Estimation of hourly traffic flow profiles using speed data and annual average daily traffic data

• US 9,123,239 B2
• Filed: 01/21/2014
• Issued: 09/01/2015
• Est. Priority Date: 01/21/2014
• Status: Active Grant

• Current Assignee: Iteris Incorporated
• Original Assignee: Iteris Incorporated
• Inventors: Kurzhanskiy, Alex A.
• Primary Examiner(s): NGUYEN, TAN QUANG
• Application Number:
  US14/159,769
• Publication Number:
  US 20150206428A1
• Time in Patent Office: 588 Days
• Field of Search: 701/117, 701/118, 701/119, 340/905
• US Class Current: 1/1
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0145 for active traffic flow control
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P

Abstract

A framework for performance evaluation and active management of a transportation network infrastructure reconstructs traffic flow profiles by modeling annual average daily traffic data and collected traffic speed data to estimate an hourly traffic flow profile for a roadway segment, or link. Total daily flow for a link is derived from the corresponding annual average daily traffic data for that link, and is adjusting by the day of week and the monthly seasonal factors. An hourly flow distribution profile for a roadway link is then constructed using the traffic speed data relative to that link.

Claims

1. A method of estimating an hourly traffic flow profile for a roadway segment, comprising:

computing a daily traffic flow value at a location on a roadway segment, and for a specified direction at a specified date, from annual average daily traffic data;

determining an hourly flow distribution profile for the location and for the specified direction at the specified date from collected traffic speed data, by

     1) constructing temporal templates to detected traffic flows values,

     2) developing one or more speed profiles from the collected traffic speed data, and

     3) assigning a temporal template to a speed profile; and

calculating hourly traffic flow profiles by multiplying the hourly flow distribution profile by the daily flow value.

2. The method of claim 1, wherein the computing a daily traffic flow value at a location on a roadway segment further comprises applying a monthly seasonal factor obtained from published traffic volume trends.

3. The method of claim 1, wherein the computing a daily traffic flow value at a location on a roadway segment further comprises applying a day of the week factor, the day of the week factor obtained by finding locations with existing detector measurements representative of an overall traffic volume situation, collecting daily flows for a specified time period, and grouping daily flows by a day of week.

4. The method of claim 1, wherein the determining an hourly flow distribution profile further comprises selecting locations of the roadway segment with proper flow detection, grouping together data falling into a plurality of time periods, and averaging those values over a selected time period to construct the temporal templates, wherein the plurality of time periods includes a morning peak, an afternoon peak, a Saturday period, a Sunday period, and a double peak.

5. The method of claim 4, wherein the determining an hourly flow distribution profile further comprises comparing the temporal templates assigned to a location, direction and day in a speed profile with ground truth data representative of an actual flow measurement.

6. The method of claim 1, further comprising identifying a candidate temporal template for a free flow traffic speed estimation by determining a time period when a maximum speed drop occurs.

7. The method of claim 6, further comprising comparing traffic speed data for an opposing time period, wherein a speed drop that is 85% or higher of the maximum speed drop results in a double peak time period assigned as the temporal template hourly distribution profile.

8. The method of claim 1, further comprising assigning a confidence level to each speed profile assigned with a temporal template.

9. The method of claim 8, further comprising examining an opposite direction of traffic flow at the location and one or more neighboring links of roadway segments where the assigned confidence level is zero, wherein where the opposite direction of traffic flow at the location and one or more neighboring links of roadway segments have high confidence levels assigned and confirm an assignment of a correct distribution profile template for the location, the hourly flow distribution profiles assigned with high confidence are smeared to the neighboring links with an assigned confidence level of zero, and infer the hourly flow distribution profile for the opposite direction at the location.

10. A system comprising:

a computer-readable storage medium operably coupled to at least one computer processor and having program instructions stored therein, the computer processor being operable to execute the program instructions to perform one or more data processing functions in a plurality of modules, the plurality of modules including;

a data ingest module configured to ingest input data that at least includes annual average daily traffic data, detected traffic flow values, and collected traffic speed data,

a daily traffic flow module configured to normalize the annual average traffic data with a monthly seasonal factor and a day of the week factor for each location on a roadway segment, for a specified direction and a specified date to formulate a daily traffic flow value,

an hourly flow distribution profile module configured to assign temporal templates developed from detected traffic flow values to speed profiles representative of collected traffic speed data to determine an hourly flow distribution profile for each location, for the specified direction at the specified date, and

a classification module configured to categorize the average annual daily traffic data with the collected traffic speed data to allocate the average annual daily traffic data into a time period that includes a morning peak, an afternoon peak, a Saturday period, a Sunday period, and a double peak, by multiplying the hourly flow distribution profile by the daily traffic flow value.

