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..
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Authors: Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% 15% over state-of-the-art baselines. We conducted extensive experiments on two large-scale real-world datasets, and the proposed approach obtains significant improvement over state-of-the-art baseline methods. |
| Researcher Affiliation | Academia | University of Southern California, California Institute of Technology EMAIL, EMAIL |
| Pseudocode | No | The paper describes mathematical equations for its models (e.g., DCGRU equations), and provides a system architecture diagram (Figure 2), but does not include any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | 1The source code is available at https://github.com/liyaguang/DCRNN. |
| Open Datasets | Yes | 70% of data is used for training, 20% are used for testing while the remaining 10% for validation. |
| Dataset Splits | Yes | 70% of data is used for training, 20% are used for testing while the remaining 10% for validation. |
| Hardware Specification | No | The paper mentions using TensorFlow and an Adam optimizer, but it does not specify any hardware details such as CPU, GPU models, or memory specifications used for the experiments. |
| Software Dependencies | No | All neural network based approaches are implemented using Tensorflow (Abadi et al., 2016), and trained using the Adam optimizer with learning rate annealing. ARIMAkal: ... model is implemented using the statsmodel python package. VAR: ... model is implemented using the statsmodel python package. (Specific version numbers for TensorFlow or statsmodel are not provided.) |
| Experiment Setup | Yes | The best hyperparameters are chosen using the Tree-structured Parzen Estimator (TPE) (Bergstra et al., 2011) on the validation dataset. Detailed parameter settings for DCRNN as well as baselines are available in Appendix E. (Appendix E provides specific details for FNN, FC-LSTM, and DCRNN including hidden layer units, learning rates, epochs, dropout, weight decay, batch size, loss function, K value, and scheduled sampling parameters). |