Investigating the spatial transferability of freight generation (FG) models is an imperative research need to enable the usage of formerly estimated model parameters in new application contexts with or without the usage of local data. By understanding how to transfer models (and to what extent), planning agencies in large countries like India can save freight survey costs in regions where they lack the institutional capacity and resources. Due to geographically sprawling nature of most of the Indian cities, an important research question regarding transferability is whether the models developed for urban areas can provide accurate estimates of freight activity in the suburban areas or vice versa. This paper aims to provide two solutions to this problem: (i) compare the relative effectiveness of transferability depending up on the direction of transfer and (ii) assess which models can be transferred and which cannot. Data collected from seven cities are used for this study. A set of freight production (FP) models are developed using this data to understand the differential influence of geographical location and industry segment on the model coefficients. The estimated FP models show that suburban establishments exhibit significantly higher FP rates as compared to other establishments. Subsequently, transferability direction and accuracy are determined using standard metrics such as transfer R 2 , relative aggregate transfer error and transfer index. The transferability findings will provide actionable insights into development of FP models in regions with data constraints, which is of great value in an era of declining budgets for travel surveys.

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 distribution. 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 quantified using root-mean-square error and mean absolute error. The validation results show that the non-parametric SVR models 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 industrial 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.

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