Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
Authors: Jiahao Ji, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, Yu Zheng
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | 1School of Computer Science & Engineering, Beihang University, China 2School of Economics & Management, Beihang University, China 3Department of Computer Science, Musketeers Foundation Institute of Data Science, University of Hong Kong, China 4JD Intelligent Cities Research, Beijing, China 5JD i City, JD Technology, Beijing, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Model implementation is available at https://github.com/Echo-Ji/ST-SSL. |
| Open Datasets | Yes | We evaluate our model on two types of public real-world traffic datasets summarized in Tab. 1. The first kind is about bike rental records in New York City. NYCBike1 (Zhang, Zheng, and Qi 2017) spans from 04/01/2014 to 09/30/2014, and NYCBike2 (Yao et al. 2019) spans from 07/01/2016 to 08/29/2016. [...] NYCTaxi (Yao et al. 2019) [...] BJTaxi (Zhang, Zheng, and Qi 2017)... |
| Dataset Splits | Yes | We use a sliding window strategy to generate samples, and then split each dataset into the training, validation, and test sets with a ratio of 7:1:2. |
| Hardware Specification | No | The paper states the model is implemented with PyTorch and experiments are conducted on the Lib City platform, but no specific hardware details (e.g., GPU/CPU models, memory) are provided. |
| Software Dependencies | No | The paper mentions implementation with 'PyTorch' and evaluation on the 'Lib City platform', but it does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The embedding dimension D is set as 64. Both the temporal and spatial convolution kernel sizes of ST encoder are set to 3. The perturbation ratios for both traffic-level and topology-level augmentations are set as 0.1. The training phase is performed using the Adam optimizer and the batch size of 32. |