Happy Valley



If you find our code or dataset useful for your research, please cite our paper:

Sijie Ruan, Jie Bao, Yuxuan Liang, Ruiyuan Li, Tianfu He, Chuishi Meng, Yanhua Li, Yingcai Wu and Yu Zheng. “Dynamic Public Resource Allocation based on Human Mobility Prediction.”, ACM IMWUT/UbiComp 2020.


Python 3.6

  • numpy==1.14.5
  • networkx==2.2
  • shapely==1.6.4
  • pickle


We organize our dataset into two archives, i.e., MALMCS_data.zip and PREDICTION_data.zip

  1. MALMCS_data.zip
  • frames_20180101_20181101_24.npy: this is the hourly crowd flows data in Beijing Happy Valley from 01/01/2018 to 01/11/2018 scraped from the Tencent Heat Map. The last month is used for evaluation, and previous months are used for training & validation.
  • pred_all_stresnet_mf4_masked.pkl: this is the predicted results from the prediction model for evaluation acceleration purpose. In the paper, those results are obtained by training MF-STN.
  1. PREDICTION_data.zip

This archive provides some external factors for crowd flow prediction, which can be used to train the crowd flow prediction model together with frames_20180101_20181101_24.npy. This dataset is also a data source for UrbanFM.

  • holiday features: external/holiday_20180101_20181101_24.npy
  • meteorology features: external/mete_cy_20180101_20181101_24.npy
  • ticket price features: external/price_20180101_20181101_24.npy
  • time of day features: external/tod_20180101_20181101_24.npy


Tunable Parameters

  • Service radius radius
  • Energy limitation cost_limit
  • Number of agents k

 python evaluate.py


The code and data are released under the MIT License.

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