Global Positioning System Reference
In-Depth Information
Yet another map-matching algorithm based on fuzzy theory is proposed by Guo and Luo
(2009). First, the algorithm compares the similarity degree between the trajectory curve of
the road and all candidate roads to identify the road on which a vehicle is traveling.
Subsequently, fuzzy preference relations are adopted to perform a multi-criteria decision
and a look-ahead technique is employed to improve the matching accuracy. The algorithm
requires testing and analysis with GPS data in addition to cell phone positions.
2.4 Kalman filter approach
There has been abundant research on application of Kalman filters in combination with GPS
and dead-reckoning signals to solve spatial mismatches. This integrated technology
improves positioning accuracy by estimating white noise and error in the GPS and then
correcting the vehicle's position (Jo et al., 1996; W. Kim et al., 2000; Zhao et al., 2003). For
example, Quddus et al. (2003) present a general map-matching algorithm that integrates
GPS and dead-reckoning sensor data (position, velocity, and time) through an extended
Kalman filter and uses them as input to improve performance of the algorithm. The physical
location of the vehicle on a roadway link is determined empirically from the weighted
averages of two state determinations of the vehicle position based on topological
information and external sensors.
Yang et al. (2003) present an improved map-matching algorithm that employs Kalman
filtering to filter unreasonable GPS data and the Dempster-Shafer (D-S) theory to correctly
snap GPS vehicle coordinates to the digital roadway map. The D-S theory allows explicit
representation of ignorance and combination of evidence and operates with a smaller set of
uncertainties. Although the authors report satisfying results, they suggest additional
research to verify the accurate performance of the algorithm.
Nassreddine et al. (2009) describe a map-matching method based on D-S theory and interval
analysis to compute accurate vehicle positions from an initial estimated position on a digital
road network. The authors state that the proposed technique proves to be successful at
junctions and parallel roads. However, real world data needs to be examined in addition to
simulated data.
2.5 Particle filtering and map-matching
Particle filtering, based on a stochastic process, is another approach to the map-matching
problem. Particle filters are recursive implementations of Monte Carlo-based statistical
signal processing (Crisan & Doucet, 2002). Gustafsson et al. (2002) evaluate in real time a
map-matching particle filter used to match a vehicle's horizontal driven path to a digital
roadway map. They conclude that the particle filter converged relatively rapid after a few
iterations of the algorithm. The challenge of this map-matching technique is to find
nonlinear relations and non-Gaussian sensor models that provide the most information
about the vehicle's position. The authors assert that research is still needed to seek a reliable
way to detect divergence and to restart the filter.
Toledo-Moreo et al. (2009) present a multiple-hypothesis particle-filter based algorithm to
solve the map-matching problem with integrity provision at the lane level. The proposed
system joins measurements from a GPS receiver, an odometer, and a gyroscope along with
road information in digital maps. A set of six experiments were conducted with real data for
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