Prediction of Road Accident Using Artificial Neural Network

Prediction of Road Accident Using Artificial Neural Network

© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Mayura Yeole, Rakesh Kumar Jain, Radhika Menon
DOI :  10.14445/22315381/IJETT-V70I2P217

How to Cite?

Mayura Yeole, Rakesh Kumar Jain, Radhika Menon, "Prediction of Road Accident Using Artificial Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 151-161, 2022. Crossref,

A traffic accident is one of the biggest road safety concerns. Predictability models are used to explain the relationship between highway calamity and applicable parameters such as volume of traffic, conditions of roads and environmental concerns. For this work, a prediction model has been developed using ANN (Artificial Neural Network) and compared with the multiple linear regression techniques. The dataset collected and used for this study was for a mixed traffic flow of Pimpri Chinchwad Municipal Corporation (PCMC), Pune, Maharashtra, India. Weather condition, Vehicles loading condition, number of lanes and different time slots of accidents are considered as input parameters. Along with these, Traffic signals, speed breakers, the road intersects were also reflected as input parameters. For this, the accident data were collected for a period of six years ranging from 2014 to 2019. A recorded number of 887 major, as well as minor accidents, were considered for this study. For the ANN model, the available accident dataset was divided into three parts. In this study, 70% of data were used for model preparation, 15% for Training and Testing of the model and the remaining 15% for Validation of the model. Results show that the prediction model using ANN gives excellent accuracy. In this study, more emphasis has been given to the actual parameters which are responsible for accidents caused by mixed traffic flow.

Regression, Performance, Road accident, Prediction, Artificial Neural Network.

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