Traffic prediction based on -temporal guided multi graph Sandwich-Transformer(STGMT)
The ability of spatial-temporal traffic prediction is crucial for urban computing, traffic management and future autonomous driving. In this paper, a novel spatial-temporal guided multi graph Sandwich-Transformer model (STGMT) is proposed, which is composed of the encoder, decoder and attention mechanism modules. The three modules are responsible for feature extraction of historical traffic data, autoregressive prediction and capture the features of spatial-temporal dimension respectively. Compared to the original transformer framework, the spatial-temporal embedding layer's output is introduced to guide the attention mechanism through meta learning which considers the heterogeneity of spatial nodes and temporal nodes. The temporal and spatial features are encoded through Time2Vec(T2V) and Node2Vec(N2V), and coupled into spatial-temporal embedding blocks. In addition, the multi graph is adopted to perform multi-head spatial self attention(MSA). Finally, the attention module and the feed forward layer are recombined to form the Sandwich-Transformer.
Before entering this project, you may need to configure the environment based on Tensorflow2.x-gpu.
!pip install node2vec
If you want to run this project, please download the datasets and weight file from the Google. Then put the checkpoints_NYC and checkpoints_pems08 into the project as named 'checkpoints' .After some tossing, you can run data_fac.py to generate data files in pkl format for your training and testing, which may be a long wait. The pkl flie consists of 5 parts->traind data, validation data, test data, multi graph, node2vec results, and inverse_transform scalar
If you just want to inference and not train your own datasets, you can modify any dataset and name it checkpoints, for example checkpoints_pems08->checkpoints
The backbone STGMT
The operations.py,layer.py and framework.py are the most important componets in this project. Moerover, You can come up with some innovative and great ideas and you can also can change the hyperparmetes in the Hyperparameters.py if you like .Before train your own datasets, you can just change the train.py, line 18 you can change your datasets path from Hyperparameters.py, line 52, l1 loss is used.
So you can finally train the model by running the following command:
python train.py
You will get a new file of your own trained weights saved in checkpoints folders.Don't worry about getting an error, even if there are weight files in the folder, they will be overwritten during training. CheckpointManager in the code can guarantee continuous training or future training line 62 to line 68.
If you only want to inferrence on our dataset, it doesn't matter. Take the dataset in New York as an example, PEMS08 performs the same operation The test.py is the kernel, before testing, the operation as follows
change the data path-> line 19
change error path and compare path -> line78, line 79
python test.py
We provide three metrics: MAE, RMSE, and MAPE
In the end, the terminate will show the results of 3,6,9,12 steps errors and average errors of each steps. Three tables will saved into your project gap.csv , pred.csv, and ana.xlsx
The result of the NYC prediction:
More details please see the paper!
At last, thank you very much for the contribution of the co-author in the article, and also thank my girlfriend for giving me the courage to pursue for a Ph.d.
MIT © YanjieWen
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