Machine learning based accident prediction in secure IoT enable transportation system

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rap...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2022-01, Vol.42 (2), p.713-725
Main Authors: Mohanta, Bhabendu Kumar, Jena, Debasish, Mohapatra, Niva, Ramasubbareddy, Somula, Rawal, Bharat S.
Format: Article
Language:English
Subjects:
Accident prediction
Artificial intelligence
Artificial neural networks
Classification
Cryptography
Decision trees
Intelligent transportation systems
Internet of Things
Machine learning
New technology
Smart cities
Traffic accidents
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Summary:Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-189743