Keras implementation of DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis. Ziqian Lin^, Jie Feng^, Ziyang Lu, Yong Li, and Depeng Jin. AAAI 2019. (^ indicates equal contribution) PDF If our codes is helpful to your research, you can cite our work by:

@article{lin2019deepstn+:,
title={DeepSTN+: Context-aware Spatial Temporal Neural Network for Crowd Flow Prediction in Metropolis},
author={Lin, Ziqian and Feng, Jie and Lu, Ziyang and Li, Yong and Jin, Depeng},
booktitle={Thirty-Thrid AAAI Conference on Artificial Intelligence},
year={2019}
}

Similar to ST-ResNet, our dataset is from the NYC Bike. Besides, we collect 9 types of PoIs for this dataset. The spatial map size of the dataset is 21x12. The dataset is in the folder /DATA/dataBikeNYC flow_data.npy ( TimeLenth x In&OutFlow x MapHeight x MapWidth = 4392 x 2 x 21 x 12 ) and poi_data.npy ( PoICategories x MapHeight x MapWidth = 9 x 21 x 12 ) for directly used.

• python 3.5
• Keras 2.0
• NumPy

File BikeNYC corresponds the Dataset BikeNYC in the Paper DeepSTN+.

• /DATA
  • dataBikeNYC contain dataset flow_data.npy and poi_data.npy
  • lzq_read_data_time_poi.py transfer flow_data.npy and poi_data.npy to the input of the DeepSTN+ network
• /DST_network baseline from ST-ResNet
  • ilayer.py
  • metrics.py
  • STResNet.py
• /DeepSTN_00/SCORE are used to save the results of DeepSTN
• /DeepSTN_10/SCORE are used to save the results of DeepSTN+plus
• /DeepSTN_01/SCORE are used to save the results of DeepSTN+PoI$*$time
• /DeepSTN_11/SCORE are used to save the results of DeepSTN+plus+PoI$*$time
• /DeepSTN_network
  • DeepSTN_net.py model codes for DeepSTN+
  • metrics.py contains the metric RMSE
• /ComparisonBikeNYC.py you can run this file to get the results of ST-ResNet and DeepSTN in the paper.

python ComparisonBikeNYC.py

Refer to ComparisonBikeNYC.py and DeepSTN_net.py

• for training:
  • epoch, batch_size, lr, days_test, iterate_num
  • XDST, X11, X10, X01, X00, trainDST, train11, train10, train01, train00
• model definition: H, W, channel, T, len_closeness, len_period, len_trend, T_closeness, T_period, T_trend, pre_F, conv_F, R_N, drop, is_plus, plus, rate, is_pt, P_N, T_F, PT_F, is_summary, kernel1, isPT_F