Filtering road traffic condition data obtained from mobile data sources

• US 8,014,936 B2
• Filed: 05/31/2006
• Issued: 09/06/2011
• Est. Priority Date: 03/03/2006
• Status: Expired due to Fees

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Love, Scott R., Burns, Mitchel A. Jr., Downs, Oliver B., Barker, Alec, Chapman, Craig H.
• Primary Examiner(s): Tran; Khoi
• Assistant Examiner(s): MOYER, DALE S
• Application Number:
  US11/444,998
• Publication Number:
  US 20070208495A1
• Time in Patent Office: 1,924 Days
• Field of Search: 340/933, 340/995.13, 701/117, 701/209, 701/119, 701/213
• US Class Current: 701/117
• 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. A computer-implemented method for assessing data samples reported by vehicles traveling on roads, the data samples including information regarding the travel of the vehicles, the method comprising:

receiving indications of multiple road segments of one or more roads;

receiving information related to current traffic conditions of the multiple road segments, the received information including a plurality of data samples that each are reported from one of multiple vehicles and reflect a reported speed of the one vehicle at a reported geographic location and reflect a reported travel heading of the one vehicle; and

for each of the multiple road segments, assessing traffic conditions for the road segment based on data samples identified to represent travel on the road segment by,

identifying a group of multiple data samples from the plurality of data samples such that the data samples of the group have reported geographic locations that are within a predefined distance of one or more predefined geographic locations for the road segment and have reported travel headings that are within a predefined difference from one or more predefined headings for the road segment;

automatically determining that one or more of the data samples of the group are unrepresentative of actual vehicle travel on the road segment based at least in part on the reported geographic locations of the determined data samples not corresponding to predefined portions of the road segment for which vehicle travel is of interest and based at least in part on the reported speeds of one or more vehicles that reported the determined data samples not corresponding to a range of speeds associated with the road segment, wherein the predetermined portions of one or more of the multiple road segments that are each part of a highway do not include any ramps on or off the highway and wherein the determined one or more data samples for each of the one or more road segments include at least one data sample having a reported geographic location that is determined to correspond to one the ramps, the automatic determining being performed by one or more configured computing systems;

automatically excluding from the group the data samples determined to be unrepresentative of actual vehicle travel on the road segment, the automatic excluding being performed by the one or more configured computing systems; and

after the excluding, automatically using the data samples remaining in the group to infer traffic conditions for all vehicles traveling on the road segment, so that inferred traffic conditions based on data samples are available for use in facilitating travel on the road segments.

2. The method of claim 1 wherein one of the multiple road segments corresponds to a first section of a highway having multiple lanes, wherein the predefined geographic locations for the one road segment include a geographic area that covers the multiple lanes of the highway for the first section, wherein the predefined distance by which the geographic area is extended is based at least in part on a degree of accuracy of a type of location determination device that is used to determine the reported geographic locations for at least some of the data samples of the identified group for the one road segment, wherein the predefined headings for the one road segment include one or more headings that correspond to a direction of vehicle travel on the multiple lanes of the highway for the first section, and wherein the predefined difference for the one or more predefined headings for the one road segment is based at least in part on a degree of accuracy of a type of heading determination device that is used to determine the reported travel headings for at least some of the data samples of the identified group for the one road segment.

3. The method of claim 2 wherein the predefined portions of the one road segment for which vehicle travel is of interest include one or more lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a collector/distributor road or lane near the highway.

4. The method of claim 2 wherein the predefined portions of the one road segment for which vehicle travel is of interest include a subset of the lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a lane of the highway that is not in the subset of lanes.

5. The method of claim 2 wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample that is from a vehicle traveling on another road segment corresponding to a distinct second section of the highway and that is determined to be incorrectly associated with the one road segment.

6. The method of claim 2 wherein the type of location determination device is a type of Global Positioning System (GPS) device, and wherein the predefined distance by which the geographic area is extended corresponds to a distance within which a reading from a GPS device of the type is accurate.

7. The method of claim 1 wherein each of the plurality of data samples further indicate a reported time associated with the reported speed and reported geographic location and reported travel heading for the data sample, wherein the assessing of the traffic conditions for each of the multiple road segments is performed for each of multiple distinct periods of time, and wherein the identifying of the group of data samples for a road segment for a period of time is further performed such that the identified data samples of the group have reported times that correspond to the period of time.

