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Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction (DSTGCN)

DSTGCN is a graph-based neural network that predicts the risk of traffic accidents in the future.

Please refer to our Neurocomputing 2021 paper “Deep Spatio-Temporal Graph Convolutional Network for Traffic Accident Prediction” for more details.

Project Structure

The descriptions of principal files in this project are introduced as follows:

model/
- spatial_layer.py: the Spatial Convolutional Layer
- spatial_temporal_layer.py: the Spatial-Temporal Convolutional Layer
- fully_connected.py: fully connected network for the Embedding layer
- DSTGCN.py: the Deep Spatio-Temporal Graph Convolutional Network
preprocessing_data/
- process_xxx.py: process the raw 'xxx' dataset
- generate_data.py: generate the dataset for our model
transform_coord/: convert the coordinate, which could be obtained from here.
train/
- train_model.py and train_main.py: train models
test/
- test_main.py: evaluate models
utils/: utility files (e.g. data loader, metrics calculation, loss function, configurations)
original_data/: original datasets. Due to the data privacy, we do not provide the original data. But, you could use the preprocess codes in preprocessing_data/ folder to generate your own datasets and use them to train the model.
data/: processed datasets. We provide a sampled dataset here. You can download it and then put the data files in this folder.
saves/ and runs/: folders to save models and outputs of tensorboardX, respectively
results/: folders to save the evaluation metrics for models.

Format of the processed data:

accident data format: records of traffic accidents.

"longitude"	"latitude"	"startTime"	"endTime"
accident longitude	accident latitude	accident start time	accident end time

poi data format: records of pois.

"longitude"	"latitude"	"poi_type"
poi longitude	poi latitude	poi function type

road data (beijing_roadnet.gpickle) format: networkx.classes.graph.Graph, road network structure that records the connectivity of road segments. You can download the original data from here, and then run preprocessing_data/process_beijing_road_net.py to get the preprocessed file.
speed data (all_grids_speed.h5) format: DataFrame, containing the traffic speed of each grid
weather data format (weather.h5) format: records of the weather condition. You can run preprocessing_data/process_weather.py to get the preprocessed file.

"temp"	"dewPt"	"pressure"	"wspd"	...
temperature	dew point	pressure	wind speed	etc.

edge.h5 data format: records of spatial features (poi and road segment features), which is a combination of preprocessed poi data and road data.

"XCoord"	"YCoord"	"LENGTH"	"NUM_NODE"	"spatial_features"
road segment longitude	road segment latitude	road segment length	points that road segment contains	road segment poi distribution (a list of each poi type numbers)

Parameter Settings

Please refer to our paper for more details of parameter settings. Hyperparameters could be found in utils/config.json and you can adjust them when running the model.

How to use

Training: after setting the parameters, run python train_main.py to train models.
Testing: run python test_main.py to evaluate models based on the path of saved models.

Principal environmental dependencies

Citation

Please consider citing the following paper when using our data or code.

@article{DBLP:journals/ijon/YuDHSHL21,
  author    = {Le Yu and
               Bowen Du and
               Xiao Hu and
               Leilei Sun and
               Liangzhe Han and
               Weifeng Lv},
  title     = {Deep spatio-temporal graph convolutional network for traffic accident
               prediction},
  journal   = {Neurocomputing},
  volume    = {423},
  pages     = {135--147},
  year      = {2021}
}