Predicting road traffic density using a machine learning-driven approach-Reference-Cited by-同舟云学术

Predicting road traffic density using a machine learning-driven approach

Published:2021-12-09 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Electrical, Computer and Energy Technologies (ICECET)
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Author:

Zeroual Abdelhafid¹,Harrou Fouzi²,Sun Ying²

Affiliation:

1. University of 20 August 1955,Faculty of technology,Department of electrical engineering,Skikda,Algeria,21000

2. King Abdullah University of Science and Technology (KAUST),Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division,Thuwal,Saudi Arabia,23955–6900

Funder

King Abdullah University of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9698246/9698247/09698639.pdf?arnumber=9698639

Reference24 articles.

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2. Highway traffic forecasting by support vector regression model with tabu search algorithms

3. Air passengers traffic prediction with hybrid arima-svms models;wei;Mathematical Problems in Engineering Hindawi Publishing Corporation,2014

4. Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results

5. Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

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