# AI on the Road: A Comprehensive Analysis of Traffic Accidents and Accident Detection System in Smart Cities

arXiv: https://arxiv.org/abs/2307.12128 · HTML: https://mlsanity.com/paper/2307.12128/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 22 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Temporal Action Detection with Structured Segment Networks](https://mlsanity.com/paper/1704.06228/) (2017)
- [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://mlsanity.com/paper/2208.09588/) (2022)
- [Weakly-supervised Temporal Action Localization by Uncertainty Modeling](https://mlsanity.com/paper/2006.07006/) (2020)
- [Deep Learning Algorithm for Threat Detection in Hackers Forum (Deep Web)](https://mlsanity.com/paper/2202.01448/) (2022)

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

- [Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection](https://mlsanity.com/paper/2310.10038/) (2023)
- [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)
- [Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities](https://mlsanity.com/paper/2503.02967/) (2025)

## Abstract (arXiv)

Accident detection and traffic analysis is a critical component of smart city and autonomous transportation systems that can reduce accident frequency, severity and improve overall traffic management. This paper presents a comprehensive analysis of traffic accidents in different regions across the United States using data from the National Highway Traffic Safety Administration (NHTSA) Crash Report Sampling System (CRSS). To address the challenges of accident detection and traffic analysis, this paper proposes a framework that uses traffic surveillance cameras and action recognition systems to detect and respond to traffic accidents spontaneously. Integrating the proposed framework with emergency services will harness the power of traffic cameras and machine learning algorithms to create an efficient solution for responding to traffic accidents and reducing human errors. Advanced intelligence technologies, such as the proposed accident detection systems in smart cities, will improve traffic management and traffic accident severity. Overall, this study provides valuable insights into traffic accidents in the US and presents a practical solution to enhance the safety and efficiency of transportation systems.