Global Positioning System Reference
In-Depth Information
before and after applying the algorithm. All FN percentages decrease after executing the
algorithm, independent from the spatial data quality. Average FN percentages computed
with original data present smaller values than data perturbed with 2- and 5-m error before
and after applying the algorithm. For example, FN percentages increase from 22% to 48% for
Polk County after executing the algorithm when introducing a 5-m error. In general, the
percentage of data points that should snap to a roadway centerline increases when there is
larger error in the DGPS data points.
Figure 19 presents the percentages of solved spatial ambiguities by the algorithm before and
after perturbing the DGPS data points with original and simulated random errors (2-and 5-
meter standard deviation) for Columbia County. This figure shows that the percentage of
incorrect snaps solved after applying the algorithm for original Columbia County data are
larger than those computed with perturbed data. On average, the percentage of solved cases
decreases approximately 20% and 40% for data with 2- and 5-meter error for all buffer sizes,
except for the 20-foot buffer. This small buffer is not able to accommodate the spatial
ambiguities that arise with simulated data. Similarly, Portage and Polk counties present a
drop in the percentages of solved data points from approximately 68% and 50% for original
data to approximately 10% and 15%, respectively, for 5 m perturbed data.
5. Summary and conclusions
Transportation applications employ AVL/DGPS technology to collect vehicle positions and
other sensor data. Normally, DGPS data points are associated with roadways by snapping
to the nearest centerline in a GIS environment. The map-matching problem or spatial
ambiguities arise during this association due to errors in DGPS measurements and digital
cartography. Such ambiguities are common at underpasses and converging or diverging
roadways. These can result in DGPS data points being snapped to incorrect roadway
centerlines affecting the calculation of cumulative distance traveled by the vehicles along a
roadway network, or the allocation of non-spatial data collected from vehicle sensors to
incorrect roadways. Thus, this problem propagates to the computation of performance
measures or decision management tools.
This study contributes with the development and implementation of a post-processing
decision-rule map-matching algorithm that resolves many of these spatial ambiguities by
examining the feasibility of paths between pairs of snapped data points. A viable path is the
shortest-distance path between two snapped points that a vehicle can travel, while
following network topology and turn restrictions, at a speed comparable to its average
recorded speed. If a given shortest path is not feasible, then DGPS data points are related to
other roadway centerlines within their buffers and new shortest paths are calculated; or
adjacent DGPS data points are used to determine feasible paths. Examples were presented
to describe the step-by-step process of the map-matching algorithm. Five variables were
studied independently to analyze the performance of the map-matching algorithm. These
variables are buffer size, speed range tolerance, number of consecutive points, temporal
resolution, and positional error in the DGPS data points. Data collection frequency and
DGPS error are variables controlled externally through the data, while buffer size, speed
range, and number of consecutive data points are algorithm parameters are controlled by
the user.
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