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Green Routing Fuel Saving Opportunity Assessment: A Case Study Using Large-Scale Real-World Travel Data

机译:绿色路由燃料保存机会评估:使用大规模现实世界旅行数据的案例研究

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New technologies such as connected and automated vehicles have attracted more and more research attention for their potential to improve the energy efficiency and environmental impact of current transportation systems. Green routing is one such connected vehicle strategy under which drivers receive information about the most fuel-efficient route before departing for a given destination. This paper introduces an evaluation framework for estimating the benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption on alternate routes that could be taken between two locations and comparing these to the estimated fuel consumption of the actual route taken. A route-based fuel consumption estimation model that considers road traffic conditions, functional class, and grade is proposed and used in the framework. A study using a large-scale, high-resolution data set from the California Household Travel Survey indicates that 31% of actual routes have fuel savings potential, and among these routes the cumulative fuel savings could reach 12%. Alternately calculating the potential fuel savings relative to the full set of actual routes (including those that already follow the greenest route recommendation), the potential savings relative to the overall estimated fuel consumption would be 4.5%. Notably, two thirds of the fuel savings occur on green routes that save both fuel and time relative to the original actual routes. The remaining third would be subject to weighing the potential fuel savings against required increases in travel time for the recommended green route.
机译:连接和自动化车辆等新技术吸引了越来越多的研究潜力,以提高当前运输系统的能效和环境影响。绿色路由是一种这样的连通的车辆策略,驾驶员在出发前接收有关最省油路线的信息,然后才出发给定目的地。本文介绍了一种评估框架,用于估计基于大规模的现实世界旅行数据的绿色路线的好处。该框架具有通过估计可以在两个地点之间采取的替代路线的燃料消耗来量化燃料节省的能力,并将其与所采取的实际路线的估计燃料消耗进行比较。提出了一种基于路线的燃料消耗估计模型,用于在框架中使用和使用函数类和等级。来自加州家庭旅行调查的大规模的高分辨率数据集的研究表明,31%的实际路线具有燃料节省潜力,并且在这些路线中,累计燃料节省可能达到12%。交替地计算相对于全套实际路线的潜在燃料节省(包括已经遵循最近的路线推荐),相对于整体估计的燃料消耗的潜在节约将是4.5%。值得注意的是,在绿色路线上出现三分之二的燃料节省,以节省燃料和时间相对于原始实际路线。剩下的第三个将受到权衡推荐绿色路线的旅行时间所需的潜在燃料节省。

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