Traffic aggregation and reporting in real-time

• US 9,934,683 B2
• Filed: 05/29/2014
• Issued: 04/03/2018
• Est. Priority Date: 05/29/2014
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: HERE Global B.V. (HERE Holding Corporation)
• Inventors: Fowe, James Adeyemi
• Primary Examiner(s): Edwards, Jerrah
• Application Number:
  US14/289,988
• Publication Number:
  US 20150348406A1
• Time in Patent Office: 1,405 Days
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station

Abstract

Systems, apparatuses, and methods are provided for aggregating and reporting real-time traffic conditions. Real-time traffic data for a network is collected. A request from a customer is received for a percentage of the real-time traffic data in the network, the percentage being greater than 0% and less than 100%. The real-time traffic data in the network is aggregated. The aggregated real-time traffic data is reported to the customer.

Claims

1. A method comprising:

collecting real-time traffic data for a network from one or more probes;

receiving a request from a navigation device for a first percentage of the real-time traffic data in the network;

identifying, using a processor, a cost function for each of a plurality of road segments;

ranking, using the processor, each of the plurality of road segments based on the cost function;

aggregating, using the processor, the first percentage of data including road segments having a rank above the first percentage of the total plurality of road segments in the network; and

transmitting the aggregated first percentage of data to the navigation device.

2. The method of claim 1, wherein identifying the cost function for each of the plurality of road segments is based on a naï

ve algorithm.

3. The method of claim 2, wherein the naï

ve algorithm determines the cost function for each road segment of the plurality of road segments by multiplying a traffic density by a difference of a real-time average speed and a historic average speed, and wherein the priority is based on the cost functions for the plurality of road segments.

4. The method of claim 2, wherein the naï

ve algorithm determines the cost function for each road segment of the plurality of road segments by multiplying a traffic density by a historic average speed by a difference of a real-time average speed and the historic average speed, and wherein the priority is based on the cost functions for the plurality of road segments.

5. The method of claim 1, wherein identifying the first percentage comprises prioritizing the real-time traffic data for a plurality of connected road segments based on a minimum spanning tree algorithm.

6. The method of claim 5, wherein the minimum spanning tree algorithm determines the cost function for each road segment of the plurality of connected road segments, wherein:

7. The method of claim 5, wherein the minimum spanning tree algorithm determines the cost function for each road segment of the plurality of connected road segments, wherein:

8. The method of claim 7, wherein w1>

w2.

9. The method of claim 7, wherein w2>

w1.

10. The method of claim 1, wherein the reporting comprises encoding the aggregated real-time traffic data in a TPEG-ML file.

11. The method of claim 1, wherein the first percentage of data is less than 10% of the real-time traffic data.

12. The method of claim 1, further comprising:

aggregating, using the processor, a second subset of data including road segments having a rank above a second percentage of the total plurality of the road segments in the network; and

transmitting the second subset of data to the navigation device when requested by the device.

13. A method comprising:

collecting, from one or more probes, real-time traffic data for a plurality of road segments in a network;

identifying, using a processor, a number of vehicles occupying each road segment of the plurality of road segments within a given time frame;

calculating, using the processor, an average speed for each road segment at the given time frame;

identifying, using the processor, a historical speed for the road segment at a comparable time frame;

calculating, using the processor, a cost function based on the number of vehicles and a difference between the average speed and historical speed for each road segment;

receiving a request from a navigation device for a first percentage of the real-time traffic data in the network;

ranking, using the processer, each road segment of the plurality of road segments based on the cost function;

aggregating, using the processor, the first percentage of the real-time traffic data in the network based on the ranking; and

reporting the first percentage of aggregated real-time traffic data to the navigation device in a transport protocol experts group (TPEG-ML) file.

14. The method of claim 13, further comprising:

aggregating, using the processor, a second subset of data by prioritizing the real-time traffic data for a plurality of road segments based on the cost function; and

transmitting the second subset of data to the navigation device when requested by the device.

15. 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;

collect, from one or more probes, real-time traffic data for a plurality of road segments in a network;

identify a number of vehicles occupying each road segment of the plurality of road segments within a given time frame;

calculate an average speed for each road segment from the real-time traffic data at the given time frame;

identify a historical speed for the road segment at a comparable time frame from historical traffic data stored in the at least one memory;

calculate a cost function based on the number of vehicles and a difference between the average speed and historical speed for each road segment;

receive a request from a navigation device for a first percentage of the real-time traffic data in the network,

rank each road segment of the plurality of road segments based on the cost function;

aggregate the first percentage of the real-time traffic data in the network based on the rank; and

report the aggregated real-time traffic data to the navigation device.

16. The apparatus of claim 15, wherein calculating the cost function is based on a minimum spanning tree algorithm,

wherein the minimum spanning tree algorithm determines the cost function for each road segment of the plurality of connected road segments, wherein:

17. The apparatus of claim 15, wherein calculating the cost function is based on a minimum spanning tree algorithm,

wherein the minimum spanning tree algorithm determines the cost function for each road segment of the plurality of connected road segments, wherein:

18. The apparatus of claim 17, wherein w1>

w2.

19. The apparatus of claim 17, wherein w2>

w1.

20. The apparatus of claim 15, wherein the reporting comprises encoding the aggregated real-time traffic data in a TPEG-ML file.

21. The apparatus of claim 15, wherein the first percentage of real-time traffic data comprises less than 10% of the real-time traffic data.