کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
409880 679101 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Intersection traffic flow forecasting based on ν-GSVR with a new hybrid evolutionary algorithm
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Intersection traffic flow forecasting based on ν-GSVR with a new hybrid evolutionary algorithm
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 147, 5 January 2015, Pages 343–349
نویسندگان
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