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Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic

机译：改进对基于方向盘的重型汽车年度平均每日流量的估算

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摘要

The estimation of annual average daily traffic (AADT) is an important parameter collected and maintained by all US departments of transportation. There have been many past research studies that have focused on ways to improve the estimation of AADT. This paper builds upon previous research and compares eight methods, both traditional and cluster-based methodologies, for aggregating monthly adjustment factors for heavy-duty vehicles (US Department of Transportation Federal Highway Administration (FHWA) vehicle classes 4-13). In addition to the direct comparison between the methodologies, the results from the analysis of variance show at the 95% confidence level that the four cluster-based methods produce statistically lower variance and coefficient of variation over the more traditional approaches. In addition to these findings - which are consistent with previous total volume studies - further analysis is performed to compare total heavy-duty monthly adjustment factors, both directions of traffic, with direction-based monthly adjustment factors. The final results show that the variance as well as the coefficient of variation improve on average by 25% when directional aggregate monthly adjustment factors are used instead of total direction.
机译：年度平均每日交通量（AADT）的估算是美国所有交通运输部门收集和维护的重要参数。过去有许多研究集中在改进AADT估算的方法上。本文以先前的研究为基础，比较了八种方法（传统方法和基于群集的方法），以汇总重型车辆的月度调整因子（美国运输部联邦公路管理局（FHWA）车辆类别4-13）。除了方法之间的直接比较之外，方差分析的结果显示，在95％的置信度上，与传统方法相比，四种基于聚类的方法在统计学上产生的方差和变异系数更低。除了这些发现（与以前的总流量研究一致）之外，还进行了进一步的分析，以比较两个方向的总重载月度调整因子和基于方向的月度调整因子。最终结果表明，使用定向合计月度调整因子代替总方向时，方差和变异系数平均提高25％。

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