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

作　　者：Lizong Zhang Nawaf R Alharbe Guangchun Luo Zhiyuan Yao Ying Li 

机构地区：[1]School of Computer Science and Engineering,University of Electronic Sciences and Technology of China [2]College of Community,Taibah University, Al-Madinah, Saudi Arabia

出　　处：《Tsinghua Science and Technology》2018年第4期479-492,共14页清华大学学报（自然科学版（英文版）

基　　金：supported by the Science and Technology Department of Sichuan Province of China (Nos. 2017JY0007, 2016JY0073, and 2016JZ0031);the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry;the Fundamental Research Funds for the Central Universities (No. ZYGX2015J063)

摘　　要：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 1-605 interstate highway in California. Results show that the proposed RF- CGASVR model achieves better performance than other methods.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 1-605 interstate highway in California. Results show that the proposed RF- CGASVR model achieves better performance than other methods.

关 键 词：traffic flow forecasting feature selection parameter optimization genetic algorithm machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] U491.2[自动化与计算机技术—控制科学与工程]