +3

Mobile devices continuously interact with cellular base stations, generating massive volumes of signaling records that provide broad coverage for understanding human mobility. Howe…

cs.SI20251 cited

JiuTian Chuanliu: A Large Spatiotemporal Model for General-purpose Dynamic Urban Sensing

Liangzhe Han, Leilei Sun, Tongyu Zhu +3

As a window for urban sensing, human mobility contains rich spatiotemporal information that reflects both residents' behavior preferences and the functions of urban areas. The anal…

cs.LG2023

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

Ruixing Zhang, Liangzhe Han, Leilei Sun +3

User profiling and region analysis are two tasks of significant commercial value. However, in practical applications, modeling different features typically involves four main steps…

cs.LG20222 cited

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

Ruixing Zhang, Liangzhe Han, Boyi Liu +2

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 cha…