# RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

arXiv: https://arxiv.org/abs/2003.00819 · HTML: https://mlsanity.com/paper/2003.00819/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 30 of 33)

- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization](https://mlsanity.com/paper/2311.11114/) (2023)
- [Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition](https://mlsanity.com/paper/2202.04075/) (2022)
- [LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction](https://mlsanity.com/paper/2304.14343/) (2023)
- [Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation](https://mlsanity.com/paper/2306.10683/) (2023)
- [Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis](https://mlsanity.com/paper/2311.00164/) (2023)
- [AutoSTL: Automated Spatio-Temporal Multi-Task Learning](https://mlsanity.com/paper/2304.09174/) (2023)
- [STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction](https://mlsanity.com/paper/2307.00495/) (2023)
- [Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction](https://mlsanity.com/paper/2309.05072/) (2023)
- [Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity](https://mlsanity.com/paper/2407.19668/) (2024)
- [HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data](https://mlsanity.com/paper/2203.03100/) (2022)
- [TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks](https://mlsanity.com/paper/2304.08640/) (2023)
- [STS-CCL: Spatial-Temporal Synchronous Contextual Contrastive Learning for Urban Traffic Forecasting](https://mlsanity.com/paper/2307.02507/) (2023)
- [CrashFormer: A Multimodal Architecture to Predict the Risk of Crash](https://mlsanity.com/paper/2402.05151/) (2024)
- [Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen](https://mlsanity.com/paper/2505.21743/) (2025)
- [Traffic Accident Risk Forecasting using Contextual Vision Transformers](https://mlsanity.com/paper/2209.11180/) (2022)
- [Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques](https://mlsanity.com/paper/2406.13968/) (2024)
- [Safety in Traffic Management Systems: A Comprehensive Survey](https://mlsanity.com/paper/2308.06204/) (2023)
- [Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline](https://mlsanity.com/paper/2403.14941/) (2024)
- [Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics](https://mlsanity.com/paper/2208.08878/) (2022)
- [Graph Neural Networks in Intelligent Transportation Systems: Advances, Applications and Trends](https://mlsanity.com/paper/2401.00713/) (2024)
- [Enhancing Graph Neural Networks in Large-scale Traffic Incident Analysis with Concurrency Hypothesis](https://mlsanity.com/paper/2411.02542/) (2024)
- [Deep Learning Advances in Vision-Based Traffic Accident Anticipation: A Comprehensive Review of Methods, Datasets, and Future Directions](https://mlsanity.com/paper/2505.07611/) (2025)
- [SynEVO: A neuro-inspired spatiotemporal evolutional framework for cross-domain adaptation](https://mlsanity.com/paper/2505.16080/) (2025)
- [Test-time GNN Model Evaluation on Dynamic Graphs](https://mlsanity.com/paper/2509.23816/) (2025)
- [Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events](https://mlsanity.com/paper/2402.09553/) (2024)
- [ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution](https://mlsanity.com/paper/2403.01738/) (2024)
- [SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction](https://mlsanity.com/paper/2407.17642/) (2024)
- [LISA: Learning-Integrated Space Partitioning Framework for Traffic Accident Forecasting on Heterogeneous Spatiotemporal Data](https://mlsanity.com/paper/2412.15365/) (2024)

## Abstract (arXiv)

Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this work, we propose a novel framework RiskOracle, to improve the prediction granularity to minute levels. Specifically, we first transform the zero-risk values in labels to fit the training network. Then, we propose the Differential Time-varying Graph neural network (DTGN) to capture the immediate changes of traffic status and dynamic inter-subregion correlations. Furthermore, we adopt multi-task and region selection schemes to highlight citywide most-likely accident subregions, bridging the gap between biased risk values and sporadic accident distribution. Extensive experiments on two real-world datasets demonstrate the effectiveness and scalability of our RiskOracle framework.