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Explaining heavy vehicle demand evolution in toll roads: a dynamic panel data approach in Spain

机译:解释收费公路上重型车辆需求的演变:西班牙的动态面板数据方法

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摘要

Improving the knowledge of demand evolution over time is a key aspect in the evaluation of transport policies and in forecasting future investment needs, specially critical for the case of toll roads. However, literature regarding demand elasticity estimates in toll roads is sparse and leaves some important aspects to be analyzed in greater detail. In particular, previous research does not focus on heavy vehicle demand, so that the specific behavioral patterns of this traffic segment are not taken into account. Furthermore, GDP is the main socioeconomic variable most commonly chosen to explain road freight traffic growth over time. This paper seeks to determine the variables that better explain the evolution of heavy vehicle demand in toll roads over time. To that end, we present a dynamic panel data methodology aimed at identifying the key socioeconomic variables that explain the behavior of road freight traffic throughout the years. The results show that, despite the usual practice, GDP may not constitute a suitable explanatory variable for heavy vehicle demand. Rather, considering only the GDP of those sectors with a high impact on transport demand, such as construction or industry, leads to more consistent results. The methodology is applied to Spanish toll roads for the 1990-2011 period. This is an interesting case in the international context, as road freight demand has experienced an even greater reduction in Spain than elsewhere, since the beginning of the economic crisis.
机译:在评估运输政策和预测未来投资需求时,尤其是对于收费公路而言,至关重要的是,不断提高需求随时间变化的知识是一个关键方面。但是,关于收费公路需求弹性估算的文献很少,并且有一些重要方面需要更详细地分析。尤其是,先前的研究并未将重点放在重型车辆需求上,因此未考虑此交通路段的特定行为模式。此外,GDP是主要的社会经济变量,最常被用来解释公路货运量随时间的增长。本文试图确定能够更好地解释收费公路随时间推移重型车辆需求变化的变量。为此,我们提出了一种动态面板数据方法,旨在确定可解释多年来道路货运行为的关键社会经济变量。结果表明,尽管采用了通常的做法,GDP可能无法构成重型车辆需求的合适解释变量。相反,仅考虑那些对运输需求有重大影响的行业的GDP,例如建筑或工业,会产生更加一致的结果。该方法适用于1990-2011年期间的西班牙收费公路。在国际背景下,这是一个有趣的案例,因为自经济危机开始以来,西班牙的公路货运需求比其他任何地方都经历了更大的减少。

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