Adaptive Spatiotemporal Transformer Graph Network for Traffic Flow Forecasting by IoT Loop Detectors-Reference-Cited by-同舟云学术

Adaptive Spatiotemporal Transformer Graph Network for Traffic Flow Forecasting by IoT Loop Detectors

Author:

Huang Boyu¹^ORCID,Dou Haowen¹,Luo Yu²^ORCID,Li Junchao³,Wang Jiaqi⁴,Zhou Teng¹^ORCID

Affiliation:

1. Department of Computer Science, Shantou University, Shantou, China

2. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China

3. Mechanical Engineering College, Xi’an Shiyou University, Xi’an, China

4. School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University, Guangzhou, China

Funder

National Natural Science Foundation of China

2022 Guangdong Basic and Applied Basic Research Foundation

2020 Li Ka Shing Foundation Cross-Disciplinary Research

Open Fund of Guangdong Provincial Key Laboratory of Infectious Diseases and Molecular Immunopathology

STU Incubation Project for the Research of Digital Humanities and New Liberal Arts

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

Reference44 articles.

1. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting;yu;arXiv 1709 04875,2017

5. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting;li;arXiv 1707 01926,2017

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