11. The system of claim 10, wherein the monthly seasonal factor is obtained from published traffic volume trends, and the day of the week factor is obtained from locations with existing detector measurements representative of an overall traffic volume situation, for which daily flows are collected for a specified time period and grouped by a day of week.

12. The system of claim 10, wherein the hourly flow distribution profile module is further configured to select locations of the roadway segment with proper flow detection, group together data falling into a plurality of time periods, and average those values over a selected time period to construct the temporal templates.

13. The system of claim 10, wherein the hourly flow distribution profile module is further configured to compare the temporal templates assigned to a location, direction and day in a speed profile with ground truth data representative of an actual flow measurement.

14. The system of claim 10, wherein the classification module is further configured to identify a candidate temporal template for a free flow traffic speed estimation by determining a time period when a maximum speed drop occurs.

15. The system of claim 14, wherein the classification module is further configured to compare traffic speed data for an opposing time period, wherein a speed drop that is 85% or higher of the maximum speed drop results in a double peak time period assigned as the temporal template hourly distribution profile.

16. The system of claim 10, wherein the classification module is further configured to assign a confidence level to each speed profile assigned with a temporal template and examine an opposite direction of traffic flow at the location and one or more neighboring links of roadway segments where the assigned confidence level is zero, so that where the opposite direction of traffic flow at the location and one or more neighboring links of roadway segments have high confidence levels assigned and confirm an assignment of a correct distribution profile template for the location, the hourly flow distribution profiles assigned with high confidence are smeared to the neighboring links with an assigned confidence level of zero to infer the hourly flow distribution profile for the opposite direction at the location.

17. A method comprising:

ingesting input data that at least includes annual average daily traffic data, detected traffic flow values, and collected traffic speed data;

modeling the input data to construct estimates of hourly traffic flow profiles, by;

normalizing the annual average traffic data with a monthly seasonal factor and a day of the week factor for each location on a roadway segment, for a specified direction and a specified date to formulate a daily traffic flow value,

assigning temporal templates constructed from detected traffic flow values to speed profiles representative of collected traffic speed data to determine an hourly flow distribution profile for each location, for the specified direction at the specified date, and

categorizing the average annual daily traffic data with the collected traffic speed data to allocate the average annual daily traffic data into a time period that includes a morning peak, an afternoon peak, a Saturday period, a Sunday period, and a double peak; and

generating output data representative of estimations of hourly traffic flow profiles.

18. The method of claim 17, wherein the normalizing the average annual traffic data further comprising developing the monthly seasonal factor from published traffic volume trends, and developing the day of the week factor from locations with existing detector measurements representative of an overall traffic volume situation, for which daily flows are collected for a specified time period and grouped by a day of week.

19. The method of claim 17, wherein the assigning temporal templates further comprises selecting locations of the roadway segment with proper flow detection, grouping together data falling into a plurality of time periods, and averaging those values over a selected time period to construct the temporal templates.

20. The method of claim 19, wherein the assigning temporal templates further comprises comparing the temporal templates assigned to a location, direction and day in a speed profile with ground truth data representative of an actual flow measurement.

21. The method of claim 17, wherein the modeling the input data to construct estimates of hourly traffic flow profiles further comprises identifying a candidate temporal template for a free flow traffic speed estimation by determining a time period when a maximum speed drop occurs.

22. The method of claim 21, wherein the modeling the input data to construct estimates of hourly traffic flow profiles further comprises comparing traffic speed data for an opposing time period, wherein a speed drop that is 85% or higher of the maximum speed drop results in a double peak time period assigned as the temporal template hourly distribution profile.

23. The method of claim 17, wherein the modeling the input data to construct estimates of hourly traffic flow profiles further comprises assigning a confidence level to each speed profile assigned with a temporal template.

24. The method of claim 23, wherein the modeling the input data to construct estimates of hourly traffic flow profiles further comprises examining an opposite direction of traffic flow at the location and one or more neighboring links of roadway segments where the assigned confidence level is zero, wherein where the opposite direction of traffic flow at the location and one or more neighboring links of roadway segments have high confidence levels assigned and confirm an assignment of a correct distribution profile template for the location, the hourly flow distribution profiles assigned with high confidence are smeared to the neighboring links with an assigned confidence level of zero, and infer the hourly flow distribution profile for the opposite direction at the location.

25. The method of claim 23, wherein the generating output data representative of estimations of hourly traffic flow profiles further comprises generating animated content for visualization of the output data on a graphical user interface.

26. The method of claim 23, wherein the generating output data representative of estimations of hourly traffic flow profiles further comprises computing at least one of roadway network throughput, delay in vehicle-hours imposed by a traffic condition, and a degree of roadway utilization as a measure of productivity.

27. The method of claim 23, wherein the generating output data representative of estimations of hourly traffic flow profiles further comprises generating real-time traffic control and route recommendations for content distribution to one or more of web-based applications, mobile-specific applications, and broadcast media.