A Decision-Rule Topological Map-Matching Algorithm with Multiple Spatial Data - Global Navigation Satellite Systems: Signal,Theory and Applications - page 237

The results of this study indicate that the success of the map-matching algorithm in solving
spatial ambiguities depends on not only by the variables employed by the algorithm, but
also by the sampling interval and the quality of the spatial measurements and roadway map
scale. If lower spatial data qualities and less frequent sampling intervals are used, then the
algorithm requires larger buffers and speed ranges to obtain best results. On the other hand,
if GPS data points collected more frequently are snapped to higher accuracy maps, such as
the Columbia County case, then larger percentages of incorrect snaps are solved and smaller
buffer sizes are adequate. By increasing the number of consecutive data points, a larger
number of spatial ambiguities are solved, particularly when alternative roadway centerlines
are equally viable, and FN percentages are reduced since more combinations are examined
between pairs of snapped DGPS data points. However, no significant variations in the
solved results for Polk County are apparent as the number of consecutive data points
increases since lower spatial data accuracies were used in this county. Table 2 presents the
best and worst variable values encountered when solving incorrect snaps after applying the
map-matching algorithm by county. This table indicates that larger speed range values, and
numbers of consecutive points provide better results in maximizing solved cases. Stable
percentage values are reached as both speed range and number of consecutive points reach
certain values. While small speed ranges tend to reject tested paths, larger speed ranges
accept most of these paths without improving the performance of the algorithm. Similarly,
larger percentages of solved cases are obtained as the number of consecutive points
increases since additional combinations between pairs of snapped data points are examined.
Overall, higher parameter values yield better results as data are collected less frequently and
snapped to lower quality roadway maps.

Buffer Size (ft) Speed Range (mi/hr) Number of Consecutive Points

County

Best Worst Best Worst Best Worst

Columbia 30 ≥50 35 5 8 3

Portage 50 20 ≥25 5 8 3

Polk 40 20 ≥15 5 ≥3 ≥3

Table 2. Best and Worst Variable Values for Solved Cases by County

Introducing positional error in the DGPS data points decreases the percentage of solved
incorrect snaps and total number of snapped data points obtained before and after applying
the algorithm. As the positional error increments from 2 to 5 meters in standard deviation,
the percentage of solved cases decrease and FN percentages increase for all counties. Thus,
larger buffer sizes and speed ranges are needed for lower quality data. Future research is
required to explore these parameter values against additional spatial data qualities derived
from multiple ITS applications. Further research may involve online implementation of the
map-matching algorithm, in which spatial ambiguities are solved as GPS measurements are
collected in real-time.

6. References
Blazquez, C., & Vonderohe, A. (2005). Simple Map-Matching Algorithm Applied to
Intelligent Winter Maintenance Vehicle Data. Journal of Transportation Research
Board , Vol. 1935, pp. 68-76.