Predicting impact of a traffic incident on a road network

• US 9,008,954 B2
• Filed: 04/30/2012
• Issued: 04/14/2015
• Est. Priority Date: 04/30/2012
• Status: Active Grant

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Gupta, Chetan Kumar, Miller, Mahalia Katherine
• Primary Examiner(s): Jabr, Fadey
• Assistant Examiner(s): Soofi, Yazan A
• Application Number:
  US13/460,230
• Publication Number:
  US 20130289865A1
• Time in Patent Office: 1,079 Days
• Field of Search: 701/119, 701/117, 701/118
• US Class Current: 701/119
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions

Abstract

A method and system for predicting impact of traffic incidents on a road network by using a classification scheme to identify a known impact classes associated with captured traffic data.

Claims

1. A method for predicting impact of a traffic incident on a road network, the method comprising:

receiving, by a processor, traffic data from at least one data provider; and

using a processor to;

calculate a plurality of traffic-flow velocities from the traffic data, each of the traffic-flow velocities being associated with a data-provider and a data-capture time; and

use a classification scheme and a learning model to predict, based on the traffic data, an impact class associated with the traffic-flow velocities, in which the impact class indicates a degree of severity of an incident and includes a cumulative incident delay identified based on the traffic data.

2. The method of claim 1, wherein the processor is further configured to identify data providers having an associated traffic-flow velocity less than their associated recurrent traffic-flow velocity at the data-capture time.

3. The method of claim 1, wherein the data providers include a police log.

4. The method of claim 1, wherein the impact class includes a temporal impact class.

5. The method of claim 1, wherein the impact class includes an economic loss class.

6. The method of claim 1, further comprising calculating at least one feature vector from the traffic-flow velocity.

7. The method of claim 6, further comprising calculating at least one feature vector from traffic data obtained from a police log or weather report.

16. The method of claim 1, further comprising mapping an incident to an upstream sensor of the data provider.

17. The method of claim 1, in which an impact class identifies an incident duration that indicates an amount of time at which a traffic-flow velocity returns to a recurrent velocity.

18. The method of claim 1, further comprising predicting whether a report of an incident is a false alarm.

19. The method of claim 18, in which the prediction of whether a report of an incident is a false alarm is based on at least one of a difference between a measured speed and a recurrent speed, a road occupancy, and a police report.

8. A system for predicting impact of a traffic incident in a road network, the system comprising:

a plurality of data-capture devices disposed along the road network, the data-capture devices configured to capture the traffic data at a data-capture time;

a processor configured to;

calculate a plurality of traffic-flow velocities from the traffic data, each of the traffic-flow velocities being associated with a data-capture time and one of the traffic-data capture devices,

use a classification scheme and a learning model to predict, based on the traffic data, an impact class associated with the traffic-flow velocities, in which an impact class indicates a degree of severity of an incident and a cumulative incident delay associated with the traffic-flow velocities.

9. The system of claim 8, wherein each of the traffic data-capture devices is selected from the group consisting of a loop induction sensor, an image capture device, and a radar device.

10. The system of claim 8, wherein the impact class includes an impact delay class.

11. The system of claim 8, wherein the impact class includes an economic loss class, in which the economic loss class is calculated based on a cumulative lost time of all drivers multiplied by a monetary value per hour.

12. The system of claim 8, further comprising an output device configured to display the impact class graphically.

13. The system of claim 8, further comprising a processor configured to calculate at least one feature vector from the traffic-flow data.

20. The system of claim 8, in which an impact class indicates an incident delay, and in which an incident delay comprises a cumulative delay of all drivers as a result of an incident.

14. A non-transitory computer-readable medium having stored thereon instructions for predicting impact of a traffic incident in a road network which when executed by a processor causes the processor to perform a method comprising:

receiving traffic data from a plurality of data-capture devices; and

using a processor to;

calculate a plurality of traffic-flow velocities from the traffic data, each of the traffic-flow velocities being associated with a data-capture device and a data capture time,

identify an impact type to associate with the incident region identified based on traffic data from data-capture data devices upstream of an incident, in which an impact type is divided into multiple impact classes, and

use a classification scheme and a learning model to predict, based on the traffic data, an impact class associated with the traffic-flow velocities, in which an impact class indicates a degree of severity of an incident and includes a cumulative incident delay identified based on the traffic data.

15. The non-transitory computer-readable medium of claim 14, further comprising calculating a feature vector based on the traffic-flow velocities.