ST-ResNet (Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction) implementaion with Pytorch, improved preprocessing speed over the original code.
Most of the code in the model definition is based on https://github.com/KL4805/STResNet-PyTorch, which is another ST-ResNet Pytorch implimentaion.
Copy TaxiBJ and/or BikeNYC dataset under datasets dir.
Note TaxiBJ dataset is currently unavailable due to circumstances at the data provider.
See: TaxiBJ21: An open crowd flow dataset based on Beijing taxi GPS trajectories
python run.py [-h] [-s SEED] FILE
positional arguments:
FILE path to config file
options:
-h, --help show this help message and exit
-s SEED, --seed SEED seed for initializing trainingpython run.py examples/TaxiBJ/L4-C3-P1-T1/config.iniThe following is a setting for TaxiBJ dataset and L4-C3-P1-T1, means four residual blocks, three closeness time steps, one period time step and one trend time step.
[dataset]
data_files = ["./datasets/TaxiBJ/BJ13_M32x32_T30_InOut.h5", "./datasets/TaxiBJ/BJ14_M32x32_T30_InOut.h5", "./datasets/TaxiBJ/BJ15_M32x32_T30_InOut.h5", "./datasets/TaxiBJ/BJ16_M32x32_T30_InOut.h5"]
holiday_file = ./datasets/TaxiBJ/BJ_Holiday.txt
meteorol_file = ./datasets/TaxiBJ/BJ_Meteorology.h5
T = 48 ; time steps of the day. T=48 means 24 * 60 / 48 = 30 min = one time step
len_closeness = 3 ; number of time steps used as closeness
len_period = 1 ; number of time steps used as period
len_trend = 1 ; number of time steps used as trend
period_interval = 1 ; 1 specifies 1 day interval: 1 * T = 1 * 48 * 30 min = 24 hr = 1 day
trend_interval = 7 ; 7 specifies 1 week interval: 7 * T = 7 * 48 * 30 min = 7 days = 1 wk
len_test = 1344 ; number of test data
use_meta = true ; whether to use day of the week and weekend information
use_holiday = true ; use holiday information
use_meteorol = true ; use weather information
[model]
nb_flow = 2 ; number of channels: 2 means the number of in/out-flows
map_height = 32 ; grid height
map_width = 32 ; grid width
nb_residual_unit = 4 ; number of residual blocks
[learning]
epochs = 100
batch_size = 32
learning_rate = 0.0002J. Zhang, Y. Zheng and D. Qi, "Deep spatio-temporal residual networks for citywide crowd flows prediction", AAAI, pp. 1655-1661, 2017.
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