TRAFFIC CONGESTION PREDICTION METHOD

• US 20130261944A1
• Filed: 12/09/2011
• Published: 10/03/2013
• Est. Priority Date: 12/15/2010
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Koshizen, Takamasa
• Granted Patent: US 8,731,809 B2
• Time in Patent Office: Days
• US Class Current: 701/118
• CPC Class Codes: G08G 1/00 Traffic control systems for road vehicles arrangement of road signs or traffic signals E01F9/00 ; automatic vehicle control B62D
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0133 for classifying traffic situation

Abstract

A traffic congestion prediction method including the steps of: detecting an acceleration of a vehicle; calculating a power spectrum corresponding to a frequency from a frequency analysis of the detected 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 detected inter-vehicle distance by using a distribution estimation method; calculating a minimum value of covariance value from the estimated inter-vehicle distance distribution; estimating a distribution of a group of vehicles ahead from a correlation between the minimum value of covariance value and the maximum gradient value; and performing a real-time traffic congestion prediction based on the distribution of the group of vehicles.

Claims

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.