Combining knowledge graph into metro passenger flow prediction: A split-attention relational graph convolutional network

Author:

Zeng Jie,Tang Jinjun

Funder

Central South University

National Natural Science Foundation of China

Ministry of Education of the People's Republic of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference67 articles.

1. Extended four-step travel demand forecasting model for urban planning;Agrawal;Lecture Notes in Networks and Systems,2018

2. Bai, L., Yao, L., Kanhere, S. S., Wang, X., & Sheng, Q. Z. (2019a). StG2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting. IJCAI International Joint Conference on Artificial Intelligence, 2019-Augus, 1981–1987. https://doi.org/10.24963/ijcai.2019/274.

3. Bai, S., Zico Kolter, J., & Koltun, V. (2019b). Trellis networks for sequence modeling. 7th International Conference on Learning Representations, ICLR 2019, 103, 1–18.

4. Subway passenger flow prediction for special events using smart card data;Chen;IEEE Transactions on Intelligent Transportation Systems,2020

5. MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems;Chen;ArXiv,2015

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