Title: Map Reduce approach for road accident data analysis using data mining techniques

Authors: S. Nagendra Babu; J. Jebamalar Tamilselvi

Addresses: R&D Center, Bharathiar University, Coimbatore, India ' Jaya Engineering College, CTH Road, Prakash Nagar, Thiruninravur, Chennai, Tamil Nadu 602024, India

Abstract: Nowadays, the most life-threatening risk to humans is road accidents. Traffic accidents that cause a lot of damages are occurring all over the place. The best answer for these sorts of accidents is to foresee future accidents ahead of time, giving driver's odds to maintain a strategic distance from the perils or decrease the harm by reacting rapidly. The motivation behind this manuscript is to fabricate an anticipating structure that can resolve every one of these issues. This paper proposed hybrid N-clustering algorithm for performing clustering on road accident data and then improved association rule mining algorithm (IARM) for designing of several association rules for accident prediction and congestion control using machine framework (CCMF) and traffic congestion analyser using Map Reduce for efficient prediction of road accident on several factors using Map Reduce methods. To enhance the foreseeing precision, amended information is arranged into a few gatherings, to which characterisation investigation is connected.

Keywords: road accident prediction; Map Reduce; clustering; pre-processing; association rules; dataset.

DOI: 10.1504/IJAIP.2024.136784

International Journal of Advanced Intelligence Paradigms, 2024 Vol.27 No.1, pp.1 - 17

Received: 24 Mar 2018
Accepted: 16 Apr 2018

Published online: 22 Feb 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article