Understanding the spatial and temporal dynamics of automated vehicle (AV) crash severity is critical for advancing urban mobility safety and infrastructure planning. In this work, we introduce ST-GraphNet, a spatio-temporal graph neural network framework designed to model and predict AV crash severity by using both fine-grained and region-aggregated spatial graphs. Using a balanced dataset of 2,352 real-world AV-related crash reports from Texas (2024), including geospatial coordinates, crash timestamps, SAE automation levels, and narrative descriptions, we construct two complementary graph representations: (1) a fine-grained graph with individual crash events as nodes, where edges are defined via spatio-temporal proximity; and (2) a coarse-grained graph where crashes are aggregated into Hexagonal Hierarchical Spatial Indexing (H3)-based spatial cells, connected through hexagonal adjacency. Each node in the graph is enriched with multimodal data, including semantic, spatial, and temporal attributes, including textual embeddings from crash narratives using a pretrained Sentence-BERT model. We evaluate various graph neural network (GNN) architectures, such as Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Dynamic Spatio-Temporal GCN (DSTGCN), to classify crash severity and predict high-risk regions. Our proposed ST-GraphNet, which utilizes a DSTGCN backbone on the coarse-grained H3 graph, achieves a test accuracy of 97.74\%, substantially outperforming the best fine-grained model (64.7\% test accuracy). These findings highlight the effectiveness of spatial aggregation, dynamic message passing, and multi-modal feature integration in capturing the complex spatio-temporal patterns underlying AV crash severity.
Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques
University of Kentucky（肯塔基大学）；University of Louisville（路易斯维尔大学）；Kentucky Transportation Center（肯塔基交通中心）
RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction
1 Department of Computer Science；Engineering, Santa Clara University, Santa Clara, USA Email；2 Manning College of Information \& Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA Email；3 Department of Electrical；Computer Engineering, Carnegie Mellon University, Pittsburgh, USA Email；4 University of Pennsylvania, Philadelphia, USA Email
Michigan State University（密歇根州立大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Causal analysis and classification of injury severity applying non-parametric methods for traffic crashes has received limited attention. This study presents a methodological framework for causal inference, using Granger causality analysis, and injury severity classification of traffic crashes, occurring on interstates, with different machine learning techniques including decision trees (DT), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN). The data used in this study were obtained for traffic crashes on all interstates across the state of Texas from a pe…
Predicting and Explaining Traffic Crash Severity Through Crash Feature Selection
Honda Research Institute Europe（霍恩达欧洲研究机构）；The Ohio State University（俄亥俄州立大学）；Department of Computer Science, University of Crete（克里特大学计算机科学系）；American Honda Motor Co., Inc.（美国本田摩托公司）
From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes
Department of Civil Engineering（土木工程系）；Rajshahi University of Engineering & Technology（拉贾沙希工程与技术大学）；Traffic Safety Group（交通安全组）；Multimodal Planning Division (MPD)（多模式规划部门）；Arizona Department of Transportation (ADOT)（亚利桑那州交通部门）；Texas Southern University（德克萨斯南方大学）
Long-Horizon Traffic Forecasting via Incident-Aware Conformal Spatio-Temporal Transformers
Department of Mechanical and Aerospace Engineering and the Center for Automotive Research, The Ohio State University（机械与航空航天工程系和汽车研究中心，俄亥俄州立大学）；Ohio Department of Transportation（俄亥俄州交通部）；Honda Research Institute USA, Inc.（本田美国研究院）
