Freight Demand Forecasting Considering Economic Growth Factors

Q3 Economics, Econometrics and Finance
Taesung Hwang
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引用次数: 3

Abstract

This work proposes a methodology to project future freight demand for all commodity types that begin and end in each geographical region and the amount of freight that moves between all origin and destination pairs. Following the traditional four-step demand forecasting framework, the procedure corresponds to trip generation and trip distribution analysis for interregional freight demand. Using future economic growth factors from macroeconomic and input-output models, the amounts of freight production and attraction in each analysis zone are forecasted and taken as given. Subsequently, an iterative matrix balancing method is applied to determine the estimated freight shipment demand for all origin and destination zone pairs. The proposed algorithm is applied to generate predicted future freight demand within the United States from 2010-2050 in five-year increments based on the national freight demand data from 2007. Four different scenarios are proposed that consider variations in both global economic growth and environmental regulation. This study will assist transportation planners and decision makers in public and private sectors to assess how future freight delivery demand on the national scale considering various future global economic growth and environmental policy scenarios will affect various issues such as air quality and human health problems.
考虑经济增长因素的货运需求预测
这项工作提出了一种方法来预测在每个地理区域开始和结束的所有商品类型的未来货运需求,以及在所有原产地和目的地对之间移动的货运量。按照传统的四步需求预测框架,该过程对应于区域间货运需求的行程生成和行程分布分析。利用宏观经济模型和投入产出模型中的未来经济增长因素,预测了各分析区货运产出量和吸引量,并将其作为给定。然后,应用迭代矩阵平衡法确定所有始发和目的地区域对的估计货运需求。该算法基于2007年以来的全国货运需求数据,以五年增量的形式预测了2010-2050年美国国内的货运需求。考虑到全球经济增长和环境监管的变化,提出了四种不同的情景。这项研究将帮助公共和私营部门的交通规划者和决策者评估未来全国范围内的货运需求如何考虑未来各种全球经济增长和环境政策情景,将影响各种问题,如空气质量和人类健康问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of International Logistics and Trade
Journal of International Logistics and Trade Business, Management and Accounting-Marketing
CiteScore
2.60
自引率
0.00%
发文量
6
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