Adaptive Traffic Dynamics Prediction

• US 20170076595A1
• Filed: 11/28/2016
• Published: 03/16/2017
• Est. Priority Date: 02/10/2014
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: Macfarlane, Jane, Grossman, Robert, Bennett, Collin, Pivarski, James
• Granted Patent: US 9,875,652 B2
• Time in Patent Office: Days
• 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 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: H05K 999/99 dummy group

Abstract

The disclosed embodiments relate to prediction of traffic dynamics. A descriptive model is provided that uses historical probe data to create “tidal-like” patterns for the usual dynamics on the road network and creates a framework for taking a future time, e.g. in terms of month, day, time, and suggesting a typical speed for the specified road network link at that specific time. With this model, better predictions for estimated time of arrival will be derived. As opposed to blindly extrapolating from a static model, the disclosed embodiments dynamically adapt to current conditions using real time data to adapt, based on current conditions, the model from which a predicted speed may be determined.

Claims

1. A computer implemented method comprising:

storing, by a processor in a database stored in a memory, a plurality of speed profiles each of which comprises data indicative of observed travel speeds along a portion of a road network during a prior time period;

storing, in the memory by the processor, data indicative of real-time speed of travel along the portion of the road network;

identifying based on a future time period, by the processor coupled with the database, a subset of the plurality of speed profiles comprising at least two of the plurality of speed profiles; and

determining, by the processor based on the data indicative of the real-time speed of travel and the identified subset of the plurality of speed profiles, one or more predicted traffic dynamics for the future time period for the portion of the road network.

2. The computer implemented method of claim 1 wherein the future time period comprises a calendar date and time of day.

3. The computer implemented method of claim 1 wherein the determining one or more predicted traffic dynamics comprises determining one or more predicted traffic dynamics for the entire portion of the road network.

4. The computer implemented method of claim 1 wherein the determining one or more predicted traffic dynamics comprises determining one or more predicted traffic dynamics for a particular location along the portion of the road network.

5. The computer implemented method of claim 1 wherein the portion of the road network is at least part of a route between a starting location and a destination.

6. The computer implemented method of claim 1 wherein the portion of the road network comprises one or more adjacent route links having one or more similar traffic patterns observed over one or more adjacent time periods.

7. The computer implemented method of claim 1 wherein the data indicative of the real-time speed of travel along the portion of the road network is derived from one or more traffic data sources which have recently collected traffic data for at least part of the portion of the road network.

8. The computer implemented method of claim 1 wherein the future time period comprises a future occurrence of a recurring time period, the subset of the plurality of speed profiles comprising a speed profile for each of a plurality of frequently recurring travel speed patterns observed during prior occurrences of the recurring time period.

9. The computer implemented method of claim 1 wherein the determining further comprises computing a weighted average of the profiles of the subset of the plurality of profiles weighted based on data indicative of the real-time speed of travel.

10. The computer implemented method of claim 1 wherein the determining further comprises selecting the one profile of the subset of the plurality of speed profiles based on a best fit of the data indicative of real-time speed of travel.

11. A system comprising:

a processor and a memory coupled therewith, the memory comprising data indicative of real-time speed of travel along a portion of a road network;

a database stored in the memory, the database comprising a plurality of speed profiles each of which comprises data indicative of observed travel speeds along the portion of the road network during a prior time period;

first logic stored in the memory and executable by the processor to cause the processor to identify, based on a future time period, a subset of the plurality of speed profiles comprising at least two of the plurality of speed profiles; and

second logic stored in the memory and executable by the processor to cause the processor to determine, based on the data indicative of the real-time speed of travel and the identified subset of the plurality of speed profiles, one or more predicted traffic dynamics for the future time period for the portion of the road network.

12. The system of claim 11 wherein the portion of the road network comprises one or more adjacent route links having one or more similar traffic patterns observed over one or more adjacent time periods.

13. The system of claim 11 wherein the data indicative of the real-time speed of travel along the portion of the road network is derived from one or more traffic data sources which have recently collected traffic data for at least part of the portion of the road network.

14. The system of claim 11 wherein the future time period comprises a future occurrence of a recurring time period, the subset of the plurality of speed profiles comprising a speed profile for each of a plurality of frequently recurring travel speed patterns observed during prior occurrences of the recurring time period.

15. The system of claim 11 wherein the second logic is further executable by the processor to cause the processor to compute a weighted average of the profiles of the subset of the plurality of speed profiles weighted based on data indicative of the real-time speed of travel.

16. The system of claim 11 wherein the second logic is further executable by the processor to cause the processor to select the one of the subset of the plurality of speed profiles based on a best fit of the data indicative of real-time speed of travel.

17. A system comprising:

a real time speed model operative to provide data indicative of real-time speed of travel along a portion of a road network;

a historical speed model coupled with the real time speed model and operative to access, based on a future time period, a database including a plurality of speed profiles each of which comprises data indicative of observed travel speeds along the portion of the road network during a prior time period and obtain therefrom a subset of the plurality of speed profiles comprising at least two of the plurality of speed profiles; and

a traffic dynamics predictor coupled with the historical speed model and the real time speed model and operative to determine, based on the data indicative of the real-time speed of travel and the obtained subset of the plurality of speed profiles, one or more predicted traffic dynamics for the future time period for the portion of the road network.

18. The system of claim 17 wherein the portion of the road network comprises one or more adjacent route links having one or more similar traffic patterns observed over one or more adjacent time periods.

19. The system of claim 17 wherein the data indicative of the real-time speed of travel along the portion of the road network is derived from one or more traffic data sources which have recently collected traffic data for at least part of the portion of the road network.

20. The system of claim 17 wherein the traffic dynamics predictor is further operative to compute a weighted average of the profiles of the subset of the plurality of speed profiles weighted based on data indicative of the real-time speed of travel, select the one profile of the subset of the plurality of speed profiles based on a best fit of the data indicative of real-time speed of travel, or a combination thereof.