# Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

arXiv: https://arxiv.org/abs/2310.10038 · HTML: https://mlsanity.com/paper/2310.10038/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning](https://mlsanity.com/paper/2008.00334/) (2020)
- [Towards High Performance Video Object Detection](https://mlsanity.com/paper/1711.11577/) (2017)
- [Vision-Based Traffic Accident Detection and Anticipation: A Survey](https://mlsanity.com/paper/2308.15985/) (2023)
- [Computer Vision-based Accident Detection in Traffic Surveillance](https://mlsanity.com/paper/1911.10037/) (2019)
- [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://mlsanity.com/paper/2208.09588/) (2022)
- [Real-Time Accident Detection in Traffic Surveillance Using Deep Learning](https://mlsanity.com/paper/2208.06461/) (2022)
- [Reduced-Gate Convolutional LSTM Using Predictive Coding for Spatiotemporal Prediction](https://mlsanity.com/paper/1810.07251/) (2018)
- [Baby Physical Safety Monitoring in Smart Home Using Action Recognition System](https://mlsanity.com/paper/2210.12527/) (2022)
- [AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities](https://mlsanity.com/paper/2307.12128/) (2023)

## Built on by (most cited first, 8 of 8)

- [Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors](https://mlsanity.com/paper/2502.00402/) (2025)
- [Integrating Generative Adversarial Networks and Convolutional Neural Networks for Enhanced Traffic Accidents Detection and Analysis](https://mlsanity.com/paper/2506.16186/) (2025)
- [DARTS: A Drone-Based AI-Powered Real-Time Traffic Incident Detection System](https://mlsanity.com/paper/2510.26004/) (2025)
- [Big Data and Deep Learning in Smart Cities: A Comprehensive Dataset for AI-Driven Traffic Accident Detection and Computer Vision Systems](https://mlsanity.com/paper/2401.03587/) (2024)
- [TemporalAugmenter: An Ensemble Recurrent Based Deep Learning Approach for Signal Classification](https://mlsanity.com/paper/2401.06970/) (2024)
- [Context-Aware Detection of Mixed Critical Events using Video Classification](https://mlsanity.com/paper/2411.15773/) (2024)
- [Pre-trained Under Noise: A Framework for Robust Bone Fracture Detection in Medical Imaging](https://mlsanity.com/paper/2507.09731/) (2025)
- [Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction](https://mlsanity.com/paper/2601.17216/) (2026)

## Abstract (arXiv)

The dynamic and unpredictable nature of road traffic necessitates effective accident detection methods for enhancing safety and streamlining traffic management in smart cities. This paper offers a comprehensive exploration study of prevailing accident detection techniques, shedding light on the nuances of other state-of-the-art methodologies while providing a detailed overview of distinct traffic accident types like rear-end collisions, T-bone collisions, and frontal impact accidents. Our novel approach introduces the I3D-CONVLSTM2D model architecture, a lightweight solution tailored explicitly for accident detection in smart city traffic surveillance systems by integrating RGB frames with optical flow information. Our experimental study's empirical analysis underscores our approach's efficacy, with the I3D-CONVLSTM2D RGB + Optical-Flow (Trainable) model outperforming its counterparts, achieving an impressive 87% Mean Average Precision (MAP). Our findings further elaborate on the challenges posed by data imbalances, particularly when working with a limited number of datasets, road structures, and traffic scenarios. Ultimately, our research illuminates the path towards a sophisticated vision-based accident detection system primed for real-time integration into edge IoT devices within smart urban infrastructures.