"A Hybrid Forecasting Framework Based on Support Vector Regression with" by Lizong Zhang, Nawaf R. Alharbe et al.

Home > TSINGHUA-SCIENCE-AND-TECHNOLOGY > Vol. 23 (2018) > Iss. 4

Article Title

A Hybrid Forecasting Framework Based on Support Vector Regression with a Modified Genetic Algorithm and a Random Forest for Traffic Flow Prediction

Authors

Lizong Zhang, the School of Computer Science and Engineering, University of Electronic Sciences and Technology of China, Chengdu 611731, China.
Nawaf R. Alharbe, the College of Community, Taibah University, Al-Madinah, Saudi Arabia.
Guangchun Luo, the School of Computer Science and Engineering, University of Electronic Sciences and Technology of China, Chengdu 611731, China.
Zhiyuan Yao, the School of Computer Science and Engineering, University of Electronic Sciences and Technology of China, Chengdu 611731, China.
Ying Li, the School of Computer Science and Engineering, University of Electronic Sciences and Technology of China, Chengdu 611731, China.

Keywords

traffic flow forecasting, feature selection, parameter optimization, genetic algorithm, machine learning

Abstract

The ability to perform short-term traffic flow forecasting is a crucial component of intelligent transportation systems. However, accurate and reliable traffic flow forecasting is still a significant issue due to the complexity and variability of real traffic systems. To improve the accuracy of short-term traffic flow forecasting, this paper presents a novel hybrid prediction framework based on Support Vector Regression (SVR) that uses a Random Forest (RF) to select the most informative feature subset and an enhanced Genetic Algorithm (GA) with chaotic characteristics to identify the optimal forecasting model parameters. The framework is evaluated with real-world traffic data collected from eight sensors located near the I-605 interstate highway in California. Results show that the proposed RF-CGASVR model achieves better performance than other methods.

DOI

https://doi.org/10.26599/TST.2018.9010045

Publisher

Tsinghua University Press

Recommended Citation

Lizong Zhang, Nawaf R Alharbe, Guangchun Luo et al. A Hybrid Forecasting Framework Based on Support Vector Regression with a Modified Genetic Algorithm and a Random Forest for Traffic Flow Prediction. Tsinghua Science and Technology 2018, 23(4): 479-492.

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