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Driving demand: What can gasoline refueling patterns tell us about planning an alternative fuel network?

机译:推动需求:汽油加油模式可以告诉我们有关规划替代燃料网络的哪些信息?

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Knowing which variables predict gasoline demand can help inform which are useful in determining future demand at an alternative fuel station such as those for bio-fuels, natural gas, hydrogen, or fast-charge electricity. This study explores the spatial distribution of demand by comparing two main classes of variables: those without a displacement component such as population in a census block group, and those that imply a vector or directionality such as vehicle kilometers traveled. The spatial distribution of these variables is compared to the spatial distribution of demand for gasoline using regression. Many models examining the transition from gasoline to an alternative fuel assume a demand pattern for fuel a priori in order to estimate potential demand at a future alternative fuel station. This paper studies not the models themselves but the variables used to predict demand. The results indicate that vehicle kilometers traveled (VKT) is the best variable to pinpoint where demand for fuel will occur. However, travel to the central business district of the metropolitan area does not appear to translate into demand for fuel in proportion to the VKT. While gasoline demand does appear to vary with population as well, the location of demand is much less specific than that predicted by VKT. The results also suggest that the route between home and the nearest freeway entrance may help predict a large portion of refueling and merits further investigation. This possible tendency can be used to create a new variable called "population-traffic" which appears to describe the spatial distribution of demand well. The good performance of this independent variable in regressions suggests that stations sited along the freeway may serve customers needs and provide the necessary concentration of demand for initial alternative fuel stations. A practical application of this work would be to help define refueling demand patterns in a rollout of alternative fueled vehicles in a neighborhood or town.
机译:知道哪些变量可以预测汽油需求,可以帮助您确定哪些变量对确定替代燃料站(例如生物燃料,天然气,氢气或快速充电电力站)的未来需求有用。这项研究通过比较两个主要类别的变量来探索需求的空间分布:一类变量没有位移分量,例如人口普查区块组中的人口;另一类变量是矢量或方向性,例如行驶的车辆公里数。使用回归将这些变量的空间分布与汽油需求的空间分布进行比较。检查从汽油到替代燃料的过渡的许多模型都假定先验燃料的需求模式,以便估算未来替代燃料站的潜在需求。本文不是研究模型本身,而是研究用于预测需求的变量。结果表明,行驶的公里数(VKT)是确定燃料需求发生位置的最佳变量。但是,去大都市的中央商务区旅行似乎并没有转化为对燃料的需求,与VKT成比例。尽管汽油需求的确也随着人口的变化而变化,但需求的位置比VKT预测的要明确得多。结果还表明,从家到最近的高速公路入口之间的路线可能有助于预测大部分加油情况,值得进一步研究。这种可能的趋势可以用来创建一个称为“人口流量”的新变量,该变量似乎很好地描述了需求的空间分布。该自变量在回归中的良好表现表明,沿高速公路设置的加油站可满足客户需求,并为初始替代加油站提供必要的需求集中。这项工作的实际应用将是帮助在邻里或城镇推出替代燃料车辆时定义加油需求模式。

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