INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
As it facilitates traffic management, lessens congestion, and improves the traveler experience, traffic prediction is a vital part of Intelligent Transportation Systems (ITS). In ITS, machine learning (ML) is now a common technology for predicting traffic. In order to estimate traffic for ITS, this paper will explore a variety of ML techniques, including Random Forest, Decision Tree, Logistic Regression, Naive Bayes, KNN, SVM, and Neural Networks. We test these methods using a range of evaluation metrics on real-world traffic datasets, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). We also go through various feature selection methods that are used to increase the precision of traffic prediction. Our findings demonstrate that ensemble methods, such Random Forest and SVM, perform better in traffic prediction for ITS than other methods.
Keywords:
Random Forest, Decision Tree, Naive Bayes, KNN, SVM, Logistic Regression
Cite Article:
"Traffic Prediction For Intelligent Transport System", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.e880-e886, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305506.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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