Machine Learning Based Road Crash Data Analysis and Prediction

• Author(s): Harshitha M; Nisarga H; Nikhitha P; Pallavi M; Supritha Shree B A
• Paper ID: 1718068
• Page: 3558-3561
• Published Date: 25-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

Road crashes have emerged as a major global health issue, particularly impacting vulnerable road users such as pedestrians, cyclists, and two-wheeler riders in developing nations. Existing traffic systems rely on classical learning models that are inefficient, less accurate, and limited to manual record-keeping without performing intelligent analysis. This paper presents a web-based real-time application integrated with data mining and unsupervised machine learning classification algorithms to analyze traffic crash data and predict the environmental, behavioral, and situational factors contributing to accidents. The proposed system automates pattern discovery and parameter tuning to discover hidden traffic associations, providing data-driven insights that assist traffic departments in implementing preventive road safety measures.

Keywords

Machine Learning, Association Rule Mining, Apriori Algorithm, Traffic Safety, Road Crash Prediction, Digital Platforms.

Citations

IRE Journals:
Harshitha M, Nisarga H, Nikhitha P, Pallavi M, Supritha Shree B A "Machine Learning Based Road Crash Data Analysis and Prediction" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 3558-3561 https://doi.org/10.64388/IREV9I11-1718068

IEEE:
Harshitha M, Nisarga H, Nikhitha P, Pallavi M, Supritha Shree B A "Machine Learning Based Road Crash Data Analysis and Prediction" Iconic Research And Engineering Journals, 9(11) https://doi.org/10.64388/IREV9I11-1718068