Spatio-temporal traffic accidents detection via graph based generative adversarial network

Due to urbanization and economic growth, traffic accidents have become a severe social problem. With the development of intelligent transportation systems and Internet of Things devices, detecting traffic accidents from big data is becoming an increasingly important trend for the future. However, there are several main challenges for accident detection. Firstly, traffic data is complex due to its spatial and temporal correlations. Secondly, traffic accidents are spatially and temporally dispersed, making them challenging to capture. Additionally, the high cost of labeling presents a significant obstacle, leading to a scarcity of available labels. Thirdly, unsupervised anomaly detection necessitates the approximation of normal samples, posing a challenge in approximating time series data in high-dimensional distributions collected from Internet of Things devices. To address these problems, we propose a novel spatio-temporal graph generative adversarial network framework, comprising a discriminator and a generator. The discriminator aims to identify fake and true samples by learning the representation of each input and its spatio-temporal context. The generator aims to generate fake data from the spatio-temporal context and fool the discriminator. Through adversarial training, the model can identify anomaly samples. We validate the performance of the proposed model on two real-world traffic accident datasets. The experimental results demonstrate that our model surpasses the baselines, thereby showcasing its effectiveness. Furthermore, a case study is conducted to analyze the characteristics and potential impact of the traffic accident, providing valuable insights for the improvement of this field and future research. © 2025 Elsevier Ltd.