Estimating time travel distributions on signalized arterials

• US 10,223,909 B2
• Filed: 10/18/2013
• Issued: 03/05/2019
• Est. Priority Date: 10/18/2012
• Status: Active Grant

• Current Assignee: Uber Technologies, Inc.
• Original Assignee: Uber Technologies, Inc.
• Inventors: Margulici, J. D., Adda, Kevin, Gueziec, Andre, Rojas, Edgar
• Primary Examiner(s): Elchanti, Hussein
• Application Number:
  US14/058,195
• Publication Number:
  US 20140129124A1
• Time in Patent Office: 1,964 Days
• Field of Search: 701119, 701411, 701414
• 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

Abstract

Systems and methods are provided for estimating time travel distributions on signalized arterials. The systems and methods may be implemented as or through a network service. Traffic data regarding a plurality of travel times on a signalized arterial may be received. A present distribution of the travel times on the signalized arterial may be determined. A prior distribution based on one or more travel time observations may also be determined. The present distribution may be calibrated based on the prior distribution.

Claims

1. A system for estimating time travel distributions on signalized arterials, comprising:

a processor;

memory; and

an application stored in memory and executable by the processor to;

receive travel data, wherein the received travel data includes;

travel time observation of the one or more signalized arterials of interest,

travel time observation of one or more nearby signalized arterials to the one or more signalized arterials of interest, and

contextual evidence associated with the one or more signalized arterials of interest,

estimate a first distribution on one or more signalized arterials, wherein the estimated first distribution is based on the received travel data, and wherein the first distribution comprises a linear combination of individual paces weighted by distance traveled,

incorporate travel time variability components with the estimated first distribution, wherein the travel time variability components include individual variations, time-of-day variations and daily variations, and

calibrate the first distribution with prior data including regular past patterns of travel times associated with the one or more signalized arterials to obtain a second distribution, wherein the second distribution is a more recent estimate of travel time compared to the first distribution, and wherein the second distribution also comprises a linear combination of individual paces weighted by distance traveled.

2. The system of claim 1, wherein the travel data is received from one or more mobile GPS devices.

3. The system of claim 1, wherein the travel data is received from one or more re-identification devices.

4. The system of claim 3, wherein the re-identification device is a magnetic signature.

5. The system of claim 3, wherein the re-identification device is a toll tag.

6. The system of claim 3, wherein the re-identification device is a license plate.

7. The system of claim 3, wherein the re-identification device is a Bluetooth receiver.

8. The system of claim 1, wherein the travel data is received from a third-party server that collected the data.

9. The system of claim 1, wherein the server is an open-source server.

10. The system of claim 1, wherein the application is executable by the processor further to update the prior data with the received travel data.

11. The system of claim 1, wherein the contextual evidence associated with the one or more signalized arterials of interest include travel data for the one or more signalized arterials related to local weather, incidents, special events, road closures or other traffic diversions.

12. The system of claim 1, wherein the application is executable by the processor further to normalize the received travel data into a unit of pace that is expressed in seconds per mile.

23. The system of claim 1, wherein the prior data corresponds to travel time distribution by time of day.

13. A method for estimating time travel distributions on signalized arterials, comprising:

receiving travel data, wherein the received travel data includes;

travel time observation of the one or more signalized arterials of interest,

travel time observation of one or more nearby signalized arterials to the one or more signalized arterials of interest, and

contextual evidence associated with the one or more signalized arterials of interest; and

executing instructions stored in memory, wherein execution of the instructions by a computer processor causes the computer processor to;

estimate a first distribution on one or more signalized arterials, wherein the estimated first distribution is based on the received travel data, and wherein the first distribution comprises a linear combination of individual paces weighted by distance traveled,

incorporate travel time variability components with the estimated first distribution, wherein the travel time variability components include individual variations, time-of-day variations and daily variations, and

calibrate the first distribution with prior data including regular past patterns of travel times associated with the one or more signalized arterials to obtain a second distribution, wherein the second distribution is a more recent estimate of travel time compared to the first distribution, and wherein the second distribution also comprises a linear combination of individual paces weighted by distance traveled.

14. The method of claim 13, wherein the travel data is received from one or more mobile GPS devices.

15. The method of claim 13, wherein the travel data is received from one or more re-identification devices.

16. The method of claim 15, wherein the re-identification device is a magnetic signature.

17. The method of claim 15, wherein the re-identification device is a toll tag.

18. The method of claim 15, wherein the re-identification device is a license plate.

19. The method of claim 15, wherein the re-identification device is a Bluetooth receiver.

20. The method of claim 13, wherein the travel data is received from a third-party server that collected the data.

21. The method of claim 13, wherein execution of the instructions by the computer processor further causes the computer processor to estimate traffic conditions on a particular signalized arterial segment based on the calibrated distribution.

22. The method of claim 13, wherein execution of the instructions by the computer processor further causes the computer processor to display the estimated traffic conditions to a user through a graphical interface of a mobile device.