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

机译：建模零售场所的货运行程：比较预测每周总运送量的方法

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摘要

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.
机译：假设货运行程的产生是到达零售场所的货运车辆的总数，出于装载/卸载的目的，并且在定义的时间段内，我们进行实验并比较四种替代建模方法，以预测货运行程的产生。目的是获得更好的货运行程生成模型，从而有助于提高正确确定尺寸的机会，例如，满足需求所需的停车基础设施，或在微观层面估算货运量的影响。代表实践的状态，前两种方法基于交叉分类/类别分析。第三种方法使用广义线性模型规范，这是普通最小二乘线性回归的可靠替代方案。第四种方法是使用序数Logit模型探索因变量简化。数据的主要来源是基于机构的货运调查，该调查收集了来自葡萄牙里斯本市的604家零售机构的数据。选择的独立变量是企业的行业类别，员工人数和零售区域。分析得出的结论是：（a）变量影响取决于所选的建模方法，（b）取决于所选模型的预测质量变化不大，但是通过减少预测可以显着改善正确的预测（c）建议的指标框架适合比较模型预测，并可能由所应用预测的子集充分表示。

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