1 code implementation • IEEE 39th International Conference on Data Engineering (ICDE) 2023 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, Chenxing Wang
To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied.
Ranked #1 on Traffic Prediction on PeMSD8
no code implementations • 6 Sep 2022 • Xinyue Ma, Chenxing Wang
Single-pixel imaging (SPI) is significant for applications constrained by transmission bandwidth or lighting band, where 3D SPI can be further realized through capturing signals carrying depth.
1 code implementation • 20 Jan 2022 • Chenxing Wang, Fang Zhao, Haichao Zhang, Haiyong Luo, Yanjun Qin, Yuchen Fang
To tackle these challenges, we propose a meta learning based framework, MetaTTE, to continuously provide accurate travel time estimation over time by leveraging well-designed deep neural network model called DED, which consists of Data preprocessing module and Encoder-Decoder network module.
no code implementations • 6 Dec 2021 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng
Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e. g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs.
no code implementations • 6 Dec 2021 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Liang Zeng, Bo Hui, Chenxing Wang
Besides, we propose a novel encoder-decoder architecture to incorporate the cross-time dynamic graph-based GCN for multi-step traffic forecasting.
no code implementations • 4 Dec 2021 • Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, Chenxing Wang
In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously.
no code implementations • 4 Dec 2021 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Chenxing Wang
Traffic prediction has gradually attracted the attention of researchers because of the increase in traffic big data.
no code implementations • 4 Jan 2021 • Fanzhou Wang, Chenxing Wang, Qingze Guan
Multiple works have applied deep learning to fringe projection profilometry (FPP) in recent years.