FILTERING ROAD TRAFFIC CONDITION DATA OBTAINED FROM MOBILE DATA SOURCES

• US 20110288756A1
• Filed: 07/29/2011
• Published: 11/24/2011
• Est. Priority Date: 03/03/2006
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Burns, Mitchel A. JR., Downs, Oliver B., Barker, Alec, Chapman, Craig H., Love, Scott R.
• Granted Patent: US 9,449,508 B2
• Time in Patent Office: Days
• US Class Current: 701/118
• 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 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/052 with provision for determining speed or overspeed speed measuring in general G01P
• CPC Class Codes: G08G 1/056 with provision for distinguishing direction of travel
• CPC Class Codes: G08G 1/065 by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
• CPC Class Codes: H04W 88/02 Terminal devices

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, as well as in some situations data from one or more other sources (such as physical sensors near to or embedded in the roads). The assessment of road traffic conditions 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 from the data samples. In some situations, the filtering of the data samples includes identifying data samples that are inaccurate or otherwise unrepresentative of actual traffic condition characteristics, such as data samples that are not of interest based at least in part on roads with which the data samples are associated and/or that otherwise reflect vehicle locations or activities that are not of interest.

Claims

1-10. -10. (canceled)

11. A computer-implemented method for assessing data samples representing vehicles traveling on roads, the method comprising:

receiving an indication of one or more segments of one or more roads, each road segment having multiple associated data samples that each are reported by one of multiple vehicles and indicate a reported location of the vehicle that corresponds to the road segment; and

for each of at least one of the road segments,

automatically analyzing, by one or more configured computing systems, the multiple associated data samples for the road segment so as to determine one or more of those data samples that are unrepresentative of actual vehicle travel on the road segment, at least one of the determined data samples each indicating a reported location for the vehicle that reported the data sample such that the reported location does not correspond to the actual vehicle travel on the road segment, and at least one of the determined data samples each having an associated heading for the vehicle that reported the data sample such that the associated heading does not correspond to the actual vehicle travel on the road segment, and wherein the determining that the one or more data samples are unrepresentative is further based at least in part on vehicle speeds indicated by the one or more data samples; and

providing, by the one or more configured computing systems, one or more indications to exclude the determined data samples from later use so that the other data samples are available for use in facilitating travel on the road segment.

14. The method of claim 11 wherein, for each of one or more of the at least one road segments, the determining of the one or more data samples for the road segment that are unrepresentative of actual vehicle travel on the road segment includes determining that the reported vehicle locations for those data samples correspond to a section of road that is not of interest for use in representing the actual vehicle travel on the road segment.

15. The method of claim 14 wherein, for each of at least one of the one or more road segments, the section of road is at least part of a lower-capacity road that leads to and/or from the road segment.

16. The method of claim 14 wherein, for each of at least one of the one or more road segments, the section of road is part of a distinct road that is near to the road of the road segment.

17. The method of claim 14 wherein, for each of at least one of the one or more road segments, the section of road is a subset of multiple lanes that are part of the road segment.

18. The method of claim 14 wherein, for each of at least one of the one or more road segments, the section of road is at least part of one or more of a ramp on and/or off the road segment, a collector/distributor road associated with the road of the road segment, a collector/distributor lane associated with the road of the road segment, a feeder lane associated with the road of the road segment, a shoulder of the road of the road segment, and a breakdown area for the road of the road segment.

19. The method of claim 14 wherein, for one of the one or more road segments, the section of road that is not of interest is part of the one road segment.

20. The method of claim 14 wherein, for one of the one or more road segments, the section of road that is not of interest is at least a part of another road segment distinct from the one road segment.

21. The method of claim 11 wherein, for one of the at least one road segments, at least some of the multiple data samples associated with the one road segment are also associated with one or more other distinct road segments, and wherein the determined one or more data samples for the one road segment are from the at least some data samples.

22. The method of claim 11 further comprising, for one of the road segments, automatically determining that the multiple associated data samples for the one road segment are not of interest based at least in part on the one road segment not being of interest, and providing one or more indications to exclude the multiple associated data samples from later use.

23. The method of claim 22 wherein the one road segment is determined to not be of interest based at least in part on being of a functional road class that is not of interest.

