Title: Reliable methodology for acquiring exact road accident information using deep learning

Authors: S. Sophia; K. Iyswarya; S.R. Boselin Prabhu; U.K. Sridevi

Addresses: Sri Krishna College of Engineering and Technology, Kuniyamuthur, Coimbatore, 641008, Tamil Nadu, India ' Ambal Professional Group of Institutions, K.N. Puram, Trichy Road, Palladam, 641 662, Tamil Nadu, India ' Surya Engineering College, Kathirampatti, Mettukadai, 638107, Tamil Nadu, India ' PSG College of Technology, Peelamedu, Coimbatore, 641004, Tamil Nadu, India

Abstract: In a highly populated country like India, the death rate increases alarmingly due to road accidents. Various reasons for road accidents include rash driving, violating the speed limits, drunken driving, bad weather conditions, improper road construction and maintenance, etc. For avoiding these road accidents, intelligent prediction of the occurrence of road accidents would be greatly helpful. The main intention of the research in this paper is to formulate an information-centric road safety mechanism that shall increase safety to human lives. This paper deals with the proposal of a reliable deep learning technique for prediction of road accidents through a comparative analysis of K-nearest neighbours (K-NN) algorithm and Regression algorithm. Coimbatore city located in Tamilnadu, India is being chosen for the data procurement. Various causes that lead to accidents and their impacts like death rate, time of accidents and category of vehicles involved in the accidents were chosen for analysis. On the basis of comparative analysis and its prediction results, this research work aids in modelling an information-centred road safety mechanism that helps in preventing future road accidents in a cost-effective way.

Keywords: reliable transportation; information-centric approach; regression algorithm; deep learning; predictive analysis; road accidents; accident prone areas.

DOI: 10.1504/IJBIS.2025.144863

International Journal of Business Information Systems, 2025 Vol.48 No.3, pp.406 - 432

Received: 29 Mar 2021
Accepted: 02 Jul 2021
Published online: 06 Mar 2025 *

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