Global Positioning System Reference
In-Depth Information
regarded as a solved case. If a data point is snapped incorrectly before applying the
algorithm and it is snapped incorrectly after applying the algorithm, then the spatial
mismatch is not solved. If this occurs, then some neighboring data points may be left
incorrectly snapped. Note that FN, FP, and no solution are not included in the solved and
not solved case analysis.
In the following section of this chapter, FN data points are minimized and solved spatial
mismatches are maximized after applying the algorithm. Although FP and no solution cases
occur due to spatial database incompleteness, they amount to less than 0.5% of the total
number of data points examined in this study. Therefore, these two cases were not taken
into account in the analysis.
4.3 Analysis of the impact of variables on the performance of the map-matching
algorithm
This section examines each algorithmic variable independently to determine its effect on the
performance of the map-matching algorithm. These variables are classified into two groups.
One group consists of parameters controlled by the user (i.e., buffer size, speed range,
number of consecutive data points) and the other group comprises parameters controlled
through the data (i.e., temporal resolution and DGPS error).
4.3.1 Buffer size
The appropriate buffer size employed during the snapping process when solving spatial
ambiguities depends on the quality and geometry of the spatial data. This proximity
parameter used to select roadway centerlines around data points is critical for solving the
map-matching problem and, therefore, for the success of the algorithm. Buffers that are
overly small in size might not include any roadways. While extremely large buffers make
the algorithm less efficient since it needs to examine more roadways, many of which will not
be correct.
Roadways are typically represented by centerlines that do not account for lane widths.
Therefore, data points will almost always appear offset some distance from roadway
centerlines in addition to being affected by errors in the DGPS measurements and digital
roadway maps (Wolf & Ghilani, 1997). Hence, the buffer size parameter was tested at 10-ft
increments from 20 ft to 60 ft for data collected in Columbia and Portage Counties, and at
20-ft increments from 20 to 100 ft for data collected in Polk County. The latter is due to the
smaller scale of the Polk County roadway centerline map. These buffer size values were
predetermined through the computation of average distance percentages between the data
points and roadway centerlines. As different buffer sizes were analyzed and tested against
the map-matching algorithm, the speed range tolerance and number of consecutive data
points were maintained constant with values 25 mi/h and 5, respectively.
Figure 12 shows a chart with the average percentages of FN before and after applying the
algorithm, as the buffer size varies for Columbia, Portage, and Polk County. This figure
indicates that lower FN percentages are obtained after applying the algorithm for all three
counties. Portage and Polk counties present the largest decrease of FN percentages with an
average difference of 20% before and after executing the algorithm. Overall, average
percentages of FN data points diminish as the buffer size increases since more data points
are snapped to roadway centerlines.
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