# Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data

arXiv: https://arxiv.org/abs/2103.05107 · HTML: https://mlsanity.com/paper/2103.05107/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 21 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [Learning feed-forward one-shot learners](https://mlsanity.com/paper/1606.05233/) (2016)
- [Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US](https://mlsanity.com/paper/1702.06683/) (2017)
- [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016)
- [Real-time Traffic Accident Risk Prediction based on Frequent Pattern Tree](https://mlsanity.com/paper/1701.05691/) (2017)
- [Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement](https://mlsanity.com/paper/1306.1704/) (2013)
- [Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life](https://mlsanity.com/paper/1608.00462/) (2016)
- [A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction](https://mlsanity.com/paper/1710.09543/) (2017)

## Built on by (most cited first, 3 of 3)

- [SST-GCN: The Sequential based Spatio-Temporal Graph Convolutional networks for Minute-level and Road-level Traffic Accident Risk Prediction](https://mlsanity.com/paper/2405.18602/) (2024)
- [ALCo-FM: Adaptive Long-Context Foundation Model for Accident Prediction](https://mlsanity.com/paper/2507.08153/) (2025)
- [MSGAT-GRU: A Multi-Scale Graph Attention and Recurrent Model for Spatiotemporal Road Accident Prediction](https://mlsanity.com/paper/2509.17811/) (2025)

## Abstract (arXiv)

Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate overfitting in fusing multimodal features and develop some new features such as fractal measure of road complexity in satellite images, taxi flows, POIs, and road width and connectivity in OpenStreetMap. The solution is more promising in performance than the baseline methods and the single-modality data based solutions. After visualization from a micro view, the visual patterns of the scenes related to high and low risk are revealed, providing lessons for future road design. From city point of view, the predicted risk map is close to the ground truth, and can act as the base in optimizing spatial configuration of resources for emergency response, and alarming signs. To the best of our knowledge, it is the first work to fuse visual and spatio-temporal features in traffic accident prediction while advances to bridge the gap between data mining based urban computing and computer vision based urban perception.