Open AccessSVRGSA: a hybrid learning based model for short‐term traffic flow forecasting
Author(s) -
Cai Lingru,
Chen Qian,
Cai Weihong,
Xu Xuemiao,
Zhou Teng,
Qin Jing
Publication year - 2019
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5315
Subject(s) - support vector machine , traffic flow (computer networking) , intelligent transportation system , computer science , term (time) , component (thermodynamics) , time series , machine learning , artificial intelligence , data mining , engineering , transport engineering , physics , computer security , quantum mechanics , thermodynamics
Accurate and timely short‐term traffic flow forecasting is a critical component for intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to complex non‐linear data pattern of traffic flow. Support vector regression (SVR) has been widely employed in non‐linear regression and time series prediction problems. However, the lack of knowledge of the choice of hyper‐parameters in the SVR model leads to poor forecasting accuracy. In this study, the authors propose a hybrid traffic flow forecasting model combining gravitational search algorithm (GSA) and the SVR model. The GSA is employed to search optimal SVR parameters. Extensive experiments have been conducted to demonstrate the superior performance of the proposal.