Global Positioning System Reference
In-Depth Information
a period of 30 minutes proving the feasibility of the approach for lane-level applications.
The authors mention that outlier removal, multipath effect mitigation, and additional
method validation are tasks that need to be addressed in the future.
2.6 Personal navigation assistants and map-matching
White et al. (2000) discuss solutions to the map-matching problem for personal navigation
assistants (PNA). Four different map-matching algorithms were implemented and tested: 1)
use of minimum distance (point-to-curve), 2) comparison of heading information with arc
and trajectory, 3) use of topology to select roads that are reachable from the current road,
and 4) construction of piece-wise linear curves from different paths, followed by comparison
of them to centerline curves using points (curve-to-curve matching). The authors conclude
that these algorithms performed better when the distance between the GPS point and the
closest road is small and that correct matches tend to occur at greater speeds on straight
roadways.
Freitas et al. (2009) explain the necessity of map-matching algorithms to correctly locate GPS
positions on a map when using PNA, particularly for dynamic route guidance systems. The
authors describe an approach to update digital maps through the use of GPS points, in order
to identify map incongruence. The proposed system was designed as a prototype and lacks
of extensive testing, however, it correctly processes and implements methods for map-
matching and detecting discrepancies between the real network and digital maps.
2.7 Topological network-based algorithms
Taylor et al. (2001) describe an algorithm called “Road Reduction Filter (RRF)” that uses
differential corrections and height aids. RRF identifies all possible roadway candidates
while systematically removing incorrect ones. RRF is improved by using shortest path
network analysis and drive restriction information. A shortest path network routine
calculates the distance through the roadway network from a vehicle's previous position to
each potential present position offered by the algorithm. The drive restriction information
routine selects roadways using direction and access information.
Greenfeld (2002) presents a map-matching procedure that consists of two algorithms. One
algorithm assesses similarity between characteristics of the roadway network and the
positioning pattern of the vehicle. The second algorithm performs topological analysis and
applies a weighting scheme to match each GPS data point to the roadway network. The
highest weighted score determines the most likely candidate for a correct match. The author
indicates that further research is needed to determine the correct position of the vehicle
along a roadway segment and to verify the accuracy performance of the algorithms.
Doherty et al. (2000) studied an algorithm that automatically matches GPS data points to
roadway segments along a network. First, the algorithm joins GPS points to create a linear
object forming the vehicle's track. Subsequently, it creates a buffer zone around the linear
object, and then identifies all the roadways that are totally included within the buffer to
select the correct one.
Marchal et al. (2005) presents an innovative map-matching algorithm that relies on GPS
measurements and network topology. The algorithm consists of maintaining a set of
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