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.


Patrol section map in Columbia County, WI, USA.
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f12-sensors-14-02683: Patrol section map in Columbia County, WI, USA.

Mentions: Spatial data covers a county roadway and an interstate highway in Columbia County, WI, USA (Figure 12). The coordinate system is the Columbia County Coordinate System based on the North American Datum 83 (NAD 83) and the measurement units are U.S. survey feet. Reference and test roadway spatial databases depict roadway centerlines with different nominal scales. The reference map, used in the previous section, is employed for modeling and simulating positional errors in the commercial and public maps shown in Table 3. The commercial map has 5 m accuracy in almost all areas. However, for the public map, roadway centerlines are available in limited areas and represented with a 1:24,000 nominal scale. The commercial map represents a limited resource but shows accurate roadway centerlines, while the public map represents an open source but coarsely depicts roadway centerlines. In the application, the functional class, attribute, and topology in the roadway spatial database are utilized for calculating performance measures with DGPS data from winter maintenance vehicles. DGPS data were collected over the 2001–2002 and 2002–2003 winter seasons, and it is assumed that the DGPS datasets have identical properties of positional uncertainty shown in Table 1.


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

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

Patrol section map in Columbia County, WI, USA.
© Copyright Policy
Related In: Results  -  Collection

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

f12-sensors-14-02683: Patrol section map in Columbia County, WI, USA.
Mentions: Spatial data covers a county roadway and an interstate highway in Columbia County, WI, USA (Figure 12). The coordinate system is the Columbia County Coordinate System based on the North American Datum 83 (NAD 83) and the measurement units are U.S. survey feet. Reference and test roadway spatial databases depict roadway centerlines with different nominal scales. The reference map, used in the previous section, is employed for modeling and simulating positional errors in the commercial and public maps shown in Table 3. The commercial map has 5 m accuracy in almost all areas. However, for the public map, roadway centerlines are available in limited areas and represented with a 1:24,000 nominal scale. The commercial map represents a limited resource but shows accurate roadway centerlines, while the public map represents an open source but coarsely depicts roadway centerlines. In the application, the functional class, attribute, and topology in the roadway spatial database are utilized for calculating performance measures with DGPS data from winter maintenance vehicles. DGPS data were collected over the 2001–2002 and 2002–2003 winter seasons, and it is assumed that the DGPS datasets have identical properties of positional uncertainty shown in Table 1.

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.