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Uncertainty and Sensitivity Assessments of GPS and GIS Integrated Applications for Transportation

机译:GPS和GIS集成应用在交通运输中的不确定度和灵敏度评估

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摘要

Uncertainty and sensitivity analysis methods are introduced, concerning the quality of spatial data as well as that of output information from Global Positioning System (GPS) and Geographic Information System (GIS) integrated applications for transportation. In the methods, an error model and an error propagation method form a basis for formulating characterization and propagation of uncertainties. They are developed in two distinct approaches: analytical and simulation. Thus, an initial evaluation is performed to compare and examine uncertainty estimations from the analytical and simulation approaches. The evaluation results show that estimated ranges of output information from the analytical and simulation approaches are compatible, but the simulation approach rather than the analytical approach is preferred for uncertainty and sensitivity analyses, due to its flexibility and capability to realize positional errors in both input data. Therefore, in a case study, uncertainty and sensitivity analyses based upon the simulation approach is conducted on a winter maintenance application. The sensitivity analysis is used to determine optimum input data qualities, and the uncertainty analysis is then applied to estimate overall qualities of output information from the application. The analysis results show that output information from the non-distance-based computation model is not sensitive to positional uncertainties in input data. However, for the distance-based computational model, output information has a different magnitude of uncertainties, depending on position uncertainties in input data.
机译:介绍了不确定性和敏感性分析方法,涉及空间数据的质量以及来自全球定位系统(GPS)和地理信息系统(GIS)集成运输应用程序的输出信息的质量。在这些方法中,误差模型和误差传播方法构成了确定特征和不确定性传播的基础。它们以两种不同的方法开发:分析和仿真。因此,执行初始评估以比较和检查来自分析和仿真方法的不确定性估计。评估结果表明,从分析和仿真方法估计的输出信息范围是兼容的,但是对于不确定性和灵敏度分析,仿真方法而不是分析方法更可取,因为它具有灵活性和能够在两个输入数据中实现位置误差的能力。因此,在案例研究中,基于模拟方法的不确定性和敏感性分析是在冬季维护应用程序上进行的。灵敏度分析用于确定最佳输入数据质量,然后将不确定性分析应用于从应用程序估计输出信息的整体质量。分析结果表明,基于非距离的计算模型的输出信息对输入数据中的位置不确定性不敏感。但是,对于基于距离的计算模型,取决于输入数据中的位置不确定性,输出信息具有不同的不确定性大小。

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