Traffic Data Simulator

• US 20150127243A1
• Filed: 11/01/2013
• Published: 05/07/2015
• Est. Priority Date: 11/01/2013
• Status: Active Grant

• Current Assignee: HERE Global B.V. (HERE Holding Corporation)
• Original Assignee: HERE Global B.V. (HERE Holding Corporation)
• Inventors: Jang, Dongwook, Arcot, Praveen
• Granted Patent: US 9,495,868 B2
• Time in Patent Office: Days
• US Class Current: 701/117
• CPC Class Codes: G08G 1/0116 from roadside infrastructure, e.g. beacons
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• 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, traffic data that originates from sensors, cameras, or observations is analyzed. The traffic data is associated with multiple repeating time epochs or intervals. The traffic data is divided into clusters using a clustering technique. The clustering technique may include clusters of variable sizes. Each of the clusters is analyzed to calculate statistical parameters including but not limited to an average value for one or more clusters and a standard deviation value for one or more clusters. In response to a request for traffic data, simulated traffic data may be generated by providing the average value and the standard deviation value for one or more of the clusters.

Claims

1. A method comprising:

receiving traffic data associated with multiple repeating time epochs;

dividing the traffic data into a plurality of clusters;

calculating, by a processor, an average value and a standard deviation value for each of the plurality of clusters;

receiving a request for traffic data; and

providing the average value and the standard deviation value for one or more of the plurality of clusters in response to the request for traffic data.

2. The method of claim 1, wherein dividing into clusters comprises:

selecting, using a random algorithm, an initial centroid for each of the plurality of clusters;

assigning additional traffic data to the initial centroids; and

calculating subsequent centroids based on averages of the additional traffic data and the initial centroids.

3. The method of claim 2, wherein the subsequent centroids are calculated as additional traffic data is added to the plurality of clusters until a threshold is met.

4. The method of claim 3, further comprising:

comparing a difference in location between one of the initial centroids and a corresponding one of the subsequent centroids to the threshold.

5. The method of claim 3, further comprising:

comparing a difference in quantity between one of the plurality of clusters and the one of the plurality of clusters after the subsequent centroids are calculated.

6. The method of claim 3, further comprising:

comparing a difference in an average variance of the one of the plurality of clusters and the one of the plurality of clusters after the subsequent centroids are calculated.

7. The method of claim 1, further comprising:

dividing the traffic data into the plurality of clusters using a first number of clusters;

dividing the traffic data into the plurality of clusters using a second number of clusters;

calculating a first variance using the first number of clusters; and

calculating a second variance using the second number of clusters.

8. The method of claim 1, wherein pseudo traffic data is calculated based a random variable, the average value and the standard deviation value.

9. The method of claim 8, wherein the random variable designates one of the plurality of clusters.

10. 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,

receive traffic data associated with time epochs;

divide the traffic data into a plurality of clusters, wherein the plurality of clusters include variable centroids;

calculate a standard deviation value for each of the plurality of clusters; and

generate simulated traffic data based on the standard deviations and the variable centroids.

11. The apparatus of claim 10, wherein 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,

receive a request for traffic data;

and

provide the variable centroid and the standard deviation value for one or more of the plurality of clusters in response to the request for traffic data.

12. The apparatus of claim 10, wherein 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,

select, using a random algorithm, an initial centroid for each of the plurality of clusters;

assign additional traffic data to one of the initial centroids; and

calculate a subsequent centroid based on an average of the additional traffic data and the initial centroid for one of the plurality of clusters.

13. The apparatus of claim 10, wherein the subsequent centroid is recalculated until a threshold is met.

14. The apparatus of claim 13, wherein 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,

compare a difference in location between the one of the initial centroids and the subsequent centroid to the threshold.

15. The apparatus of claim 13, wherein 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,

compare a difference in quantity between one of the plurality of clusters and the one of the plurality of clusters after the subsequent centroid is calculated.

16. The apparatus of claim 13, wherein 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,

compare a difference in an average variance of the one of the plurality of clusters and the one of the plurality of clusters after the subsequent centroid is calculated.

17. The apparatus of claim 13, wherein 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,

divide the traffic data into the plurality of clusters using a first number of clusters;

divide the traffic data into the plurality of clusters using a second number of clusters;

calculate a first variance using the first number of clusters; and

calculate a second variance using the second number of clusters.

18. The apparatus of claim 10, wherein one of the plurality of clusters for the simulated traffic data is selected based on a random valuable.

19. A non-transitory computer readable medium including instructions that when executed are operable to:

receive measured traffic data organized into time intervals;

divide the measured traffic data within the time intervals into a plurality of data clusters, wherein the plurality of clusters are variably defined using a clustering technique; and

calculate statistical parameters of the measured traffic data within the time intervals, wherein simulated traffic data is generated based on the statistical parameters.

20. The non-transitory computer readable medium of claim 19, the instructions configured to:

select one of the plurality of clusters based a random value.