most citedAttention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting

3 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023

Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction

Haiyang Liu, Chunjiang Zhu, Detian Zhang +1

Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems. The complex and dynamic spatial-temporal dependencies make the traffic flow pred…

cs.LG2023★ 3 cited

STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction

Xunlian Luo, Chunjiang Zhu, Detian Zhang +1

Traffic prediction has been an active research topic in the domain of spatial-temporal data mining. Accurate real-time traffic prediction is essential to improve the safety, stabil…

cs.DB2023

Fairness-driven Skilled Task Assignment with Extra Budget in Spatial Crowdsourcing

Yunjun Zhou, Shuhan Wan, Detian Zhang +1

With the prevalence of mobile devices and ubiquitous wireless networks, spatial crowdsourcing has attracted much attention from both academic and industry communities. On spatial c…

cs.LG2023★ 3 cited

Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting

Haiyang Liu, Chunjiang Zhu, Detian Zhang +1

Traffic forecasting is one of the most fundamental problems in transportation science and artificial intelligence. The key challenge is to effectively model complex spatial-tempora…

cs.LG2023★ 2 cited

Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction

Xunlian Luo, Chunjiang Zhu, Detian Zhang +1

Accurate and real-time traffic state prediction is of great practical importance for urban traffic control and web mapping services. With the support of massive data, deep learning…