METHOD AND APPARATUS FOR PROVIDING TRAFFIC JAM DETECTION AND PREDICTION

• US 20160247397A1
• Filed: 02/24/2015
• Published: 08/25/2016
• Est. Priority Date: 02/24/2015
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: HERE Global B.V. (HERE Holding Corporation)
• Inventors: XU, Bo, MACFARLANE, Jane
• Granted Patent: US 9,569,960 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0125 Traffic data processing
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P

Abstract

An approach is provided for predicting starting points and/or ending points for traffic jams in one or more travel segments. The approach involves processing and/or facilitating a processing of probe data associated with at least one travel segment to cause, at least in part, a generation of at least one speed curve with respect to a distance dimension and a time dimension, wherein the probe data includes speed information, and wherein the at least one speed curve indicates at least one previous starting point, at least one previous ending point, or a combination thereof for one or more previous traffic jams based, at least in part, on the speed information. The approach also involves processing and/or facilitating a processing of the at least one previous starting point, the at least one previous ending point, or a combination thereof to determine at least one starting point trend curve, at least one ending point trend curve, or a combination thereof with respect to the distance dimension and the time dimension. The approach further involves determining at least one predicted evolution of at least one starting point, at least one ending point, or a combination thereof for at least one traffic jam in the at least one travel segment based, at least in part, on the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof.

Claims

1. A method comprising:

processing and/or facilitating a processing of probe data associated with at least one travel segment to cause, at least in part, a generation of at least one speed curve with respect to a distance dimension and a time dimension, wherein the probe data includes speed information, and wherein the at least one speed curve indicates at least one previous starting point, at least one previous ending point, or a combination thereof for one or more previous traffic jams based, at least in part, on the speed information;

processing and/or facilitating a processing of the at least one previous starting point, the at least one previous ending point, or a combination thereof to determine at least one starting point trend curve, at least one ending point trend curve, or a combination thereof with respect to the distance dimension and the time dimension; and

determining at least one predicted evolution of at least one starting point, at least one ending point, or a combination thereof for at least one traffic jam in the at least one travel segment based, at least in part, on the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof.

2. A method of claim 1, further comprising:

causing, at least in part, an initiation of the determination of the at least one predicted evolution after a collection of one or more data points of the at least one observed starting point, the at least one observed ending point, or a combination thereof.

3. A method of claim 1, further comprising:

determining the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof to use based, at least in part, on a curve-fitting of the at least one observed starting point, the at least one observed ending point, or a combination thereof.

4. A method of claim 3, further comprising:

determining that there is not a difference above a threshold value between the one or more data points and one or more predicted starting points, one or more predicted ending points, or a combination thereof that are predicted from the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof and

causing, at least in part, an adjustment of the curve-fitting based, at least in part, on the one or more data points.

5. A method of claim 3, further comprising:

determining that there is a difference above a threshold value between the one or more data points and one or more predicted starting points, one or more predicted ending points, or a combination thereof that are predicted from the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof; and

causing, at least in part, an invalidation of the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof.

6. A method of claim 1, further comprising:

causing, at least in part, a partitioning of the at least one travel segment into one or more sections based, at least in part, on the distance dimension,

wherein the generation of the at least one speed curve is based, at least in part, on the one or more sections.

7. A method of claim 1, further comprising:

causing, at least in part, a sorting of the probe data along the time dimension using at least one time window,

wherein the at least one time window is associated respectively with the at least one speed curve.

8. A method of claim 7, further comprising:

specifying at least one time increment for moving from a first one of the at least one time window to a second one of the time window for generating the at least one speed curve.

9. A method of claim 1, further comprising:

determining the at least one previous starting point, the at least one previous ending point, the at least one starting point, the at least one ending point, or a combination thereof by comparing against at least one jam threshold value.

