DSTGCN is a graph-based neural network that predicts the risk of traffic accidents in the future.
Please refer to our Neurocomputing 2021 paper โDeep Spatio-Temporal Graph Convolutional Network for Traffic Accident Predictionโ for more details.
The descriptions of principal files in this project are introduced as follows:
- model/
spatial_layer.py
: the Spatial Convolutional Layerspatial_temporal_layer.py
: the Spatial-Temporal Convolutional Layerfully_connected.py
: fully connected network for the Embedding layerDSTGCN.py
: the Deep Spatio-Temporal Graph Convolutional Network
- preprocessing_data/
process_xxx.py
: process the raw 'xxx' datasetgenerate_data.py
: generate the dataset for our model
- transform_coord/: convert the coordinate, which could be obtained from here.
- train/
train_model.py
andtrain_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.
- 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) |
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.
- 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.
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}
}