24. The method of claim 22 wherein the one road segment is determined to not be of interest based at least in part on a determination of an amount of actual vehicle traffic on the one road segment.

25. The method of claim 22 wherein the one road segment is determined to not be of interest based at least in part on a determination of an amount of intra-day variability of vehicle traffic on the one road segment and/or on a determination of an amount of inter-day variability of vehicle traffic on the one road segment.

26. The method of claim 22 wherein the one road segment is determined to not be of interest based at least in part on a determination of an amount of actual traffic congestion on the one road segment.

27. The method of claim 22 wherein the one road segment is determined to not be of interest based at least in part on a determination of an amount of intra-day variability of traffic congestion on the one road segment and/or on a determination of an amount of inter-day variability of traffic congestion on the one road segment.

28. The method of claim 11 further comprising automatically determining that one or more data samples not associated with any of the road segments are not of interest, and providing one or more indications to exclude the one or more data samples from later use.

29. The method of claim 11 wherein, for each of one or more of the at least one road segments, the determining of the one or more data samples for the road segment that are unrepresentative of actual vehicle travel on the road segment includes determining that the one or more data samples are unrepresentative based at least in part on the reported locations of the one of more data samples.

31. The method of claim 29 further comprising, for each of at least one of the one or more road segments, and for each of at least some of the multiple associated data samples for the road segment, estimating the indicated speed for the data sample by using reported locations indicated by multiple data samples reported by the vehicle that reported the data sample.

32. The method of claim 29 wherein, for each of the one or more road segments, the multiple associated data samples for the road segment each having an associated heading of the vehicle that reported the data sample, and wherein the determining that the one or more data samples for the road segment are unrepresentative is further based at least in part on the headings associated with the one or more data samples.

33. The method of claim 32 further comprising, for each of at least one of the one or more road segments, and for each of at least some of the multiple associated data samples for the road segment, estimating the heading associated with the data sample by using reported locations indicated by multiple data samples reported by the vehicle that reported the data sample.

34. The method of claim 32 wherein one of the one or more road segments is part of a road that includes vehicles traveling in two opposite directions, wherein the one road segment corresponds to vehicles traveling in one of the two directions, and wherein the determining that one or more data samples for the one road segment are unrepresentative based at least in part on the headings associated with the one or more data samples includes determining that data samples whose associated headings correspond to travel in the other of the two directions are unrepresentative for the one road segment.

35. The method of claim 32 wherein one of the one or more road segments is part of a road that includes multiple lanes with vehicles traveling in multiple directions, wherein the one road segment corresponds to a subset of the multiple lanes with vehicles traveling in one or more of the multiple directions, and wherein the determining that one or more data samples for the one road segment are unrepresentative based at least in part on the headings associated with the one or more data samples includes determining that data samples whose associated headings do not correspond to the one or more directions are unrepresentative for the one road segment.

36. The method of claim 32 wherein one of the one or more road segments overlaps with one or more other road segments of one or more other roads, wherein the vehicles on the one road segment travel in one or more directions that are distinct from one or more other directions in which vehicles on the other road segments travel, and wherein the determining that one or more data samples for the one road segment are unrepresentative based at least in part on the headings associated with the one or more data samples includes determining that data samples whose associated headings do not correspond to the one or more directions for the one road segment are unrepresentative for the one road segment.

37. The method of claim 11 wherein, for each of one or more of the at least one road segments, the determining of the one or more data samples for the road segment that are unrepresentative of actual vehicle travel on the road segment includes comparing the one or more data samples to at least some of the other data samples for the road segment.

38. The method of claim 11 wherein, for each of one or more of the at least one road segments, the determining of the one or more data samples for the road segment that are unrepresentative of actual vehicle travel on the road segment includes identifying a subset of the actual vehicle travel on the road segment that is of interest or that is not of interest, and determining whether the one or more data samples correspond to the identified subset.

39. The method of claim 11 further comprising receiving one or more indications of a plurality of data samples that each indicate a reported location of a vehicle, and associating each of at least some of the plurality of data samples with at least one of the road segments based at least in part on the reported location of the data sample corresponding to one or more locations associated with each of the at least one road segments.

40. The method of claim 39 wherein the plurality of data samples each having an associated heading of the vehicle for the data sample, and wherein the associating of a data sample with a road segment is further based at least in part on the associated heading of the vehicle for the data sample corresponding to one or more headings associated with the road segment.

