Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction-Reference-Cited by-同舟云学术

Adaptive Spatiotemporal Dependence Learning for Multi-Mode Transportation Demand Prediction

Author:

Xu Haihui¹,Zou Tao²^ORCID,Liu Mingzhe²^ORCID,Qiao Yanan²,Wang Jingjing¹,Li Xucheng³

Affiliation:

1. Beijing Municipal Transportation Operations Coordination Center (TOCC), Beijing, China

2. State Key Laboratory of Software Development Environment (SKLSDE), School of Computer Science and Engineering, Beihang University, Beijing, China

3. Shenzhen Urban Transport Planning Center Company Ltd., Shenzhen, China

Funder

National Natural Science Foundation of China

Guangxi Innovation-Driven Development Special Fund Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

Reference39 articles.

3. Survey on the attention based RNN model and its applications in computer vision;wang;arXiv 1601 06823,2016

4. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting;li;Proc Int Conf Learn Represent,2018

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