Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction-Reference-Cited by-同舟云学术

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Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction

Published:2024-08-24 Issue: Volume:23 Page:5206-5217
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Jiang Wenzhao¹^ORCID,Han Jindong²^ORCID,Liu Hao³^ORCID,Tao Tao⁴^ORCID,Tan Naiqiang⁴^ORCID,Xiong Hui³^ORCID

Affiliation:

1. The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, Guangdong, China

2. The Hong Kong University of Science and Technology, Hong Kong, China

3. The Hong Kong University of Science and Technology (Guangzhou) & The Hong Kong University of Science and Technology, Guangzhou, Guangdong, China

4. Didichuxing Co. Ltd, Beijing, China

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Guangzhou Basic and Applied Basic Research Program

Education Bureau of Guangzhou Municipality

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671507

Reference61 articles.

1. A Review of Traffic Congestion Prediction Using Artificial Intelligence

2. Efficient Large Scale Language Modeling with Mixtures of Experts

3. Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In Advances in Neural Information Processing Systems 33.

4. PCNN: Deep Convolutional Networks for Short-Term Traffic Congestion Prediction

5. Zhengyu Chen, Teng Xiao, and Kun Kuang. 2022. BA-GNN: On Learning Bias-Aware Graph Neural Network. In 2022 IEEE 38th International Conference on Data Engineering. 3012--3024.

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