Detecting unrepresentative road traffic condition data

• US 8,880,324 B2
• Filed: 01/31/2014
• Issued: 11/04/2014
• Est. Priority Date: 03/03/2006
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inx. Inrix
• Inventors: Chapman, Craig H., Parikh, Kush G., Downs, Oliver B., Cahn, Robert C., Hersch, Jesse S.
• Primary Examiner(s): Beaulieu, Yonel
• Application Number:
  US14/170,127
• Publication Number:
  US 20140149030A1
• Time in Patent Office: 277 Days
• Field of Search: 701117-119, 340/995.13
• US Class Current: 701/119
• CPC Class Codes: G01C 21/3691 Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G08G 1/00 Traffic control systems for road vehicles arrangement of road signs or traffic signals E01F9/00 ; automatic vehicle control B62D
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• 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 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/0968 Systems involving transmission of navigation instructions to the vehicle

Abstract

Techniques are described for assessing road traffic conditions in various ways based on obtained traffic-related data, such as data samples from vehicles and other mobile data sources traveling on the roads and/or from one or more other sources (such as physical sensors near to or embedded in the roads). The road traffic conditions assessment based on obtained data samples may include various filtering and/or conditioning of the data samples, and various inferences and probabilistic determinations of traffic-related characteristics of interest from the data samples. In some situations, the inferences include repeatedly determining current traffic flow characteristics and/or predicted future traffic flow characteristics for road segments of interest during time periods of interest, such as to determine average traffic speed, traffic volume and/or occupancy, and include weighting various data samples in various ways (e.g., based on a latency of the data samples and/or a source of the data samples).

Claims

1. A computer-implemented method comprising:

receiving, by one or more configured computing systems, multiple data samples that reflect travel characteristics of multiple vehicles traveling on a road;

automatically analyzing, by the one or more configured computing systems, the multiple data samples to determine one or more data samples of the multiple data samples that are unrepresentative of actual vehicle travel on the road; and

providing, by the one or more configured computing systems, an indication of data samples from the multiple data samples other than the determined one or more data samples, to enable use of the indicated data samples in facilitating vehicle travel.

2. The computer-implemented method of claim 1 further comprising analyzing the multiple data samples other than the unrepresentative data samples in order to determine an average speed of vehicles traveling on the road, and wherein the providing of the indication includes indicating the determined average speed for use in facilitating travel of other vehicles on the road.

3. The computer-implemented method of claim 1 wherein determining of the one or more unrepresentative data samples includes determining that at least one of the unrepresentative data samples is a statistical outlier with respect to other of the multiple data samples.

4. The computer-implemented method of claim 3 wherein the travel characteristics reflected by the multiple data samples include a reported speed for each of the multiple data samples, and wherein the determining that the at least one unrepresentative data sample is a statistical outlier is based at least in part on the reported speed for each of the at least one unrepresentative data samples.

5. The computer-implemented method of claim 1 wherein the travel characteristics reflected by the multiple data samples include a reported speed for each of the multiple data samples, and wherein determining of the one or more unrepresentative data samples includes, for each of the one or more unrepresentative data samples:

determining an average speed and a standard deviation for all of the multiple data samples for the road other than the unrepresentative data sample;

determining a difference between the reported speed for the unrepresentative data sample and the determined average speed;

determining a threshold based at least in part on the determined standard deviation; and

in response to the determined difference exceeding the determined threshold, identifying the unrepresentative data sample as a statistical outlier.

6. The computer-implemented method of claim 1 wherein determining of the one or more unrepresentative data samples includes assessing an activity of each vehicle associated with at least one of the unrepresentative data samples and determining that the assessed activity of each of those vehicles does not correspond to actual vehicle travel on the road.

7. The computer-implemented method of claim 6 wherein the assessed activity of at least one of the vehicles corresponds to being a parked vehicle.

8. The computer-implemented method of claim 6 wherein the assessed activity of at least one of the vehicles corresponds to travel on a second road distinct from the road.

9. The computer-implemented method of claim 1 wherein determining of the one or more unrepresentative data samples includes identifying multiple data samples associated with a single vehicle traveling on the road, determining an activity of the single vehicle over time based on the identified data samples, and determining that the identified data samples are unrepresentative of actual vehicle travel on the road based on the determined activity.

10. The computer-implemented method of claim 1 wherein determining of the one or more data unrepresentative samples includes identifying expected values for the multiple data samples associated with the road and determining that the unrepresentative data samples do not fit the identified expected values.

11. The computer-implemented method of claim 1 wherein determining of the one or more unrepresentative data samples includes determining a statistical distribution for the multiple data samples and determining that the unrepresentative data samples do not fit the determined statistical distribution.

12. The computer-implemented method of claim 1 wherein determining of the one or more unrepresentative data samples includes identifying multiple distinct data curves for the road, each of the data curves reflecting a distinct subset of vehicle travel on at least a portion of the road, and wherein the unrepresentative data samples fit at least one of the data curves that reflects a subset of vehicle travel that is not of interest.

13. The computer-implemented method of claim 12 wherein at least one of the identified data curves is a Gaussian curve.

14. The computer-implemented method of claim 1 wherein the multiple data samples each further reflect a reported time, and wherein the automatic analyzing of the multiple associated data samples is performed with respect to a predetermined period of time such that the actual vehicle travel on the road is travel during the predetermined period of time.

