Traffic-Flow Forecasting using ARIMA, Neural Network and Judgment Adjustment

신경망, 시계열 분석 및 판단보정 기법을 이용한 교통량 예측

During the past few years, various traffic-flow forecasting models, i.e. an ARIMA, an ANN, and so on, have been developed to predict more accurate traffic flow. However, these models analyze historical data in an attempt to predict future value of a variable of interest. They make use of the following basic strategy. Past data are analyzed in order to identify a pattern that can be used to describe them. Then this pattern is extrapolated, or extended, into the future in order to make forecasts. This strategy rests on the assumption that the pattern that has been identified will continue into the future. So ARIMA or ANN models with its traditional architecture cannot be expected to give good predictions unless this assumption is valid; The statistical models in particular, the time series models are deficient in the sense that they merely extrapolate past patterns in the data without reflecting the expected irregular and infrequent future events Also forecasting power of a single model is limited to its accurate. In this paper, we compared with an ANN model and ARIMA model and tried to combine an ARIMA model and ANN model for obtaining a better forecasting performance. In addition to combining two models, we also introduced judgmental adjustment technique. Our approach can improve the forecasting power in traffic flow. To validate our model, we have compared the performance with other models. Finally we prove that the proposed model, i.e. ARIMA + ANN + Judgmental Adjustment, is superior to the other model.