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Uncertainty and sensitivity assessments of GPS and GIS integrated applications for transportation.

Hong S, Vonderohe AP - Sensors (Basel) (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Korea Institute of Construction Technology, 283 Goyangdae-ro, Ilsanseo-gu, Goyang-si, Gyeonggi-do 411-712, Korea. shong@kict.re.kr.

ABSTRACT
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.

No MeSH data available.


Conceptual view of uncertainty analysis method.
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f7-sensors-14-02683: Conceptual view of uncertainty analysis method.

Mentions: The uncertainty analysis method is designed to estimate the overall quality of output information based on spatial varieties of spatial data. A conceptual view of uncertainty analysis is illustrated in Figure 7, in which three functional components are an error model, a spatial operation, and a computation model. The error model is used to describe distributions of positional error in spatial data and to propagate the error through spatial processes so the quality of output information can be estimated. The spatial operation, referred to as a linear transformation, is used to associate GPS data points with a roadway centerline map. The computational model generates output information from the applications. A distribution of output information, finally obtained from the uncertainty analysis method, has two essential components: (1) a numerical value and (2) a degree of uncertainty. The numerical value is the best estimate of output information from given qualities of input data, and the degree of uncertainty is a quantitative indication of the reliability of the result.


Uncertainty and sensitivity assessments of GPS and GIS integrated applications for transportation.

Hong S, Vonderohe AP - Sensors (Basel) (2014)

Conceptual view of uncertainty analysis method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958225&req=5

f7-sensors-14-02683: Conceptual view of uncertainty analysis method.
Mentions: The uncertainty analysis method is designed to estimate the overall quality of output information based on spatial varieties of spatial data. A conceptual view of uncertainty analysis is illustrated in Figure 7, in which three functional components are an error model, a spatial operation, and a computation model. The error model is used to describe distributions of positional error in spatial data and to propagate the error through spatial processes so the quality of output information can be estimated. The spatial operation, referred to as a linear transformation, is used to associate GPS data points with a roadway centerline map. The computational model generates output information from the applications. A distribution of output information, finally obtained from the uncertainty analysis method, has two essential components: (1) a numerical value and (2) a degree of uncertainty. The numerical value is the best estimate of output information from given qualities of input data, and the degree of uncertainty is a quantitative indication of the reliability of the result.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Korea Institute of Construction Technology, 283 Goyangdae-ro, Ilsanseo-gu, Goyang-si, Gyeonggi-do 411-712, Korea. shong@kict.re.kr.

ABSTRACT
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.

No MeSH data available.