Freight Focus Transportation Research Record 2023, Vol. 2677(2) 154–172 Ó National Academy of Sciences: Transportation Research Board 2022 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/03611981221116369 journals.sagepub.com/home/trr Comparison of Parametric and Non-Parametric Methods for Modeling Establishment-Level Freight Generation Bhavani Shankar Balla 1 , Prasanta K. Sahu 1 , Agnivesh Pani 2 , Sushant Sharma 3 , and Bandhan Bandhu Majumdar 1 Abstract The traditional parametric modeling approaches used to predict freight generation (FG), such as ordinary least squares (OLS), suffer from limitations because of the realistic possibility of violating basic assumptions such as linearity or data distri- bution. This problem is multidimensional because of the need to model numerous industry sectors in the freight system and the possibility of using different explanatory variables; little guidance is currently available on which modeling methodology is suitable for a particular case. The non-parametric models that could solve several limitations with traditional FG models are rarely examined in their predictive ability. This paper offers insights into this frequent research question by comparing the performance of various modeling methodologies that can be used for predicting FG: OLS, weighted least squares, robust regression (RR), seemingly unrelated regression (SUR), multiple classification analysis (MCA), and support vector regression (SVR). To this effect, the research carried out in this study uses a freight dataset of 432 establishments across Kerala State, India. The model outputs are validated using resubstitution and cross-validation methods, and the prediction errors are quan- tified using root-mean-square error and mean absolute error. The validation results show that the non-parametric SVR mod- els are better alternatives in developing state-, regional- and industrial segment-level models. The MCA models are more precise in predicting FG for suburban models. RR models provide a better predictive ability for modeling FG in some indus- trial segments. Overall comparison and result interpretations suggest that the non-parametric models are superior in relation to predicting FG. At the same time, RR seems to be the only parametric modeling approach that can provide comparable model performance to non-parametric models. Keywords freight systems, planning and logistics, model/modeling Competent truck/freight transportation influences the economic activity and productivity of a nation and glo- balizes industrial performance (1–3). It is statistically evi- dent that the truck/freight flows (in miles or km) have been increasing owing to the rise in world population coupled with higher demand for production and con- sumption of a multitude/numerous of commodities (1, 4). Subsequently, truck activity growth is amplified by sub- stantial augmentation in transportation infrastructure and associated facilities. The continually increasing truck movements coupled with rapid industrialization aggra- vate freight-related negative externalities such as conges- tion, pollution, and pavement deterioration in an urban road network (5–7). An inclusive freight transportation planning approach can reduce these externalities and fos- ter sustainable growth in freight while improving consu- mers’ experience of procuring goods and services. An inclusive transport planning enhances an inclusive society that leaves no one behind by providing safe and reliable access to individuals for traveling in their preferred 1 Department of Civil Engineering, Birla Institute of Technology and Science Pilani, Hyderabad, Telangana, India 2 Department of Civil Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, Uttar Pradesh, India 3 Texas A&M University, Texas A&M Transportation Institute, Arlington, TX Corresponding Author: Prasanta K. Sahu, prasantsahu222@gmail.com