Do you have the datasets for this project? about stgan HOT 6 CLOSED

The NYC dataset is provided by https://www.microsoft.com/en-us/research/publication/detecting-collective-anomalies-from-multiple-spatio-temporal-datasets-across-different-domains/. And the BAY dataset is collected from https://pems.dot.ca.gov/. I will update the BAY dataset used in our work to GitHub as soon as possible.
If you just need data for traffic flow prediction, you can refer to the following works.

1. https://github.com/guoshnBJTU/ASTGNN
2. https://github.com/p0llx/DeepST-ResNet/tree/master/data/TaxiBJ
3. https://github.com/liyaguang/DCRNN

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I have another question. On your work( load_data.py ), you use the
graph_path = opt['data_path'] + '/node_subgraph.npy' # (num_node, n, n), the subgraph of each node adj_path = opt['data_path'] + '/node_adjacent.txt' # (num_node, n), the adjacent of each node
how can I translate the datasets (node_subgraph.npy、node_adjacent.txt) or download the datasets?
(ps. I have downloaded the datasets that you listed 11 days ago, but I haven't seen the datasets that I mention above. I am really sorry to have incommoded you. T_T) @dleyan

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I have uploaded the two datasets to Github. The node_subgraph.npy and node_adjacent.txt for NYC dataset are translated according to the data_description.pdf and NYC_862.txt provided by (https://www.microsoft.com/en-us/research/publication/detecting-collective-anomalies-from-multiple-spatio-temporal-datasets-across-different-domains/). And the two files for PeMS dataset are translated according to the locations of sensors.

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thx a lot, bro. I could close the issue finally~ lol @dleyan

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oops, I find a data file that you did not upload it maybe. Just in load_data.py, I have run the code.
dists = torch.tensor(np.loadtxt(self.opt['data_path'] + '/node_dist.txt'), dtype=torch.float) ( in function weight )
I can't find ' node_dist.txt ' that describes two datasets are available at Google Drive in your README.md. I'm really sorry to bother you again.

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I have updated these two datasets.

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