15. The computer-implemented method of claim 14 wherein determining of the one or more unrepresentative data samples includes identifying that the reported time for each of the unrepresentative data samples is not within the predetermined period of time.

16. The computer-implemented method of claim 1 wherein determining of the one or more unrepresentative data samples is performed in a substantially realtime manner after acquisition of the multiple data samples.

17. The computer-implemented method of claim 1 wherein at least some of the multiple data samples are acquired by and reported from vehicles traveling on the road, and wherein the providing of the indication of the data samples occurs in a substantially realtime manner after acquisition of the at least some data samples.

18. The computer-implemented method of claim 1 wherein the travel characteristics include a reported speed of a vehicle for each of the multiple data samples, wherein determining of the one or more unrepresentative data samples includes determining that at least one of the multiple data samples are statistical outliers with respect to other of the multiple data samples, and wherein the providing of the indications of the data samples includes analyzing the indicated data samples to determine an average speed of vehicles traveling on the road and indicating the determined average speed for use in facilitating travel of other vehicles on the road.

19. The computer-implemented method of claim 18 wherein the determining that the at least one data samples are statistical outliers with respect to other of the data samples includes performing a leave-one-out outlier analysis in a substantially realtime manner.

20. A non-transitory computer-readable medium whose stored contents configure a computing device to perform a method, the method comprising:

receiving, by the configured computing device, multiple data samples that reflect travel characteristics associated with multiple vehicles traveling on a road;

analyzing, by the configured computing device, the multiple data samples to determine one or more data samples of the multiple data samples that are unrepresentative of actual vehicle travel on the road; and

providing, by the configured computing device, an indication of the unrepresentative data samples from the multiple data samples, to exclude future use of the unrepresentative data samples.

21. The non-transitory computer-readable medium of claim 20 wherein the stored contents further configure the computing device to:

analyze the multiple data samples other than the unrepresentative data samples in order to determine an average speed of vehicles traveling on the road; and

provide an indication of the determined average speed for use in facilitating travel of other vehicles on the road.

22. The non-transitory computer-readable medium of claim 20 wherein determining of the one or more unrepresentative data samples includes determining that at least one of the unrepresentative data samples is a statistical outlier with respect to other of the multiple data samples.

23. The non-transitory computer-readable medium of claim 20 wherein determining of the one or more unrepresentative data samples includes assessing an activity of each vehicle associated with at least one of the unrepresentative data samples and determining that the assessed activity of each of those vehicles does not correspond to actual vehicle travel on the road.

24. The non-transitory computer-readable medium of claim 20 wherein determining of the one or more unrepresentative data samples includes identifying multiple data samples associated with a single vehicle traveling on the road and determining that the identified data samples are unrepresentative of actual vehicle travel on the road based on a determined activity of the single vehicle.

25. The non-transitory computer-readable medium of claim 20 wherein determining of the one or more unrepresentative data samples includes determining that the unrepresentative data samples do not fit a determined statistical distribution for the multiple data samples.

26. The non-transitory computer-readable medium of claim 20 wherein the multiple data samples each further reflect a reported time, and wherein the analyzing of the multiple associated data samples is performed with respect to a predetermined period of time, such that the actual vehicle travel on the road is travel during the predetermined period of time.

27. A computing device comprising:

one or more processors; and

one or more components configured to, when executed by at least one of the one or more processors, and for each of at least one of multiple segments of multiple roads;

receive multiple data samples that reflect travel characteristics of multiple vehicles traveling on a road segment;

identify one or more data samples of the multiple data samples that are unrepresentative of actual vehicle travel on the road segment; and

provide an indication of data samples from the multiple data samples other than the identified one or more data samples, to enable use of the indicated data samples in facilitating vehicle travel on at least one of the multiple roads.

28. The computing device of claim 27 wherein the one or more components are further configured to analyze the multiple data samples other than the unrepresentative data samples in order to determine an average speed of vehicles traveling on the road segment.

29. The computing device of claim 27 wherein the identifying of the one or more unrepresentative data samples includes determining that an assessed activity of a vehicle associated with each of at least one of the unrepresentative data samples does not correspond to actual vehicle travel on the road segment.

30. The computing device of claim 27 wherein the identifying of the one or more unrepresentative data samples includes determining multiple data samples associated with a single vehicle traveling on the road segment, assessing an activity of the single vehicle over time based on the determined data samples, and determining that the identified data samples are unrepresentative of actual vehicle travel on the road segment based on the assessed activity.

31. The computing device of claim 27 wherein the identifying of the one or more unrepresentative data samples includes determining multiple distinct data curves for the road segment, each of the data curves reflecting a distinct subset of vehicle travel the road segment, and wherein the unrepresentative data samples fit at least one of the data curves that reflects a subset of vehicle travel that is not of interest.

32. The computing device of claim 27 wherein the travel characteristics include a reported speed of a vehicle for each of the multiple data samples, wherein the identifying of the one or more unrepresentative data samples includes determining that at least one of the multiple data samples are statistical outliers with respect to other of the multiple data samples, and wherein the providing of the indications of the data samples includes analyzing the indicated data samples to determine an average speed of vehicles traveling on the road segment.