How to use map matching components

The Location Library implements a map matcher based on a
Hidden Markov Model (HMM).
This algorithm is key to updating attributes of a map, for example when
performing Traffic Sign Recognition or Local Hazard Warning.

For a description of how to construct the HMM, see the Microsoft article
Hidden Markov Map Matching Through Noise and Sparseness.
The map matcher works by determining the path through an HMM with the highest
probability.

The HMM contains states and transitions. The states of the HMM represent the
position on a road segment at which the vehicle may have been at a specific
point in time. The observations are position measurements that may contain some
degree of noise and other information such as the heading.

This means that the emission probability of an observation in a specified state
is the probability that the measurement was taken while the vehicle was at the
specified position.

The transition probability from state s1 at time t1 to state s2 at time t2 is
the probability that the vehicle moved from position p1 at time t1 to position
p2 at time t2.

Framing the map matching problem in these terms naturally leads to three
customization points:

• The
  candidate states
  to consider
• The
  emission probability
  for an observation in a specified state
• The
  transition probability
  from one state to another