Abstract

Evaluation of intercity trips may be used for anticipation of trip demands, services’ pricing and improvement of the studies for contradiction determination. Usage of trip demand modeling processes, like what existed in the classic method for analyzing urban trip demand, would make the analysis of road trips possible. A substitute method for 4 Steps Models is to use Direct Demand Modeling which is consisted from sub-models of Trip Generation, Trip Distribution, Modal Split simultaneously. The main problem in such model is that they need many numbers of different parameters in order to fulfill all the aforesaid benefits. If the number of alternatives is increased, it would be hard to calibrate the direct demand models and with low number of alternatives, the results form models would lack enough power in anticipation of the future demand. In case we can enter more number of alternatives in model using a scientific method and complete the calibration process successfully, it is possible to remove the problem of direct models for trip demand. So in this paper a new methodology for evaluation of parameters effective on air trips demand to be used in trip demand modeling with the use of Factor Analysis technique. Based on the given methodology herein, at first many alternatives are selected for modeling the trip demand. Then using factor analysis technique a new pattern of alternatives is made and tested. After completing the process for decreasing data structure with the use of factor analysis technique and obtaining the relative results, we would construct a direct model for air trip demand in linear regression method. Comparing the results form models constructed on the base of data from factor analysis methodology in this paper with simple alternatives in linear regression method, satisfactorily results has been obtained. Validating the model based on the given method in this paper indicate a significant success of the method in estimation of air trip demand in Iran compared to common methods of air trip demand modeling.