Travel Behavior Analysis for Short-Term KTX Passenger Demand Forecasting Kim, Han-Soo; Yun, Dong-Hee; Lee, Sung-Duk;
This study analyzes the travel behavior for short-term demand forecasting model of KTX. This research suggests the following. First, the outlier criteria is considered to appropriate twice the standard deviation of the traffic. Second, the result of a homogeneity test using ANOVA analysis has been divided into weekdays(Mon Thu and weekends(Fri Sun). Third, a cluster analysis for O/D pairs using trip frequency, traffic averages and th distance between stations was performed.
Rousseeuw, P. J. (1986). A Visual Display for Hierarchical Classification, In Data Analysis and Informatics 4, edited by E. Diday, Y. Escoufier, L. Lebart, J. Pages, Y. Schektman, and R. Tomassone, North- Holland, Amsterdam, 743-748.
Sarle, W. S. (1983). Cubic Clustering Criterion, SAS Technical Report A-108, Cary, NC: SAS Institute Inc.
Tsai, T., Lee, C. and Wei, C. (2005). Design of dynamic neural networks to forecast short-term railway passenger demand, Journal of the Eastern Asia Society for Transportation Studies, 6, 1651-1666.
Wardman, M., Lythgoe, W. and Whelan, G. (2007). Rail passenger demand forecasting: Cross-sectional models revisited, Research in Transportation Economics, 20, 119-152.