Limits...
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.


DGPS data points in patrol sections. (a) Patrol Section 1; (b) Patrol Section 3; and (c) Patrol Section 4.
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f13-sensors-14-02683: DGPS data points in patrol sections. (a) Patrol Section 1; (b) Patrol Section 3; and (c) Patrol Section 4.

Mentions: The sensitivity analysis aims to characterize sensitivity of output information to positional uncertainties in input data, considering complexity and curvilinearity of roadways. The sensitivity analysis method was applied to roadway centerline maps and DGPS data points in Patrol Section 1, Patrol Section 3, and Patrol Section 4, which represent the straightaway roadway, the roadway including ramps, and the curvilinear roadway, respectively (Figure 13).


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

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

DGPS data points in patrol sections. (a) Patrol Section 1; (b) Patrol Section 3; and (c) Patrol Section 4.
© Copyright Policy
Related In: Results  -  Collection

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

f13-sensors-14-02683: DGPS data points in patrol sections. (a) Patrol Section 1; (b) Patrol Section 3; and (c) Patrol Section 4.
Mentions: The sensitivity analysis aims to characterize sensitivity of output information to positional uncertainties in input data, considering complexity and curvilinearity of roadways. The sensitivity analysis method was applied to roadway centerline maps and DGPS data points in Patrol Section 1, Patrol Section 3, and Patrol Section 4, which represent the straightaway roadway, the roadway including ramps, and the curvilinear roadway, respectively (Figure 13).

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.