Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area

Abstract

This paper compares the performance of trip generation models. Trip generation estimates the number of trips to and from a traffic analysis zone. This process is the first stage of the conventional four-step travel forecasting framework. Although many approaches have been suggested for this step, regression and category analyses have been widely applied. The two methods have generated an acceptable level of performance from the perspective of transport planning. Critical problems, however, have also been observed. In the regression analysis, trip rates are treated as continuous variables that can be negative, which is obviously unrealistic. Furthermore, the method does not incorporate traveler behavior. For the category analysis, its arbitrary way of choosing independent variables and their strata has drawn critiques. The cell-by-cell calculation in this method also increases the concerns about unreliable estimation of trip rates. Censored regression, count data, and discrete choice models have been visited for the alternative of regression approach while the multiple classification method has been conceived for the substitute of the category analysis. A systematic examination of the performance among the models has not been discussed sufficiently yet, which is the motive of this paper. Six representative models – regression, tobit, Poisson, ordered logit, category, and multiple classification analyses – were applied to the home-based work trips in the Seoul metropolitan area. Cross-validation and back-casting were the key for checking the performance among the models. In this process, the measures of correlation, variance, and coincidence were compared. The category-type model was superior in overall performance.