# Accident Risk Prediction based on Heterogeneous Sparse Data: New Dataset and Insights

arXiv: https://arxiv.org/abs/1909.09638 · HTML: https://mlsanity.com/paper/1909.09638/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 24 of 24)

- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey](https://mlsanity.com/paper/2102.09759/) (2021)
- [Graph Neural Networks for Road Safety Modeling: Datasets and Evaluations for Accident Analysis](https://mlsanity.com/paper/2311.00164/) (2023)
- [Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction](https://mlsanity.com/paper/2309.05072/) (2023)
- [Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classification](https://mlsanity.com/paper/2403.13547/) (2024)
- [TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks](https://mlsanity.com/paper/2304.08640/) (2023)
- [CrashFormer: A Multimodal Architecture to Predict the Risk of Crash](https://mlsanity.com/paper/2402.05151/) (2024)
- [Sensing accident-prone features in urban scenes for proactive driving and accident prevention](https://mlsanity.com/paper/2202.12788/) (2022)
- [Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques](https://mlsanity.com/paper/2406.13968/) (2024)
- [Spatiotemporal Data Mining: A Survey](https://mlsanity.com/paper/2206.12753/) (2022)
- [Will there be a construction? Predicting road constructions based on heterogeneous spatiotemporal data](https://mlsanity.com/paper/2209.06813/) (2022)
- [Traffic incident duration prediction via a deep learning framework for text description encoding](https://mlsanity.com/paper/2209.08735/) (2022)
- [TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents](https://mlsanity.com/paper/2407.11477/) (2024)
- [Causal DAG Summarization (Full Version)](https://mlsanity.com/paper/2504.14937/) (2025)
- [Beta Distribution Learning for Reliable Roadway Crash Risk Assessment](https://mlsanity.com/paper/2511.04886/) (2025)
- [An Adaptive Clustering Approach for Accident Prediction](https://mlsanity.com/paper/2108.12308/) (2021)
- [STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction](https://mlsanity.com/paper/2308.13183/) (2023)
- [SMA-Hyper: Spatiotemporal Multi-View Fusion Hypergraph Learning for Traffic Accident Prediction](https://mlsanity.com/paper/2407.17642/) (2024)
- [Accident Impact Prediction based on a deep convolutional and recurrent neural network model](https://mlsanity.com/paper/2411.07537/) (2024)
- [Accident-Driven Congestion Prediction and Simulation: An Explainable Framework Using Advanced Clustering and Bayesian Networks](https://mlsanity.com/paper/2507.22529/) (2025)
- [Predicting Traffic Accident Severity with Deep Neural Networks](https://mlsanity.com/paper/2509.03819/) (2025)
- [VZCrash: A Large-Scale IMU Dataset of Ego-Vehicle Crashes](https://mlsanity.com/paper/2606.06074/) (2026)
- [Seahorse: A Unified Benchmarking Framework for Spatiotemporal Event Modeling](https://mlsanity.com/paper/2607.01022/) (2026)
- [CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects](https://mlsanity.com/paper/2608.27942/) (2026)

## Abstract (arXiv)

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.