Modeling retail establishments’ freight trip generation: a comparison of methodologies to predict total weekly deliveries

André Romano Alho () and João Abreu e Silva
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André Romano Alho: Universidade de Lisboa
João Abreu e Silva: Universidade de Lisboa

Transportation, 2017, vol. 44, issue 5, No 18, 1195-1212

Abstract: Abstract Assuming freight trip generation as the total number of freight vehicles arriving to retail establishments, for loading/unloading purposes and within a defined time period, we experiment and compare four alternative modeling methodologies to predict freight trip generation. The aim is to achieve better freight trip generation models, thus contributing to improving the chances of correctly dimension, for example, the parking infrastructure required to accommodate demand, or estimating the freight traffic impacts at micro level. Representing the state of the practice, the first two methodologies are based on cross-classification/category analysis. The third methodology uses a generalized linear model specification, a robust alternative to ordinary least squares linear regression. The fourth methodology consists in the exploration of a dependent variable simplification using an Ordinal Logit model. The main source of data is an Establishment-based Freight Survey, which collected data from 604 retail establishments in the city of Lisbon, Portugal. The selected independent variables were the establishments’ industry category, number of employees and retail area. The analysis allowed for the conclusion that (a) variable contribution varies depending on the chosen modelling methodology, (b) there is little variability in the quality of predictions depending on the selected model, but a considerable improvement in correct predictions can be achieved by reducing the variability of the dependent variable, and (c) the proposed indicator framework is suitable to compare model predictions and might be adequately represented by subset of those applied.

Keywords: Urban freight transport; City logistics; Freight trip generation modeling; Cross-classification/category analysis; Multiple regression (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11116-016-9700-z

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