Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction

This is a PyTorch implementation of Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction (STEVE) for traffic flow prediction as described in our paper: Jiahao Ji*, Wentao Zhang*, Jingyuan Wang, Chao Huang, Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction.

Requirement

We build this project by Python 3.8 with the following packages:

numpy==1.21.2
pandas==1.3.5
PyYAML==6.0
torch==1.10.1

Datasets

The datasets range from {NYCBike1, NYCBike2, NYCTaxi, BJTaxi}. You can download them from Beihang Cloud Drive.

Each dataset is composed of 4 files, namely train.npz, val.npz, test.npz, and adj_mx.npz.

|----NYCBike1\
|    |----train.npz    # training data
|    |----adj_mx.npz   # predefined graph structure
|    |----test.npz     # test data
|    |----val.npz      # validation data

The train/val/test data is composed of 4 numpy.ndarray objects:

• X: input data. It is a 4D tensor of shape (#samples, #lookback_window, #nodes, #flow_types), where # denotes the number sign.
• Y: data to be predicted. It is a 4D tensor of shape (#samples, #predict_horizon, #nodes, #flow_types). Note that X and Y are paired in the sample dimension. For instance, (X_i, Y_i) is the i-the data sample with i indexing the sample dimension.
• time_label: a int indicating the time index of latest input data. For example, 9:00 in the morning of workday will be present as 9, while 33 for the weekenday.
• c: a intergrate indicating the load level of latest input data.

For all datasets, previous 2-hour flows as well as previous 3-day flows around the predicted time are used to forecast flows for the next time step. adj_mx.npz is the graph adjacency matrix that indicates the spatial relation of every two regions/nodes in the studied area.

⚠️ Note that all datasets are processed as a sliding window view. Raw data of NYCBike1 and BJTaxi are collected from STResNet. Raw data of NYCBike2 and NYCTaxi are collected from STDN. If needed, one can download the original datasets from this link.

run code with:

python STEVE/run.py --config_filename configs/NYCBike1.yaml #  configs/NYCBike1.yaml gives the config of pipeline

Note that this repo only contains the NYCBike1 data because including all datasets can make this repo heavy.