Abstract
Accurate forecasting of inter-urban traffic flow has been one of most important issues in the research on road traffic congestion. The traffic flow forecasting involves a rather complex nonlinear data pattern. Recently, support vector regression (SVR) model has been widely used to solve nonlinear regression and time series problems. This investigation presents a short-term traffic forecasting model which combines SVR model with continuous ant colony optimization (SVRCACO), to forecast inter-urban traffic flow. A numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed model. The simulation results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA) time-series model.
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Hong, WC., Pai, PF., Yang, SL., Lai, CY. (2007). Continuous Ant Colony Optimization in a SVR Urban Traffic Forecasting Model. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_92
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DOI: https://doi.org/10.1007/978-3-540-73007-1_92
Publisher Name: Springer, Berlin, Heidelberg
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