output
20202026
most citedDynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns

111 citations

Showing cs.LGShow all

5 papers · 1 filter

cs.LG2024

Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models

Jialiang Wang, Hanmo Liu, Shimin Di +4

High-level automation is increasingly critical in AI, driven by rapid advances in large language models (LLMs) and AI agents. However, LLMs, despite their general reasoning power,…

cs.LG2022★ 11 cited

Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network

Yihong Tang, Junlin He, Zhan Zhao

Human mobility prediction is a fundamental task essential for various applications in urban planning, location-based services and intelligent transportation systems. Existing metho…

cs.LG2021★ 91 cited

Joint Demand Prediction for Multimodal Systems: A Multi-task Multi-relational Spatiotemporal Graph Neural Network Approach

Yuebing Liang, Guan Huang, Zhan Zhao

Dynamic demand prediction is crucial for the efficient operation and management of urban transportation systems. Extensive research has been conducted on single-mode demand predict…

cs.LG2021★ 111 cited

Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns

Yuebing Liang, Zhan Zhao, Lijun Sun

Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent transportation systems. Despite extensive research regarding traffic data imputation…

cs.LG2021★ 70 cited

NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanisms

Yuebing Liang, Zhan Zhao

Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and loca…