Gated Residual Recurrent Graph Neural Networks for Traffic Prediction
Authors: Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng485-492
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two traffic datasets have demonstrated that the proposed MRes-RGNN outperforms state-of-the-art methods significantly. |
| Researcher Affiliation | Collaboration | Cen Chen, Kenli Li, * Sin G. Teo, Xiaofeng Zou, Kang Wang Jie Wang, Zeng Zeng * College of Information Science and Engineering, Hunan University, China Institute for Infocomm Research, Singapore |
| Pseudocode | No | The paper provides equations and architectural diagrams but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about the release of open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Two large real-world datasets, i.e., METR-LA and PEMS-BAY datasets, are used in the experiment: METR-LA: Traffic data are collected from observation sensors in the highway of Los Angeles County. We use 207 sensors and 4 months of data dated from 1st Mar 2012 until 30th Jun 2012 in the experiment. PEMS-BAY: Traffic data are collected by California Transportation Agencies Performance Measurement System (Pe MS). We use 325 sensors in the Bay Area and 6 months of data dated from 1st Jan 2017 until 31th May 2017 in the experiment. |
| Dataset Splits | Yes | In each dataset, 70%, 20% and 10% of its dataset are split into training, validation and testing datasets, respectively. |
| Hardware Specification | Yes | We configure a Linux server and the other configurations to run the experiment as follows: 8 Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHZ; 256GB RAM; 4 NVIDIA P100 GPUs. |
| Software Dependencies | No | The paper mentions 'Linux server' but does not provide specific software dependencies with version numbers (e.g., Python, TensorFlow/PyTorch versions). |
| Experiment Setup | Yes | In our implementation, two Res-RGNN layers are utilized, and the graph convolution kernel size is set to 64. To have a fair comparison, these parameters are set same in DCRNN. The proposed framework utilizes one Res-RGNN branch for near time, and one hop1 Res-RGNN branch for daily period. In the above Res-RGNN branch, 6 observed data points are used to forecast traffic conditions. Another branch, the hop Res-RGNN for daily period, 4 historical data points are utilized in the experiments. |