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

arXiv: https://arxiv.org/abs/2405.18602 · HTML: https://mlsanity.com/paper/2405.18602/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 4 of 4)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Neural Networks in TensorFlow and Keras with Spektral](https://mlsanity.com/paper/2006.12138/) (2020)
- [A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction](https://mlsanity.com/paper/1710.09543/) (2017)
- [Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data](https://mlsanity.com/paper/2103.05107/) (2021)

## Built on by (most cited first, 2 of 2)

- [ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity](https://mlsanity.com/paper/2506.08051/) (2025)
- [ALCo-FM: Adaptive Long-Context Foundation Model for Accident Prediction](https://mlsanity.com/paper/2507.08153/) (2025)

## Abstract (arXiv)

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.