System For Predicting Traffic Violations Using Machine Learning

Registration ID: IJNRD_196379

Published ID: IJNRD2305828

DOI: Click Here to Get

Glen P Jose , Judin Joseph , Oliviya S Paimattam , Riya Mol Joseph , Naveena Tresa Joseph

Artificial Neural Network, Random Forest, Support Vector Machine, Machine Learning, Human Behaviour, Traffic Violation Prediction

This system is designed to predict traffic violation of a driver on the basis of their behaviour. As road accidents shoot up day by day, there is an increased chance of neglecting enforced rules by drivers. This temptation in drivers can be due to physical, mental and emotional states. This is analysed by comparing different traits like demographic information, psychological problems, addiction, personality trait, aggression and stress. Prediction is made based on behavioural analysis of drivers. Traffic violation and human behaviour is highly related. Researches show that some victims of accidents are suffering from personality and behaviour defects. In such a scenario a prediction system using human behaviour in case of traffic violation is very relevant. In addition, different machine learning models are compared to identify the accuracy score and the best one is implemented in the system. Algorithms used for comparison are ANN, Random Forest and SVM.

Published Paper   E-Certificate

Glen P Jose, Judin Joseph, Oliviya S Paimattam, Riya Mol Joseph, & Naveena Tresa Joseph (May-2023). System For Predicting Traffic Violations Using Machine Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), i213-i217. https://ijnrd.org/papers/IJNRD2305828.pdf

Volume 8 Issue 5, May-2023

Pages : i213-i217

Paper Reg. ID: IJNRD_196379

Published Paper Id: IJNRD2305828

Downloads: 000121985

Research Area: Information Technology 

Country: Kottayam, Kerala, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305828.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2305828

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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