[ACM Trans. on Intelligent Systems and Technology (TIST) 2021 Paper] Predicting Citywide Crowd Dynamics at Big Events: A Deep Learning System

• This is an extended journal version of the below conference paper.

[KDD 2019 Paper Applied Data Science Track] DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events

Outline

Under the situations of big events, citywide crowd dynamics would manifest some different patterns from the normal routine. In this study, we propose a highly deployable system to extract the trend from momentary changes, and leverage multitask learning to predict crowd density and crowd flows simultaneously.

Fig 1. Sudden change of human mobility in Tokyo before and after the Great East Japan Earthquake

Fig 2. Crowd dynamics prediction

Fig 3. Multitask ConvLSTM encoder-decoder for simultaneous multi-step prediction

Directories

├── common      # Parameters, data_utils
├── crowdFlowEncoder        # CNN autoencoder for crowd flow
├── meshdynamic     # meshgrid use
├── predCrowdDensity        # baselines for predicting crowd density
│   ├── Historical Average
│   ├── Copy Last Frame
│   ├── ARIMA
│   ├── VAR
│   ├── ST-ResNet
│   ├── CNN / CNN enc-dec.
│   └── ConvLSTM / ConvLSTM enc-dec.
├── predCrowdEncodedFlow        # baselines for predicting encoded crowd flows
│   ├── Historical Average
│   ├── Copy Last Frame
│   ├── ST-ResNet
│   ├── CNN / CNN enc-dec.
│   └── ConvLSTM / ConvLSTM enc-dec.
└── predMultitask(Ours)
    ├── Multitask CNN enc-dec.
    └── Multitask ConvLSTM enc-dec.     # our proposed model

Dependencies

Here are the working environment and major dependencies we run the code:

• Ubuntu 16.04.6 LTS
• Python 3 (>= 3.5; Anaconda Distribution)
• SciPy (>= 0.17.0)
• statsmodels (>= 0.8.0)
• NumPy (>= 1.11.0)
• pandas (>= 0.18.0)
• TensorFlow/tensorflow-gpu (>= 1.3.0; <= 1.14.0)
• Keras (>= 2.0.8)

Citation

If you find anything in this repository useful to your research, please cite our paper :) We sincerely appreciate it. DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events

@inproceedings{jiang2019deepurbanevent,
  title={DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events},
  author={Jiang, Renhe and Song, Xuan and Huang, Dou and Song, Xiaoya and Xia, Tianqi and Cai, Zekun and Wang, Zhaonan and Kim, Kyoung-Sook and Shibasaki, Ryosuke},
  booktitle={Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  pages={2114--2122},
  year={2019},
  organization={ACM}
}