41. The method of claim 40 further comprising, for each of at least some of the plurality of data samples, estimating the heading associated with the data sample by using reported locations indicated by multiple data samples corresponding to the vehicle for the data sample.

42. The method of claim 39 wherein the associating of a data sample with a road segment is further based at least in part on one or more travel characteristics of the vehicle for the data sample other than the reported location.

43. The method of claim 39 wherein the associating of a data sample with a road segment is further based at least in part on extending the one or more locations associated with the road segment by one or more distances that are determined based at least in part on an accuracy of the reported location.

44. The method of claim 43 wherein the one or more determined distances by which the one or more locations associated with a road segment are extended for a data sample are based at least in part on a type of source of the data sample.

45. The method of claim 11 wherein, for each of one or more of the at least some road segments, the multiple associated data samples for the road segment further include multiple data samples that each are reported from a traffic sensor monitoring the road segment and each reflect one or more locations on the road segment corresponding to the traffic sensor.

46. The method of claim 45 further comprising receiving one or more indications of a plurality of data samples that each are reported from one of multiple traffic sensors monitoring multiple road segments, and, for each of at least some of the plurality of data samples, associating the data sample with at least one of the road segments based at least in part on the one or more locations reflected by the data sample matching one or more locations associated with each of the at least one road segments.

47. The method of claim 11 wherein, for each of one or more of the at least one road segments, the determining of the one or more data samples for the road segment that are unrepresentative of actual vehicle travel on the road segment includes identifying multiple data samples that are reported by a single vehicle traveling on the road segment, and determining that the identified multiple data samples are unrepresentative based on combining information from the identified multiple data samples.

48. The method of claim 11 wherein, for each of one or more of the at least one road segments, the multiple associated data samples for the road segment each further reflect a reported time at which the vehicle for the data sample is at the reported location, the automatic analyzing of the multiple associated data samples for the road segment further corresponding to a predetermined period of time such that the actual vehicle travel on the road segment is travel during the predetermined period of time.

49. The method of claim 11 further comprising, for each of multiple distinct periods of time, receiving multiple associated data samples for one of the road segments that each reflect a reported location of a vehicle at a reported time on the one road segment during the period of time, and wherein the automatic analyzing is performed for the one road segment for each of the periods of time based on the data samples whose reported times are during the period of time.

50. The method of claim 11 wherein, for each of one or more of the at least one road segments, the determining of the one or more data samples for the road segment that are unrepresentative of actual vehicle travel on the road segment is performed in a substantially realtime manner.

51. The method of claim 50 wherein at least some of the multiple data samples associated with at least some of the road segments are acquired by vehicles traveling on those road segments before the reporting of the data samples by the vehicles, the reporting of the at least some data samples occurring in a substantially realtime manner after acquisition of one or more of the at least one data samples.

12-13. -13. (canceled)

30. (canceled)

52. A non-transitory computer-readable medium whose contents configure a computing device to assess data samples representing traveling vehicles, by performing a method comprising:

receiving an indication of multiple data samples that each reflect reported travel characteristics of one of multiple vehicles traveling on one or more roads, the reported travel characteristics for the data samples reflecting locations of the multiple vehicles and reflecting speeds of the multiple vehicles;

automatically determining whether one or more of the multiple data samples are unrepresentative of actual vehicle travel of interest on the one or more roads, the determining being based at least in part on the travel characteristics; and

providing one or more indications of the data samples that are not determined to be unrepresentative so that the indicated data samples are available for use in facilitating travel on the one or more roads.

53. The non-transitory computer-readable medium of claim 52 wherein the reported travel characteristics of each data sample include a reported location of the vehicle to which the data sample corresponds, and wherein the determining of whether one or more data samples are unrepresentative of actual vehicle travel of interest on the one or more roads includes determining that the one or more data samples are unrepresentative based at least in part on the reported vehicle locations of those data samples not corresponding to locations for actual vehicle travel on the one or more roads.

54. The non-transitory computer-readable medium of claim 53 wherein the determining that the one or more data samples are unrepresentative based at least in part on the reported vehicle locations of those data samples not corresponding to the locations for actual vehicle travel on the one or more roads includes determining that the reported vehicle locations of those data samples do not correspond to predetermined locations of interest for the one or more roads.

