Forecasting, Multiple regression, Passenger demand, Cargo demand, Dummy variables

The aim of this paper is to propose a model to forecast human and cargo air transport demand in Iran aviation industry. Using multiple regression method, the effects of geographical, economic, social, and competitive aspects were investigated within two separate models for passengers and cargo. Accordingly, we gathered data for 594 active air routes in Iran air network between 2009 and 2012 from Iranian airport holding company. One advantage of the proposed model in comparison to pervious researches is the exclusion of frequency of flight variable. This variable restricts the ability of forecasting the models due to its high correlation with demand for travel. Therefore, models that include with frequency of flight variable are not efficient for forecasting demand. Additionally, the comparison of forecasting results and real demand for 2013 indicates the accuracy of forecasting model. Moreover, the results show that because of plenty of differences and erratic distribution between effective variables around the country, modeling should be specific to each city. This means that a general model for all cities is not appropriate and leads to an inaccurate forecasting, so we investigated separated models for metropolises of Iran to consider special situation of them. Also, defining dummy (Binary) variables in modeling of special cities helps to increase fitting of model to real observations. We can incorporate special situation of some cities into model by means of these variables. Therefore, one of the methods to conquest the inconsistency between cities of Iran is applying dummy variables to simulate conditions of special cities

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