Some map matching algorithm for personal navigation assistants by cuiliqing

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```									Some map matching algorithm for
personal navigation assistants
Paper by Christopher White,
David Bernstein, Alain Kornhauser

Slides and Presentation by
Alireza Vahdatpour
Outline
•   Background
•   Problem statement
•   Literature review
•   Four solutions
•   Evaluation
•   Results
Background
• Personal Navigation Assistants
▫ Reconcile the user’s location with the underlying
map
• Users location coming from GPS (inacurate)
• Underlying map
▫ Could be inaccurate in some cases
▫ Accurate map may not be available
 Limited device memory
 Security issue
Problem Statement
• Map matching algorithm to reconcile inaccurate
locational data with an inaccurate map
• A person/vehicle is moving along a finite set of
streets
• We are provided by an estimate of his location in
times T0, …, Tt (denoted by Pt)
• Goal is to determine the set of streets that
contain Pt
Problem Statement
• Street system is usually represented by a
network
▫ Network consists of a set of curves (arc)
▫ Arcs are piece-wise linear
 Arcs can completely represented by a sequence of
nodes A= (A1, A2, …., An-1)
• The goal is to match the estimated points Pt with
and arc A
Problem Statement
• Example
Literature review
• Map matching as a search problem
▫ Match Pt to the closest arc
• Algorithms to find the closest match are called
range query
• Pros:
▫ Fast, easy to implement
• Cons:
▫ Inaccurate
Literature review
• Example
Literature review
• Map matching as statistical estimation
▫ Attempt to fit a curve to the sequence of estimated
locations
▫ The curve is considered to lie on the network
• Perfect if the model of the motion is simple
(straight lines)
• Not easy to model motions dictated by networks
(streets)
Literature review
• Example
Algorithm 1
1. Find nodes that are close to the GPS estimated
locations
2. Find the set of arcs that are incident to these
nodes
3. Find the closest of these arcs and project the
point onto that arc
Algorithm 1
• Two cases:
Algorithm 1
• Cons
▫ Do not use historical data

▫ Unstable
Algorithm 2
• Algorithm 1 + Heading information
• Example benefit
▫ Will not match a point to an arc that is
perpendicular to the current direction of travel
Algorithm 3
• Algorithm 2 + topological information
• Uses connectivity information to locate
candidate arcs for matching (in addition to
range query)

• Cons:
▫ One bad match can lead to a sequence of bad
matches
Algorithm 4
• Algorithm 3 + Curve to curve matching
1. Locates candidate nodes the same way as
algorithm 3
2. Constructs piece-wise linear curves from the set
of paths originating from that node
3. Construct piece-wise linear curve from GPS
points
4. Calculate distance between this curve ad
network curves
5. Return the closest match
Algorithm 4
• Example
Evaluation
• Driving a vehicle ,utilized with GPS receiver, in
Mercer county, New Jersey
▫ Limited to 4 routes
Results
• Accuracy of each algorithm vs. route
Results
• Comparison of Algorithm 1 and Algorithm 2
Results
• All algorithms work better on longer routes
• All algorithms work better with “better GPS
points”
• Higher speed is better

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