Traffic congestion prediction method

1. A traffic congestion prediction method comprising the steps of:

detecting an acceleration of a vehicle;

calculating a power spectrum corresponding to a frequency from a frequency analysis of the acceleration;

calculating a simple linear regression line of the power spectrum and calculating a maximum value of an amount of change in a gradient of the simple linear regression line in a predetermined frequency range as a maximum gradient value;

detecting an inter-vehicle distance between the vehicle and a vehicle ahead;

estimating an inter-vehicle distance distribution from the inter-vehicle distance by using a distribution estimation method;

calculating a minimum value of covariance from the inter-vehicle distance distribution;

estimating a distribution of a group of vehicles ahead from a correlation between the minimum value of covariance and the maximum gradient value; and

performing a traffic congestion prediction based on the distribution of the group of vehicles.

2. The traffic congestion prediction method according to claim 1 , wherein the step of performing the traffic congestion prediction includes specifying a region where variation in the vehicle group is large and a region where variation in the vehicle group is small in the vehicle group distribution and determining whether or not there is a boundary region between the two regions.

3. The traffic congestion prediction method according to claim 2 , wherein the boundary region corresponds to a critical region between a free-flow region where a probability that traffic congestion occurs is low and a mixed-flow region where braking and acceleration of a vehicle are mixed.

4. The traffic congestion prediction method according to claim 1 , wherein the step of estimating the distribution of the group of vehicles includes creating a correlation map between a logarithm of the minimum value of the covariance and a logarithm of the maximum gradient value.

5. A traffic congestion prediction device comprising:

a vehicle speed sensor configured to detect an acceleration of a vehicle; and

a processing unit configured to

calculate a power spectrum corresponding to a frequency from a frequency analysis of the acceleration;

calculate a simple linear regression line of the power spectrum and calculating a maximum value of an amount of change in a gradient of the simple linear regression line in a predetermined frequency range as a maximum gradient value;

detect an inter-vehicle distance between the vehicle and a vehicle ahead;

estimate an inter-vehicle distance distribution from the inter-vehicle distance by using a distribution estimation method;

calculate a minimum value of covariance from the inter-vehicle distance distribution;

estimate a distribution of a group of vehicles ahead from a correlation between the minimum value of covariance and the maximum gradient value; and

perform a traffic congestion prediction based on the distribution of the group of vehicles.

6. The traffic congestion prediction device according to claim 5 , wherein the traffic congestion prediction includes specifying a region where variation in the vehicle group is large and a region where variation in the vehicle group is small in the vehicle group distribution and determining whether or not there is a boundary region between the two regions.

7. The traffic congestion prediction device according to claim 6 , wherein the boundary region corresponds to a critical region between a free-flow region where a probability that traffic congestion occurs is low and a mixed-flow region where braking and acceleration of a vehicle are mixed.

8. The traffic congestion prediction device according to claim 5 , wherein the processing unit is configured to estimate the distribution of the group of vehicles by creating a correlation map between a logarithm of the minimum value of the covariance and a logarithm of the maximum gradient value.

9. A traffic congestion prediction device comprising:

a vehicle speed sensor for detecting an acceleration of a vehicle; and

a processing unit comprising

means for calculating a power spectrum corresponding to a frequency from a frequency analysis of the acceleration,

means for calculating a simple linear regression line of the power spectrum and calculating a maximum value of an amount of change in a gradient of the simple linear regression line in a predetermined frequency range as a maximum gradient value,

means for detecting an inter-vehicle distance between the vehicle and a vehicle ahead,

means for estimating an inter-vehicle distance distribution from the inter-vehicle distance by using a distribution estimation method,

means for calculating a minimum value of covariance from the inter-vehicle distance distribution,

means for estimating a distribution of a group of vehicles ahead from a correlation between the minimum value of covariance and the maximum gradient value, and

means for performing a traffic congestion prediction based on the distribution of the group of vehicles.

10. The traffic congestion prediction device according to claim 9 , wherein the traffic congestion prediction includes specifying a region where variation in the vehicle group is large and a region where variation in the vehicle group is small in the vehicle group distribution and determining whether or not there is a boundary region between the two regions.

11. The traffic congestion prediction device according to claim 10 , wherein the boundary region corresponds to a critical region between a free-flow region where a probability that traffic congestion occurs is low and a mixed-flow region where braking and acceleration of a vehicle are mixed.

12. The traffic congestion prediction device according to claim 9 , wherein the processing unit comprises means for estimating the distribution of the group of vehicles by creating a correlation map between a logarithm of the minimum value of the covariance and a logarithm of the maximum gradient value.