Intersection traffic flow forecasting based on ν-GSVR with a new hybrid evolutionary algorithm

Abstract To deal well with the normally distributed random error existed in the traffic flow series, this paper introduces the ν-Support Vector Regression (ν-GSVR) model with the Gaussian loss function to the prediction field of short-term traffic flow. A new hybrid evolutionary algorithm (namely CCGA) is established to search the appropriate parameters of the ν-GSVR, coupling the Chaos map, Cloud model and genetic algorithm. Consequently, a new forecasting approach for short-term traffic flow, combining ν-GSVR model and CCGA algorithm, is proposed. The forecasting process considers the traffic flow for the road during the first few time intervals, the traffic flow for the upstream road section and weather conditions. A numerical example from the intersection between Culture Road and Shi-Full Road in Banqiao is used to verify the forecasting performance of the proposed model. The experiment indicates that the model yield more accurate results than the compared models in forecasting the short-term traffic flow at the intersection.

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