Predictive Incident Aggregation

• US 20150221218A1
• Filed: 02/03/2014
• Published: 08/06/2015
• Est. Priority Date: 02/03/2014
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: Downs, Oliver, Arcot, Praveen, Tennent, Toby
• Granted Patent: US 9,613,529 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 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination

Abstract

In one embodiment, an incident report including a path segment identifier and an incident identifier is received at a computing device. The incident identifier is sent to a traffic prediction model. The traffic prediction model returns a traffic distribution value. The traffic distribution value identifies a portion of a traffic prediction distribution derived from historical data. The computing device accesses a lookup table according to traffic distribution value and the path segment identifier to receive a speed prediction.

Claims

1. A method comprising:

receiving an incident report including a path segment identifier and an incident identifier;

sending at least the incident identifier to a traffic prediction model;

receiving a traffic distribution value from the traffic prediction model; and

accessing a lookup table according to the traffic distribution value and the path segment identifier for a speed prediction.

2. The method of claim 1, wherein the traffic distribution value is a single digit.

3. The method of claim 1, wherein the lookup table matches speed ranges for path segments according to a plurality of traffic distribution values including the traffic distribution value received from the traffic prediction model.

4. The method of claim 1, wherein the traffic distribution value is a quintile number for the distribution of the historical traffic speeds.

5. The method of claim 4, wherein the quintile number is defined according to a path segment.

6. The method of claim 4, wherein the quintile number corresponds to a graphical representation of the road.

7. The method of claim 1, further comprising:

modifying the traffic distribution value from the traffic prediction model as a function of distance between a road location from the path segment identifier and an incident location from the incident identifier.

8. The method of claim 1, further comprising:

modifying the traffic distribution value from the traffic prediction model as a function of a time difference between a first timestamp from the path segment identifier and a second timestamp from the incident identifier.

9. The method of claim 1, wherein the traffic prediction model associates traffic distribution values with incidents including at least one of an accident event, a hazard event, a weather event, or a flow improving event.

10. The method of claim 9, wherein the traffic prediction model associates traffic distribution values with attributes of the incidents, wherein the attributes include at least one of shoulder location, left lane location, center lane location, right lane location, median location, emergency vehicles present, or multiple vehicles.

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 at least perform;

identify a traffic incident type;

perform a traffic prediction algorithm based on the traffic incident type;

receive a traffic distribution value from the traffic prediction algorithm; and

determine a predicted traffic speed according to traffic distribution value.

12. The apparatus of claim 11, wherein the predicted traffic speed is calculated as a function of a path segment.

13. The apparatus of claim 11, wherein the traffic distribution value is an adjustment value to adjust an expect traffic value.

14. The apparatus of claim 11, the predicted traffic speed is an average value for a statistical division of historical speed data.

15. The apparatus of claim 14, wherein the statistical division in defined according to tertile, quartile, quintile, or standard deviation.

16. The apparatus of claim 14, wherein the statistical division corresponds to a graphical representation of the road.

17. The apparatus of claim 11, wherein the traffic distribution value varies as a function of distance between a road location from the path segment identifier and an incident location from the incident identifier.

18. The apparatus of claim 11, wherein the traffic prediction algorithm associates traffic distribution values with incidents including at least one of an accident event, a hazard event, a weather event, or a flow improving event.

19. The apparatus of claim 11, wherein the traffic prediction algorithm associates traffic distribution values with attributes of the incidents, wherein the attributes include at least one of shoulder location, left lane location, center lane location, right lane location, median location, emergency vehicles present, or multiple vehicles.

20. A method comprising:

receiving historic traffic flow data;

receiving historic incident data, wherein the historic incident data describes types of incidents including at least one of an accident event, a hazard event, a weather event, or a flow improving event, wherein the historic incident data describes attributes of incidents including at least one of shoulder location, left lane location, center lane location, right lane location, median location, emergency vehicles present, or multiple vehicles; and

generating a traffic prediction model based on the historic traffic flow data and the historic incident data, wherein the traffic prediction model outputs a traffic distribution value based on an input incident type and input incident attribute.