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This paper presents an integrated clustering-regression modelling framework to assess the number of trips that each establishment of a given urban zone, city or conurbation generates. The goal of the method is to construct establishment-based trip models using the categorial FTG methodology using a minimal set of categories of activities. The methodology combines a hierarchical cluster analysis that defines a limited number of coherent categories of activities, then a regression-based modelling method that links the FTG rates (production, attraction and generation) to employment, giving a specific functional form for each category (constant, linear, logarithmic or potential). The models are constructed and calibrated on the basis of the 2011-2012 Freight Trip Survey of the Paris Region. Using a hierarchical clustering technique, 5 categories of establishment are defined and resulting FTG models obtained. Most functional forms are linear or potential, with some particular cases where constant generation is preferred. Results are assessed using the R2 coefficient and discussed. The resulting models are robust and applicable to any French city.

Highway proximity is essential to the economic growth of establishments and productivity of freight operations. The present study is motivated by the discernible gap in knowledge about the potential impacts of highway proximity on freight flow patterns of establishments. This knowledge gap impedes accurately estimating the potential benefits associated with infrastructure investments, especially related to freight-specific facility planning in developing countries. This paper investigates highway proximity impacts on freight establishments’ travel and shipment patterns using a comparative experiment approach. A total of 17 explanatory variables reflecting the freight flow pattern of an establishment have been considered. Proximity impact is evaluated by comparing the means of these explanatory variables among establishments closer to the highway, termed the “Influence” group, and those farther from the highway, defined as the “Control” group. Spatial and kernel analyses are carried out to determine the cut-off distance for demarcating samples into Influence and Control groups. The propensity score matching technique is subsequently used to create matched samples for a one-to-one comparison between the Influence group and Control group establishments. Results suggest that the Influence group typically consists of older establishments with larger business areas, generates many small-sized trips, and is associated with lower tonnage generation, expenditure, and shipment size than the Control group. The study findings are expected to have important implications on the infrastructure investment decision process and planning of freight operations.

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.

Increase in vehicle ownership (VO) as a result of rapid economic and population growth and its negative environmental effects have gradually become a potential threat to the physical and social environment. A wide range of factors influences the propensity for VO by individuals or firms; however, the existing body of literature lacks a critical review which provides an understanding of their underlying associations and potential implications for policymaking in one holistic frame. To fill this research gap, this study presents a systematic review of factors affecting private and commercial VO using the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). The factors influencing private VO decisions are extracted from the reviewed articles; subsequently, the identified factors are topologically divided into seven categories. The findings show that most studies identified the factors contributing to VO as related to socio-economic, demographic, and built environmental characteristics. A lack of research has been identified for attitudinal, technological, and parking-related variables. It is also concluded that there are gaps in the knowledge base on factors contributing to the commercial VO of a business establishment. Designing informed policy measures based on such factors that can induce a sustainable transition in the mobility sector is important to mitigate transportation-related negative externalities on the environment. The overall review findings offer actionable guidance for low-carbon mobility transition and outline a future research agenda for academic researchers and decision makers. The quantifiable factors revealed in this study are expected to assist policy makers in discouraging VO propensity among individuals or firms.