MADGCN: Multi Attention Dynamic Graph Convolution Network with A Cost Sensitive Learning for Fine-Grained TRaffic Accident Prediction

Requirements

• scipy>=0.19.0
• numpy>=1.12.1
• pandas>=0.19.2
• pyaml
• statsmodels
• tensorflow>=1.3.0
• pytorch>=2.4

Dependency can be installed using the following command:

pip install -r requirements.txt

Data Preparation

The traffic data files for Bay Area (PEMS-BAY), i.e.,pems-bay.zip, are available at Baidu Yun and the password is 'hfkn', and should be put into the data/ folder. The pems-bay.npz files store the traffic data.While the accident.npy files store the accident data.

Metric for different horizons and datasets

The following table summarizes the performance of MADGCN on two dataset with regards to different metrics.

Dataset	Metric	15min	30min	60min
NYC	P	82.98	79.45	75.65
	R	90.53	86.34	81.87
	F1	86.59	82.75	78.64
	AUC	84.36	81.45	77.64
PEMS-BAY	P	75.63	72.83	71.82
	R	81.24	77.25	73.68
	F1	78.33	74.97	72.74
	AUC	75.97	71.75	67.13