통행발생예측의 오차 보정을 위한 모형 개발

다국어 초록 (Multilingual Abstract)

The paper analyzed error rates between trip generation forecasts and measurement values, and developed regression models for finding a forecast adjustment method. We estimated general linear regression models as well as spatial regression models that consider spatial autocorrelation among analysis regions. Regression models by total, commute, business and other purposes for intra-metropolitan trips and regression models by total, business and other purposes for inter-regional trips were developed, respectively. As results, intra-metropolitan trips showed a higher spatial autocorrelation than inter-regional trips. Spatial error(SE) model was chosen for intra-metropolitan commute trips, while ordinary least square(OLS) model was selected for the others. In cases of inter-regional trips, spatial error(SE) model or spatial lagged(SL) model was suitable for all purpose trips. Based on these models, trip generation forecasts were updated, showing improved RMSE%. The research could contribute to reducing forecast errors and increasing data reliability.

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