10. A method of claim 9, wherein the determining of the at least one previous starting point, the at least one previous ending point, the at least one starting point, the at least one ending point, or a combination thereof is further based, at least in part, on at least one noise tolerance value, and wherein the at least one noise tolerance represents a threshold number of consecutive observations to make before the determining of the at least one previous starting point, the at least one previous ending point, the at least one starting point, the at least one ending point, or a combination thereof is made.

11. An apparatus comprising:

at least one processor; and

at least one memory including computer program code for one or more programs,

the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following,

process and/or facilitate a processing of probe data associated with at least one travel segment to cause, at least in part, a generation of at least one speed curve with respect to a distance dimension and a time dimension, wherein the probe data includes speed information, and wherein the at least one speed curve indicates at least one previous starting point, at least one previous ending point, or a combination thereof for one or more previous traffic jams based, at least in part, on the speed information;

process and/or facilitate a processing of the at least one previous starting point, the at least one previous ending point, or a combination thereof to determine at least one starting point trend curve, at least one ending point trend curve, or a combination thereof with respect to the distance dimension and the time dimension; and

determining at least one predicted evolution of at least one starting point, at least one ending point, or a combination thereof for at least one traffic jam in the at least one travel segment based, at least in part, on the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof.

12. An apparatus of claim 11, wherein the apparatus is further caused to:

cause, at least in part, an initiation of the determination of the at least one predicted evolution after a collection of one or more data points of the at least one observed starting point, the at least one observed ending point, or a combination thereof.

13. An apparatus of claim 11, wherein the apparatus is further caused to:

determine the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof to use based, at least in part, on a curve-fitting of the at least one observed starting point, the at least one observed ending point, or a combination thereof.

14. An apparatus of claim 13, wherein the apparatus is further caused to:

determine that there is not a difference above a threshold value between the one or more data points and one or more predicted starting points, one or more predicted ending points, or a combination thereof that are predicted from the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof; and

cause, at least in part, an adjustment of the curve-fitting based, at least in part, on the one or more data points.

15. An apparatus of claim 13, wherein the apparatus is further caused to:

determine that there is a difference above a threshold value between the one or more data points and one or more predicted starting points, one or more predicted ending points, or a combination thereof that are predicted from the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof; and

cause, at least in part, an invalidation of the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof.

16. An apparatus of claim 11, wherein the apparatus is further caused to:

cause, at least in part, a partitioning of the at least one travel segment into one or more sections based, at least in part, on the distance dimension,

wherein the generation of the at least one speed curve is based, at least in part, on the one or more sections.

17. An apparatus of claim 11, wherein the apparatus is further caused to:

cause, at least in part, a sorting of the probe data along the time dimension using at least one time window,

wherein the at least one time window is associated respectively with the at least one speed curve.

18. A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform the following steps:

process and/or facilitate a processing of probe data associated with at least one travel segment to cause, at least in part, a generation of at least one speed curve with respect to a distance dimension and a time dimension, wherein the probe data includes speed information, and wherein the at least one speed curve indicates at least one previous starting point, at least one previous ending point, or a combination thereof for one or more previous traffic jams based, at least in part, on the speed information;

process and/or facilitate a processing of the at least one previous starting point, the at least one previous ending point, or a combination thereof to determine at least one starting point trend curve, at least one ending point trend curve, or a combination thereof with respect to the distance dimension and the time dimension; and

determine at least one predicted evolution of at least one starting point, at least one ending point, or a combination thereof for at least one traffic jam in the at least one travel segment based, at least in part, on the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof.

19. A non-transitory computer-readable storage medium of claim 18, wherein the apparatus is further caused to:

cause, at least in part, an initiation of the determination of the at least one predicted evolution after a collection of one or more data points of the at least one observed starting point, the at least one observed ending point, or a combination thereof.

20. A non-transitory computer-readable storage medium of claim 18, wherein the apparatus is further caused to:

determine the at least one starting point trend curve, the at least one ending point trend curve, or a combination thereof to use based, at least in part, on a curve-fitting of the at least one observed starting point, the at least one observed ending point, or a combination thereof.

21-48. -48. (canceled)