A Novel Voronoi-Based Spatio-Temporal Graph Convolutional Network for Traffic Crash Prediction Considering Geographical Spatial Distributions

作者：Jing Gan, Qiao Yang, Dapeng Zhang, Linheng Li, Xu Qu, Bin Ran · 发表于：IEEE Transactions on Intelligent Transportation Systems · 年份：2024 · DOI：10.1109/tits.2024.3452275 · 被引用次数：9 · 研究领域：Traffic Prediction and Management Techniques、Artificial Intelligence in Healthcare

Accurately predicting the probability of crashes is crucial for preventing traffic crashes and mitigating their impacts. However, the imbalance in crash data, irregular road network structures, and heterogeneity in multi-source data pose significant challenges. To address these issues, this study introduces a spatio-temporal graph convolutional network traffic crash prediction model based on Voronoi diagrams that considers geographical spatial distribution. Initially, this study introduces a spatial partitioning method based on Voronoi diagrams, grounded on the geographic spatial distribution characteristics of traffic crashes. It constructs a novel graph structure with spatial units within Voronoi diagrams as nodes and the shared length of different road types between units as edges. This graph structure integrates the spatial distribution characteristics of crashes with the graph structure, substantially contributing to addressing the zero-inflation problem inherent in spatial units constructed on a grid basis. Subsequently, the study employs a GCN (Graph Convolutional Network) and Transformer encoder to build the VSTGCN (Voronoi-Based Spatio-Temporal Graph Convolutional Network) crash prediction model, evaluating its effectiveness using real data from New York City. Comparisons with eight baseline models demonstrate that VSTGCN outperforms them in all evaluation metrics. Moreover, the paper conducts model ablation studies from different perspectives, such as feature module...