Traffic accidents are recognized as a major social issue worldwide, causing
numerous injuries and significant costs annually. Consequently, methods for
predicting and preventing traffic accidents have been researched for many
years. With advancements in the field of artificial intelligence, various
studies have applied Machine Learning and Deep Learning techniques to traffic
accident prediction. Modern traffic conditions change rapidly by the minute,
and these changes vary significantly across different roads. In other words,
the risk of traffic accidents changes minute by minute in various patterns for
each road. Therefore, it is desirable to predict traffic accident risk at the
Minute-Level and Road-Level. However, because roads have close and complex
relationships with adjacent roads, research on predicting traffic accidents at
the Minute-Level and Road-Level is challenging. Thus, it is essential to build
a model that can reflect the spatial and temporal characteristics of roads for
traffic accident prediction. Consequently, recent attempts have been made to
use Graph Convolutional Networks to capture the spatial characteristics of
roads and Recurrent Neural Networks to capture their temporal characteristics
for predicting traffic accident risk. This paper proposes the Sequential based
Spatio-Temporal Graph Convolutional Networks (SST-GCN), which combines GCN and
LSTM, to predict traffic accidents at the Minute-Level and Road-Level using a
road dataset constructed in Seoul, the capital of South Korea. Experiments have
demonstrated that SST-GCN outperforms other state-of-the-art models in
Minute-Level predictions.

利用 GCN 和 LSTM 相结合的顺序空时图卷积网络（SST-GCN）预测首尔的道路数据集构建的分钟级和道路级交通事故，实验证明 SST-GCN 在分钟级预测方面胜过其他最先进的模型。

基于序列的时空图卷积网络用于分钟级和道路级交通事故风险预测

SST-GCN: The Sequential based Spatio-Temporal Graph Convolutional  networks for Minute-level and Road-level Traffic Accident Risk Predictio

Sparse meteorological forecasting is indispensable for fine-grained weather
forecasting and deserves extensive attention. Recent studies have highlighted
the potential of spatio-temporal graph convolutional networks (ST-GCNs) in
predicting numerical data from ground weather stations. However, as one of the
highest fidelity and lowest latency data, the application of the vision data
from satellites in ST-GCNs remains unexplored. There are few studies to
demonstrate the effectiveness of combining the above multi-modal data for
sparse meteorological forecasting. Towards this objective, we introduce
Vision-Numerical Fusion Graph Convolutional Network (VN-Net), which mainly
utilizes: 1) Numerical-GCN (N-GCN) to adaptively model the static and dynamic
patterns of spatio-temporal numerical data; 2) Vision-LSTM Network (V-LSTM) to
capture multi-scale joint channel and spatial features from time series
satellite images; 4) a GCN-based decoder to generate hourly predictions of
specified meteorological factors. As far as we know, VN-Net is the first
attempt to introduce GCN method to utilize multi-modal data for better handling
sparse spatio-temporal meteorological forecasting. Our experiments on Weather2k
dataset show VN-Net outperforms state-of-the-art by a significant margin on
mean absolute error (MAE) and root mean square error (RMSE) for temperature,
relative humidity, and visibility forecasting. Furthermore, we conduct
interpretation analysis and design quantitative evaluation metrics to assess
the impact of incorporating vision data.

VN-Net 是首个尝试在稀疏时空气象预报中利用图卷积网络和多模态数据的方法，其通过结合数值数据和卫星视觉数据进行预测，在 Weather2k 数据集上表现优异，超过了现有最先进方法的平均绝对误差 (MAE) 和均方根误差 (RMSE)。