Global Positioning System Reference
In-Depth Information
candidate paths as GPS data are processed and computing matching scores for each path.
The path with the best score represents the correct vehicle route. According to the authors
further research is needed to improve the robustness of the algorithm.
Yet another topological map-matching algorithm is proposed by Wang and Yang (2009). The
algorithm presents high accuracy and solves spatial ambiguities in complex roadway
networks, specifically near intersections and parallel roads. Nevertheless, the topological
algorithm was tested on only four road intersections with a 2-second sampling interval of
GPS measurements.
Velaga et al (2009) describe an enhanced weight-based topological map-matching algorithm
for ITS. The algorithm was tested with real data under different operational environments.
However, the optimal algorithmic weights for different factors such as heading, proximity,
connectivity, and turn-restriction still need to be estimated with a range of real-world field
data from different road environments.
Blazquez and Vonderohe (2005) propose a topological map-matching algorithm that
resolves spatial ambiguities that occur with intelligent winter maintenance vehicle data
collected in Wisconsin. The algorithm computes shortest paths between snapped GPS data
points using network topology and turn restrictions. If similarity exists between calculated
and recorded vehicle speed values, then the path is feasible and snapped GPS locations are
correct. If the path is not viable, then GPS data points are snapped to alternative roadway
centerlines, shortest paths are recalculated, and speeds are again compared. The authors
studied this problem further and published the effects of controlling parameters on the
performance of the map-matching algorithm (Blazquez & Vonderohe, 2009). The current
chapter discusses and describes in more detail the performance analysis of this map-
matching algorithm.
2.8 Other map-matching algorithms
According to Zhao (1997), many pattern recognition methods (e.g., neural network) could be
used for map-matching. Neural networks are dynamic systems that consist of many
interconnected layered nodes (neurons). These networks need to be trained to arrange the
layers and interconnections to model real-world applications. Other pattern recognition
methods can be used to work with positioning sensors such as GPS. The underlying
principle of these methods is that the digital map is used to filter out vehicle sensor errors
and to determine the best position.
Schlingelhof et al. (2008) present a two-dimension map-matching algorithm based on a lane-
level model. The output of this algorithm is the road segment identification number, the
relative vehicle position along this segment, and the relative transversal vehicle position
with respect to one of the border lines. The road selection algorithm consists of extracting
candidate segments, computing positioning solution residuals, and selecting the most likely
segment. The authors state that the first results obtained with real measurements are
encouraging. However, these should be generalized to enhanced maps.
Li et al. (2005) present a novel map-matching method using least-squares position
estimation, and digital mapping and height data to augment the vehicle position calculation.
Experiment results indicate that combining the algorithm with height aiding improves the
vehicle position accuracy when the number of visible satellites is reduced.
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