Please use this identifier to cite or link to this item: 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

Show full item record



CORE Recommender

Page view(s)

28
checked on Aug 24, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.