Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Authors: LEI BAI, Lina Yao, Can Li, Xianzhi Wang, Can Wang
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | 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. |