Forecasting Generation of Freight Groups with Regression Models for Traffic Analysis Zones in Iran

Document Type : Research Article


1 Master's Graduate of Civil Engineering, Sharif University of Technology, Tehran, Iran

2 PhD Candidate of Civil Engineering, Sharif University of Technology, Tehran, Iran

3 Master's Graduate of Urban Planning, Sharif University of Technology, Tehran, Iran

4 Associate Professor of Civil Engineering, Sharif University of Technology, Tehran, Iran


In this research, for the first time, linear regression models are developed for Iran’s inland freight production & attraction classified by commodity types which provide an insight into long-term transportation planning. The dependent variables of these models are the total road and railway freight shipped to/from 418 counties across the country. In these models, general population and employment variables are implemented together with the binary variable of significant industrial and borderland counties to explain variations in the response variable. Validation of models involved considering a causal relationship between independent and response variables and measuring the statistical significance of regressors. The R-square statistic of the calibrated models stands between 0.85 and 0.98 which is appropriate considering the limited variables employed. To predict independent variables over the study horizon, the age profile of the base year is developed in a 25-year timeline starting from 2016, using time-varying birth rates and constant mortality and migration rates. Then assuming four unemployment scenarios, employment in each county is projected using the last predicted populations. According to the models’ estimation, the total freight produced/attracted is expected to reach 545/551 million tons in 2021 and 668/660 million tons in 2041 with a 12.5 percent unemployment rate. Furthermore, with the unemployment rate rising to 25 percent, the total produced/attracted freight is expected to fall 8.6/2.2 percent in 2021 and 9.4/2.4 percent in 2041. The results indicate the inadequacy of employment as the only explanatory variable of the production models while the population appropriately explains the bulk of the freight demand variations.


Main Subjects

[1] The Geography of Transport Systems, in: D.J.-P. Rodrigue (Ed.).
[2] L. Tavasszy, G. De Jong, Modelling freight transport, Elsevier, 2013.
[3] Standard Industrial Classification (SIC), 1987, Retrieved from United States Census Bureau website:
[4] North American Industry Classification System, Executive Office of the President Office of Management and Budget, United States, 2017.
[5]J.  Holguín-Veras, M. Jaller, I. Sanchez-Diaz, J. Wojtowicz, S. Campbell, H. Levinson, C. Lawson, E.L. Powers, L. Tavasszy, Freight trip generation and land use, 0309258782, 2012.
[6] Middela, Mounisai, Pulipati, Sasanka, Modeling Freight Generation and Distirbution for Nationwide Interstate Freight Movement, Transportation in Developing Economies, 2018.
[7] J. de Dios Ortuzar, L.G. Willumsen, Modelling transport, John Wiley & Sons, 2011