STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
Authors: Jiahao Ji, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, Hu Zhang4048-4056
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. We conduct extensive experiments on three real-world traffic datasets, and the proposed method achieves significant improvement over state-of-the-art baselines. |
| Researcher Affiliation | Academia | Jiahao Ji, 1 Jingyuan Wang, 1,2* Zhe Jiang, 3 Jiawei Jiang, 4 Hu Zhang 4 1 State Key Laboratory of Software Development Environment, School of Computer Science & Engineering, Beihang University, Beijing, China 2 Peng Cheng Laboratory, Shenzhen, China 3 Department of Computer & Information Science & Engineering, The University of Florida 4 MOE Engineering Research Center of Advanced Computer Application Technology, School of Computer Science & Engineering, Beihang University, Beijing, China |
| Pseudocode | No | The paper includes mathematical equations and network architecture diagrams but does not contain any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | The code is available at https://github.com/Echo-Ji/STDEN |
| Open Datasets | No | We evaluate the performance of our model over the real-world urban traffic dataset collected by the Beijing Municipal Commission of Transport, which contains trajectories of 40,000 taxies in Beijing from April 1st 2015 to July 31st 2015 (totally 4 months). The paper describes how the dataset was collected but does not provide any link, DOI, or formal citation for public access. |
| Dataset Splits | Yes | We split each dataset into the training, validation, and test sets with a ratio of 7:1:2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions software components like 'dopri5' and 'GRU' but does not specify their version numbers or other library dependencies with versions. |
| Experiment Setup | Yes | The settings of STDEN contains the following two parts: (1) Settings of the DE-Net part. We model the dynamics of potential energy in latent space using an adaptive method dopri5 (Dormand and Prince 1980), and conduct grid search on the latent dimension over {1, 2, 4, 8}. (2) Settings of the encoder. GRU is used to encode the distribution of the initial value of the PEF-sequence. The number of hidden units in GRU is searched over {16, 32, 64, 128}. More implementation details about our STDEN and other baselines settings are given in Appendix. |