55. The non-transitory computer-readable medium of claim 52 wherein the reported travel characteristics of each data sample include a reported heading of travel of the vehicle to which the data sample corresponds, and wherein the determining of whether one or more data samples are unrepresentative of actual vehicle travel of interest on the one or more roads includes determining that the one or more data samples are unrepresentative based at least in part on the reported vehicle travel headings of those data samples not corresponding to one or more headings for actual vehicle travel on the one or more roads.

56. The non-transitory computer-readable medium of claim 55 wherein the determining that the one or more data samples are unrepresentative based at least in part on the reported vehicle travel headings of those data samples not corresponding to the one or more headings for actual vehicle travel on the one or more roads includes determining that the reported vehicle travel headings of those data samples do not correspond to predetermined travel headings of interest for the one or more roads.

57. The non-transitory computer-readable medium of claim 52 wherein the determining of whether one or more data samples are unrepresentative of actual vehicle travel of interest on the one or more roads includes identifying one or more activities in which one or more vehicles are engaged based on the reported travel characteristics from at least one data sample for each of those one or more vehicles and determining that the at least one data samples are unrepresentative based at least in part on the identified activities being activities that are not of interest.

58. The non-transitory computer-readable medium of claim 52 wherein the determining of whether one or more data samples are unrepresentative of actual vehicle travel of interest on the one or more roads includes determining that the one or more data samples are unrepresentative based at least in part on one or more associated vehicle speeds for those data samples not corresponding to at least one vehicle speed for actual vehicle travel on the one or more roads.

59. The non-transitory computer-readable medium of claim 58 wherein the determining that the one or more data samples are unrepresentative based at least in part on the associated vehicle speeds of those data samples not corresponding to the at least one vehicle speed for actual vehicle travel on the one or more roads includes determining that the associated vehicle speeds of those data samples do not correspond to predetermined vehicle speeds of interest for the one or more roads.

60. The non-transitory computer-readable medium of claim 52 wherein the contents are instructions that when executed program the configured computing device to perform the method, and where the computer-readable medium is a memory of the configured computing device.

61. A computing system configured to assess data samples representing traveling vehicles, comprising:

one or more processors;

a first component that is configured to, when executed by at least one of the one or more processors, and for each of multiple roads, receive an indication of multiple data samples for the road that have associated information, each of the multiple data samples being associated with one of multiple vehicles traveling near the road, the associated information for the multiple data samples reflecting vehicle speeds of the multiple vehicles and further reflecting, for each of the multiple data samples, a location near the road of the one associated vehicle for the data sample; and

a data sample filterer component that is configured to, when executed by at least one of the one or more processors, and for each of at least some of the multiple roads,

automatically determine one or more of the multiple data samples for the road to exclude based at least in part on the associated information for the multiple data samples not reflected vehicle location of at least one of the determined data samples not corresponding to vehicle travel of interest on the road; and

provide one or more indications of the multiple data samples for the road other than the determined data samples so that the indicated data samples are available for use in facilitating travel on the road.

62. The computing system of claim 61 wherein, for each of one or more of the at least one multiple roads, the determining of the one or more data samples for the road to exclude includes determining that the reflected vehicle locations of at least one of the determined data samples the road do not match predefined locations for the road and includes determining that one or more vehicle speeds corresponding to at least one of the determined data samples for the road do not match expected vehicle speeds for the road.

63. The computing system of claim 61 wherein the data sample filterer component is further configured to, for each of one or more of the at least one multiple roads, automatically determine one or more data samples for the road that reflect one or more vehicle travel headings that do not correspond to vehicle travel of interest on the road based at least in part on one or more predefined headings for the road.

64. The computing system of claim 61 wherein the first component and the data sample filterer component each include software instructions for execution in memory of the computing system.

65. The computing system of claim 61 wherein the first component consists of a means for, for each of multiple roads, receiving an indication of multiple data samples for the road that each reflect a location of a vehicle near the road, and wherein the data sample filterer component consists of a means for, for each of at least some of the multiple roads, automatically determining one or more of the multiple data samples for the road whose reflected vehicle locations do not correspond to vehicle travel of interest on the road, and providing one or more indications of the multiple data samples for the road other than the determined data samples so that the indicated data samples are available for use in facilitating travel on the road.