METHODS FOR ESTIMATING ANNUAL AVERAGE DAILY TRAFFIC

• US 20130110384A1
• Filed: 06/14/2012
• Published: 05/02/2013
• Est. Priority Date: 11/01/2011
• Status: Active Grant

• Current Assignee: University of New Brunswick
• Original Assignee: University of New Brunswick
• Inventors: Bagheri Garekani, Mohammad Ehsan, Zhong, Ming, Christie, James
• Granted Patent: US 8,805,610 B2
• Time in Patent Office: Days
• US Class Current: 701/117
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data

Abstract

Methods for estimating annual average daily traffic for a road segment from historical traffic counts are disclosed.

Claims

1. A computer-implemented method for estimating annual average daily traffic for a first epoch for a first road segment comprising:

providing historical traffic counts for the road segment from a short-term traffic counter from one or more historical epochs which predate the first epoch;

providing data from a first permanent traffic counter;

calculating one or more growth factors from the data of the first permanent traffic counter;

applying the one or more growth factors to the historical short-term traffic counts and converting the historical traffic counts to traffic counts in the first epoch;

converting the converted traffic counts in the first epoch to equivalent monthly average daily traffic values;

using the equivalent monthly average daily traffic values to construct a first seasonal traffic pattern for first road segment;

finding a matching seasonal traffic pattern, derived from a second permanent traffic counter, to the seasonal traffic pattern of the short-term traffic counting site; and

deriving an annual average daily traffic value from the short-term traffic counter of first epoch using one or more expansion factors from the second permanent traffic counter for the first road segment.

2. The computer-implemented method of claim 1 wherein the first permanent traffic counter is the permanent traffic counter that is closest to the short-term traffic counter and of the same functional class as the first road segment.

3. The computer-implemented method of claim 1 wherein the matching seasonal traffic pattern is a best-matching seasonal traffic pattern.

4. The computer-implemented method of claim 1 wherein the step of finding a matching seasonal traffic pattern comprising using a pattern matching algorithm.

5. The computer-implemented method of claim 4 wherein the pattern matching algorithm is selected from the group consisting of minimum square error (MSE) and coefficients of variation (COV).

6. The computer-implemented method of claim 1 wherein the first epoch is the current year.

7. The computer-implemented method of claim 1 wherein the matching seasonal traffic pattern is an average of traffic patterns of a permanent traffic counter group.

8. The computer-implemented method of claim 1, further comprising calculating a preliminary annual average daily traffic value for the first road segment.

9. The computer-implemented method of claim 8 further comprising normalizing the first seasonal traffic pattern.

10. The computer-implemented method of claim 9 wherein the step of finding a matching seasonal traffic pattern is carried out using a minimum square error (MSE).

11. The computer-implemented method of claim 1 further comprising applying a Bayesian assignment of the short-term traffic counts to the permanent traffic counter over a period of at least two years.