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https://hdl.handle.net/11499/51281
Title: | Short-term Traffic Prediction Based-on Support Vector Regression | Authors: | Urkan, O.D. Cetin, M. |
Keywords: | intelligent transportation systems machine learning Short-term traffic prediction support vector regression Forecasting Intelligent vehicle highway systems Logistic regression Support vector regression Traffic control Forecasting models Intelligent transportation systems Machine-learning Normal condition Prediction-based Short-term traffic prediction Support vector regression models Support vector regressions Traffic Forecasting Traffic prediction Intelligent systems |
Publisher: | Institute of Electrical and Electronics Engineers Inc. | Abstract: | Realizing the short-term traffic prediction under various situations is important for the traffic to continue in a certain pattern or normal conditions. Most of the designed traffic forecasting models aim to present results for the future by taking into account all traffic conditions (accidents, weather conditions, road works, events, etc.). In this study, a traffic model has been developed in order for intelligent transportation systems to predict the traffic that will occur and make it easier to take action accordingly. In order to minimize the computational complexity in training phase, the Support Vector Regression (SVR) method, which is a Machine Learning (ML) method, has been preferred. The SVR model was trained using data collected from a busy intersection for Denizli province, and then short-term traffic prediction was performed. The hyper-parameter optimization of the model trained with four different directions and thirty-day data was made and the model accuracy was presented with several criteria. The results obtained by SVR model for traffic prediction are satisfactory in terms of prediction accuracy, similar to the typical traffic prediction models in the literature. © 2022 IEEE. | Description: | 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October 2022 through 26 October 2022 -- 291239 | URI: | https://doi.org/10.1109/ICDABI56818.2022.10041512 https://hdl.handle.net/11499/51281 |
ISBN: | 9781665490580 |
Appears in Collections: | Mühendislik Fakültesi Koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection |
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