Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Authors: LEI BAI, Lina Yao, Can Li, Xianzhi Wang, Can Wang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on two real-world traffic datasets show AGCRN outperforms state-of-the-art by a significant margin without pre-defined graphs about spatial connections. |
| Researcher Affiliation | Academia | Lei Bai UNSW, Sydney EMAIL Lina Yao UNSW, Sydney EMAIL Can Li UNSW, Sydney EMAIL Xianzhi Wang University of Technology Sydney EMAIL Can Wang Griffith University EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | Code available at: https://github.com/Lei BAI/AGCRN |
| Open Datasets | Yes | To evaluate the performance of our work, we conduct experiments on two public real-world traffic datasets: Pe MSD4 and Pe MSD8 [6, 11]. Pe MS means Caltrans Performance Measure System (Pe MS) [38] |
| Dataset Splits | Yes | We split the datasets into training sets, validation sets, and test sets according to the chronological order. The split ratio is 6:2:2 for both datasets. |
| Hardware Specification | Yes | All the deep-learning-based models, including our AGCRN, are implemented in Python with Pytorch 1.3.1 and executed on a server with one NVIDIA Titan X GPU card. |
| Software Dependencies | Yes | All the deep-learning-based models, including our AGCRN, are implemented in Python with Pytorch 1.3.1 and executed on a server with one NVIDIA Titan X GPU card. |
| Experiment Setup | Yes | We optimize all the models by Adam optimizer for a maximum of 100 epochs and use an early stop strategy with the patience of 15. The best parameters for all deep learning models are chosen through a carefully parameter-tuning process on the validation set. |