Road Accident Severity Detection In Smart Cities

Deeksha K, Kavya S, Nikita J, Evangeline R. C and Evangeline R. C

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 2, 180-187

Abstract: Ensuring safety, in cities is a focus in the development of urban areas requiring new and creative methods for categorizing and managing accidents. Traditional approaches often face challenges in evaluating accident seriousness within changing city environments. This research utilizes Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to create a system that categorizes accidents into three severity levels; minor, moderate and severe. By leveraging learning capabilities, our method boosts the precision and efficiency of safety protocols in cities. The outcomes exhibit promising results in categorizing accident severity offering a tool for enhancing urban safety infrastructure. Through empowering cities to handle accidents, our model establishes a foundation for safety initiatives. In essence, this study contributes to enhancing safety standards in cities promoting resilience and sustainability, within settings.

Keywords: Smart Cities; Deep Learning; Long Short-Term Memory; Accident Classification; Severity Prediction; Urban Safety Infrastructure (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT241024 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT241024/CSEIT241024 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i2:id:27

DOI: 10.32628/CSEIT241024

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().