Forecasting urban traffic flow by SVR with continuous ACO

• Published in: Applied mathematical modelling Vol. 35; no. 3; pp. 1282 - 1291
• Main Authors: Hong, Wei-Chiang, Dong, Yucheng, Zheng, Feifeng, Lai, Chien-Yuan
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Inc 01.03.2011; Elsevier
• Subjects: Algorithmics. Computability. Computer arithmetics; Applied sciences; Computer science; control theory; systems; Continuous ant colony optimization algorithms (CACO); Data processing. List processing. Character string processing; Exact sciences and technology; Forecasting; Ground, air and sea transportation, marine construction; Inference from stochastic processes; time series analysis; Inter-urban traffic forecasting; Mathematical analysis; Mathematical models; Mathematics; Memory organisation. Data processing; Probability and statistics; Regression; Road transportation and traffic; SARIMA; Sciences and techniques of general use; Software; Statistics; Support vector regression (SVR); Theoretical computing; Time series; Traffic engineering; Traffic flow; Traffic flow forecasting; Vectors (mathematics); Traffic flow forecasting; Support vector regression (SVR); SARIMA; Inter-urban traffic forecasting; Continuous ant colony optimization algorithms (CACO); Short term; ARMA model; Insecta; Formicoidea; Modeling; Traffic flow; Vector support machine; Swarm intelligence; Aculeata; Interurban traffic; Urban traffic; Non linear regression; Time series; Regression analysis; Social insect; Particle swarm optimization; Forecasting; Arthropoda; Traffic management; Road traffic; Hymenoptera; Invertebrata; Non linear effect; Traffic congestion
• ISSN: 0307-904X
• DOI: 10.1016/j.apm.2010.09.005

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. Because the information of inter-urban traffic presents a challenging situation, the traffic flow forecasting involves a rather complex nonlinear data pattern. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a short-term traffic forecasting model which combines the support vector regression model with continuous ant colony optimization algorithms (SVRCACO) to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed SVRCACO model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA) time series model. Therefore, the SVRCACO model is a promising alternative for forecasting traffic flow.