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.
Highlights
As the Geographic Information System (GIS) has been used for a wide range of transportation applications, positional errors inherent in spatial data become critical for ensuring spatial problem-solving and decision-making
For comparison and evaluation of the analytical- and simulation-based approaches for modeling positional errors and their propagation in Global Positioning System (GPS) and GIS integrated applications, the spatial data employed in this paper are Differential Global Positioning System (DGPS) data points from probe vehicles, and roadway centerline maps (Table 1)
To model positional uncertainties in DGPS data, a stationary GPS data logger (Garmin Etrex Vista HCx) within a Wide Area Augmentation System (WAAS) mode was set up for 17 days (Figure 5), and its logging rate was 2 s, which is identical to the sampling rate of the DGPS datasets from probe vehicles
Summary
As the Geographic Information System (GIS) has been used for a wide range of transportation applications, positional errors inherent in spatial data become critical for ensuring spatial problem-solving and decision-making. Numerous map-matching algorithms have been proposed to correctly integrate GPS data points with a roadway centerline map [4,5,6,7], positional uncertainties still exist in snapped GPS-derived coordinates along roadway centerlines These uncertainties increase and propagate to output products from GIS. Considering the problems above, the following issues should be addressed when transportation agencies utilize GIS and GPS integrated applications: Characterization and propagation of positional uncertainties are not well formulated to determine a positional accuracy requirement for input data and a quality requirement for output information. Uncertainty and sensitivity analysis methods are, developed based upon the error modeling approaches As they have different approaches of formulating characterization and propagation of positional uncertainties, it is essential to compare and evaluate those approaches before implementation to the applications.
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