Metode Hibrida FCM dan PSO-SVR untuk Prediksi Data Arus Lalu Lintas

Agri Kridanto, Joko Lianto Buliali


Abstract — Traffic flow forecasting is one important part in Intelligent Transportation System. There are many methods had been developed for time series and traffic flow forecasting such as: Autoregressive Moving Average (ARIMA), Artificial Neural Network (ANN), and Support Vector Regression (SVR). SVR performance depend on kernel function and parameters of those kernel and data characteristic used in SVR as well. This research proposed hybrid method for traffic flow data clustering and forecasting. Fuzzy C-means is used in  order to minimize the variance in whole dataset. Particle Swarm Optimization (PSO) is used in order to select the appropriate parameters for SVR. Experimental result shows the proposed method give MAPE below 4% in all test sites.


Keywords—fuzzy c-means, particle swarm optimization, prediksi data lalu lintas, support vector regression, time-series.

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