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http://dx.doi.org/10.3837/tiis.2017.10.011

Hybrid CSA optimization with seasonal RVR in traffic flow forecasting  

Shen, Zhangguo (College of Computer Science and Technology, Zhejiang University of Technology)
Wang, Wanliang (College of Computer Science and Technology, Zhejiang University of Technology)
Shen, Qing (School of Information Engineering, Huzhou University)
Li, Zechao (School of Computer Science and Engineering, Nanjing University of Science and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.10, 2017 , pp. 4887-4907 More about this Journal
Abstract
Accurate traffic flow forecasting is critical to the development and implementation of city intelligent transportation systems. Therefore, it is one of the most important components in the research of urban traffic scheduling. However, traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during workday peak periods, and a lot of research has shown that traffic flow data reveals a seasonal trend. This paper proposes a new traffic flow forecasting model that combines seasonal relevance vector regression with the hybrid chaotic simulated annealing method (SRVRCSA). Additionally, a numerical example of traffic flow data from The Transportation Data Research Laboratory is used to elucidate the forecasting performance of the proposed SRVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal auto regressive integrated moving average (SARIMA), the double seasonal Holt-Winters exponential smoothing (DSHWES), and the relevance vector regression with hybrid Chaotic Simulated Annealing method (RVRCSA) models. The forecasting performance of RVRCSA with different kernel functions is also studied.
Keywords
Short term traffic flow forecasting; Relevance vector regression (RVR); Chaotic simulated annealing (CSA); Double seasonal holt-winters exponential smoothing (DSHWES);
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