HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival

This is basic implementation of our KDD’20 Applied Data Science Track (Oral) paper:

Huiting Hong, Yucheng Lin, Xiaoqing Yang, Zang Li, Kung Fu, Zheng Wang, Xiaohu Qie, Jieping Ye. 2020. HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival.

The source code is based on STGCN

HetETA framework
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Dependencies

The script has been tested running under Python 2.7.5, with the following packages installed (along with their dependencies):

• argparse==1.1
• numpy==1.16.5
• scipy==1.2.2
• networkx==2.2
• tensorflow-gpu==1.13.1
• yaml==5.1.2

Overview

Here we provide the implementation of HetETA and a toy dataset.

The folder is organised as follows:

• dataset/ contains:
  • make_sample.py randomly generates the toy_sample dataset to help readers to figure out the input format;
  • toy_sample/ contains:
    • adj_gap_top5.mat is the vehicle-trajectories based network;
    • adj.mat is the multi-relational road network;
    • link_info.npz is the static attributes of each road segment;
    • dynamic_fes.npz is the dynamic feature (speed) of each road segment over time periods;
    • eta_label.npz contains the time it takes for a vehicle to travel through a path starting form period t.
• codes/ contains:
  • data/:
    • model/ is used to save the trained model;
    • config_*.yaml configures the path and paramenter settings.
  • model/ contains the implementation of the HetETA network;
  • utils/ contains some tools for loading dataset;
  • train.py is used to execute a full training run on the dataset.

How to run

cd codes
python -u train.py --config data/config_HetETA_toy.yaml --model_dir data/model/HetETA_toy --dataset_dir ../dataset/toy_sample >> multi-HetETA_toy.log

Please note that the toy_sample dataset is not a real dataset, which is only used to provide examples of data formats, not to train models.

License

Didi Chuxing, Beijing, China.