Hybrid evolutionary algorithms in a SVR traffic flow forecasting model |
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Authors: | Wei-Chiang Hong Yucheng DongFeifeng Zheng Shih Yung Wei |
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Institution: | a Department of Information Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, Taipei 220, Taiwan, ROC b Department of Organization and Management, School of Management, Xi’an Jiaotong University, Xi’an 710049, PR China c Department of Management Science, School of Management, Xi’an Jiaotong University, Xi’an 710049, PR China d Department & Graduate Institute of Finance, National Yunlin University of Science & Technology, 123 Sec. 3, University Rd., Douliou, Yunlin 640, Taiwan, ROC |
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Abstract: | Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt-Winters (HW) and seasonal Holt-Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow. |
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Keywords: | Traffic flow forecasting Support vector regression Hybrid genetic algorithm-simulated annealing algorithm (GA-SA) Hybrid evolutionary algorithms SARIMA Back-propagation neural network BPNN Holt-Winters (HW) Seasonal Holt-Winters (SHW) |
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