Adaptive traffic dynamics prediction

Patent metadata

Field	Value
Publication number	US-10629070-B2
Application number	US-201816158958-A
Country	US
Kind code	B2
Filing date	Oct 12, 2018
Priority date	Feb 10, 2014
Publication date	Apr 21, 2020
Grant date	Apr 21, 2020

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.

First claim

What is claimed is:

1. A computer implemented method comprising:
storing, by a processor in a memory coupled therewith, data indicative of a model of traffic conditions of a road network, the model comprising at least two patterns indicative of traffic conditions that have occurred during at least a portion of a prior time period for at least a portion of the road network, including at least two patterns for the same portion of the road network and the same portion of the prior time period, each of the at least two patterns defining different traffic conditions that have occurred on the portion of the road network at the portion of the prior time period; and
adapting, by the processor, the model to account for current traffic conditions along at least a portion of the road network by obtaining data indicative of real-time traffic conditions along at least the portion of the road network, identifying, based on a future time period, a subset of the at least two patterns for the particular portion of the road network applicable to the future time period, and selecting one of the at least two patterns of the identified subset based on the obtained data indicative of real-time traffic conditions, wherein different real-time traffic conditions result in selection of a different one of the at least two patterns of the identified subset, the selected one of the at least two patterns comprising a prediction of traffic conditions for at least the portion of the road network for the future time period.

2. The computer implemented method of claim 1 wherein the traffic conditions that have occurred on the portion of the road network at the portion of the prior time period comprise observed travel speeds along the portion of the road network at the portion of the prior time period.

3. The computer implemented method of claim 1 wherein the subset comprises patterns indicative of the most frequently occurring traffic conditions along the portion of the road network at the portion of the prior time period.

4. The computer implemented method of claim 1 wherein the adapting further comprises calculating the future time period as an estimated arrival time at the portion of the road network based on a prior prediction of traffic conditions by the processor for another portion of the road network ahead of at least the portion of the road network.

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

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

7. The computer implemented method of claim 1 wherein the obtained real-time traffic conditions are derived from one or more traffic data sources which have recently collected traffic data for at least 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, each of the at least two patterns of each subset 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 selecting further comprises computing a weighted average of the at least two patterns of the identified subset based on the obtained data indicative of real-time traffic conditions.

10. The computer implemented method of claim 1 wherein the selecting further comprises selecting the one of the at least two patterns of the identified subset based on a best fit of the obtained data indicative of real-time traffic conditions.

11. A system comprising:
a processor and a memory coupled therewith, the memory comprising data indicative of a model of traffic conditions of a road network, the model comprising at least two patterns indicative of traffic conditions that have occurred during at least a portion of a prior time period for at least a portion of the road network including at least two patterns for the same portion of the road network and the same portion of the prior time period, each of the at least two patterns defining different traffic conditions that have occurred on the portion of the road network at the portion of the prior time period; and
logic stored in the memory and executable by the processor to cause the processor to adapt the model to account for current traffic conditions along at least a portion of the road network via acquisition of data indicative of real-time traffic conditions along at least the portion of the road network, identification, based on a future time period, a subset of the at least two patterns for the particular portion of the road network applicable to the future time period, and selection of one of the at least two patterns of the identified subset based on the obtained data indicative of real-time traffic conditions, wherein different real-time traffic conditions result in selection of a different one of the at least two patterns of the identified subset, the selected one of the at least two patterns comprising a prediction of traffic conditions for at least the portion of the road network for the future time period.

12. The system of claim 11 wherein the traffic conditions that have occurred on the portion of the road network at the portion of the prior time period comprise data indicative of observed travel speeds along the portion of the road network at the portion of the prior time period.

13. The system of claim 11 wherein the subset comprises patterns indicative of the most frequently occurring traffic conditions along the portion of the road network at the portion of the prior time period.

14. The system of claim 11 wherein the logic is further executable by the processor to cause the processor to calculate the future time period as an estimated arrival time at the portion of the road network based on a prior prediction of traffic conditions by the processor for another portion of the road network ahead of at least the portion of the road network.

15. The system of claim 11 wherein the future time period comprises a calendar date and time of day.

16. The system of claim 11 wherein at least the portion of the road network is at least part of a route between a starting location and a destination.

17. The system of claim 11 wherein the obtained real-time traffic conditions are derived from one or more traffic data sources which have recently collected traffic data for at least the portion of the road network.

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

19. The system of claim 11 wherein the logic is further executable by the processor to cause the processor to compute a weighted average of the at least two patterns of the identified subset based on the obtained data indicative of real-time traffic conditions.

20. The system of claim 11 wherein t the logic is further executable by the processor to cause the processor to select the one of the at least two patterns of the identified subset based on a best fit of the obtained data indicative of real-time traffic conditions.

21. A system comprising:
a model of traffic conditions of a road network, the mo

Assignees

• Here Global Bv

Inventors

• Macfarlane Jane
• Grossman Robert
• Bennett Collin
• Pivarski James

Classifications

• G08G1/0112

  from the vehicle, e.g. floating car data [FCD] · CPC title

• G08G1/012

  from other sources than vehicle or roadside beacons, e.g. mobile networks · CPC title

• G08G1/0129 Primary

  for creating historical data or processing based on historical data · CPC title

• G08G1/0141

  for traffic information dissemination · CPC title

• G08G1/0116

  from roadside infrastructure, e.g. beacons · CPC title