METHOD FOR PROVIDING A TRAFFIC PATTERN FOR NAVIGATION MAP DATA AND NAVIGATION MAP DATA

1 . A method for providing a traffic pattern for a road segment of navigation map data on the basis of time series traffic data Y(t), the method comprising:

determining reference time series ρ(t) for the road segment used to approximate the time series traffic data Y(t);

determining a weighted combination of the reference time series ρ(t) by determining weighted coefficients, α, that determine how much a predetermined reference time series contributes to the combination of the reference time series for approximating the time series traffic data Y(t);

approximating the time series traffic data Y(t) by the weighted combination of the reference time series ρ(t); and

linking the determined weighting coefficients α to the road segment of the navigation map data.

2 . The method of claim 1 where the time series traffic data Y(t) and the reference time series ρ(t) contain time-dependent mean velocities of a road segment.

3 . The method of claim 1 further comprising:

determining the number of weighting coefficients used for approximating the time series traffic data using the reference time series ρ(t).

4 . The method of claim 1 where the step of determining reference time series ρ(t):

determining a limited number of representatives of the time series traffic data of the map data.

5 . The method of claim 4 further comprising:

determining the representatives using a clustering method.

6 . The method of claim 4 where the step of determining the weighting coefficients for the representatives comprises:

comparing the approximated time series traffic data for the road segment are compared to the time series traffic data of the road segment in a linear regression method.

7 . The method of claim 1 further comprising:

determining the reference time series using standard basis functions, the time series traffic data being described on the basis of the standard basis functions; and

where the step of determining the weighting coefficients comprises at least carrying out a basis transformation in which the time series traffic data are described using the standard basis functions.

8 . The method of claim 7 where the step of determining the weighting coefficients for the standard basis functions comprises using at least one of the following methods:

Discrete Fourier Transformation (DFT);

Fast Fourier Transformation (FFT);

Discrete Wavelet Transformation (DWT);

Discrete Cosine Transformation (DCT);

Single Value decomposition (SVD); and

Chebychev Polynomials; and

further comprising providing the standard basis functions when using one of the methods listed above.

9 . The method of claim 7 where the step of determining the weighting coefficients α includes using at least one of the following methods:

Piecewise Aggregated Information (PAA); and

Adaptive Piecewise Constant Approximation (APCA).

10 . The method of claim 1 further comprising determining the variance of the weighting coefficients α.

11 . The method of claim 1 further comprising:

determining the number K of reference time series used to approximate the time series traffic data such that a difference between the time series traffic data and approximated traffic data using the weighted reference time series is smaller than a predetermined threshold.

12 . The method of claim 1 where the navigation map data includes a plurality of road segments, the weighting coefficients α for each road segment being stored together with the road segment.

13 . The method of claim 1 where the navigation map data includes a plurality of road segments, the weighting coefficients for each road segment being stored in a coefficient table together with a position information linking the weighting coefficients to one road segment.

14 . The method of claim 1 where the time series traffic data for the road segment includes the time-dependent mean velocities for the road segment.

15 . The method of claim 1 further comprising:

transmitting the weighting coefficients α and the reference time series ρ(t) for the road segment to a storage unit for storing the navigation map data;

storing the weighting coefficients together with the road segment; and

storing the reference time series ρ(t).

16 . A method for determining a traffic pattern for a road segment of navigation map data, the method comprising:

providing time series traffic data Y(t) containing time-dependent mean velocities of the road segment;

determining weighting coefficients α for the road segment;

approximating the time series traffic data Y(t) of the road segment by a weighted combination of reference time series ρ(t) using the weighting coefficients α, where the reference time series are weighted using the weighting coefficients α determining how much a predetermined reference time series ρ(t) contributes to the combination of the reference time series for approximating the time series traffic data Y(t); and

approximating the traffic pattern using the determined weighting coefficients α.

17 . The method of claim 16 further comprising:

calculating a fastest route to a predetermined destination by taking into account the approximated traffic pattern.

18 . The method of claim 16 further comprising:

calculating a route having the lowest energy consumption on the basis of the approximated traffic pattern.

19 . The method of claim 1 further comprising:

determining the variance, the skewness, or the kurtosis for the time series traffic data.

20 . A system for providing a traffic pattern for a road segment on the basis of time series traffic data, the time series traffic data containing time-dependent mean velocities of the road segment, the system comprising:

a reference time series determining unit for determining reference time series for the road segment, the reference time series containing time-dependent mean velocities for the road segment;

a weighting coefficient determining unit for determining weighting coefficients for the road segment used for approximating the time series traffic data by a weighted combination of the reference time series ρ(t), where the reference time series are weighted using weighting coefficients α determining how much a predetermined reference time series ρ(t) contributes to the combination of the reference time series for approximating the time series traffic data Y(t); and

a storage unit for storing the determined weighting coefficients in connection with the road segment.

21 . A computer storage medium comprising:

navigation map data having a plurality of road segments, each road segment being provided in connection with weighting coefficients α, the weighting coefficients α being used for approximating time series traffic data by a weighted combination of reference time series ρ(t), where the reference time series are weighted using the weighting coefficients α determining how much a predetermined reference time series ρ(t) contributes to the combination of the reference time series for approximating the time series traffic data Y(t).

22 . A navigation system for determining a route to a predetermined destination comprising:

map data comprising a plurality of road segments, each road segment being provided in connection with weighting coefficients α(n), the weighting coefficients α(n) being used for approximating time series traffic data by a weighted combination of reference time series ρ(t), where the reference time series are weighted using the weighting coefficients α determining how much a predetermined reference time series ρ(t) contributes to the combination of the reference time series for approximating the time series traffic data Y(t);

traffic data approximation means for approximating the mean velocity for the road segments on the basis of the weighting coefficients; and

route determination means determining a route to a predetermined destination using the mean velocity calculated based on the weighting coefficients.