8. The method of claim 1 wherein data samples related to current traffic conditions of the multiple road segments are repeatedly received to reflect changing traffic conditions, wherein the assessing of the traffic conditions for each of the multiple road segments is performed for recently received data samples in a realtime manner, and wherein the using of data samples remaining in a group to infer traffic conditions for all vehicles traveling on a road segment includes providing information about the inferred traffic conditions to one or more people considering upcoming travel on the road segment.

9. A non-transitory computer-readable medium whose stored contents configure a computing device to assess data samples reported by vehicles traveling on roads, the data samples including information regarding the travel of the vehicles, the assessing of the data samples including performing a method comprising:

receiving indications of multiple road segments of one or more roads;

receiving information related to current traffic conditions of the multiple road segments, the received information including a plurality of data samples that each are reported from one of multiple vehicles and reflect a reported speed of the one vehicle at a reported geographic location and reflect a reported travel heading of the one vehicle; and

for each of the multiple road segments, assessing traffic conditions for the road segment based on data samples identified to represent travel on the road segment by,

identifying a group of multiple data samples from the plurality of data samples such that the data samples of the group have reported geographic locations that are within a predefined distance of one or more predefined geographic locations for the road segment and have reported travel headings that are within a predefined difference from one or more predefined headings for the road segment;

automatically determining that one or more of the data samples of the group are unrepresentative of actual vehicle travel on the road segment based at least in part on the reported geographic locations of the determined data samples not corresponding to predefined portions of the road segment for which vehicle travel is of interest and based at least in part on the report speeds of one or more vehicles that reported the determined data samples not corresponding to one or more expected speeds associated with the road segment, wherein the predefined portions of one or more of the multiple road segments that are each part of a highway do not include any ramps on or off the highway, the automatic determining being performed by the configured computing device;

automatically excluding from the group the data samples determined to be unrepresentative of actual vehicle travel on the road segment, the automatic excluding being performed by the configured computing device; and

after the excluding, automatically using the data samples remaining in the group to infer traffic conditions for all vehicles traveling on the road segment to facilitate travel on the road segments.

10. The non-transitory computer-readable medium of claim 9 wherein one of the multiple road segments corresponds to a first section of a highway having multiple lanes, wherein the predefined geographic locations for the one road segment include a geographic area that covers the multiple lanes of the highway for the first section, wherein the predefined distance by which the geographic area is extended is based at least in part on a degree of accuracy of a type of location determination device that is used to determine the reported geographic locations for at least some of the data samples of the identified group for the one road segment, wherein the predefined headings for the one road segment include one or more headings that correspond to a direction of vehicle travel on the multiple lanes of the highway for the first section, and wherein the predefined difference for the one or more predefined headings for the one road segment is based at least in part on a degree of accuracy of a type of heading determination device that is used to determine the reported travel headings for at least some of the data samples of the identified group for the one road segment.

11. The non-transitory computer-readable medium of claim 10 wherein the predefined portions of the one road segment for which vehicle travel is of interest include one or more lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a ramp on and/or off the highway.

12. The non-transitory computer-readable medium of claim 10 wherein the predefined portions of the one road segment for which vehicle travel is of interest include one or more lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a collector/distributor road or lane near the highway.

13. The non-transitory computer-readable medium of claim 10 wherein the predefined portions of the one road segment for which vehicle travel is of interest include a subset of the lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a lane of the highway that is not in the subset of lanes.

14. The non-transitory computer-readable medium of claim 10 wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample that is from a vehicle traveling on another road segment corresponding to a distinct second section of the highway and that is determined to be incorrectly associated with the one road segment.

15. The non-transitory computer-readable medium of claim 10 wherein the type of location determination device is a type of Global Positioning System (GPS) device, and wherein the predefined distance by which the geographic area is extended corresponds to a distance within which a reading from a GPS device of the type is accurate.

16. The non-transitory computer-readable medium of claim 9 wherein, for each of one or more of the multiple road segments, the automatic determining that the one or more data samples of the group are unrepresentative of actual vehicle travel on the road segment is further based at least in part on one or more travel characteristics of the vehicles that reported those data samples other than reported geographic location and other than reported vehicle speed.

17. The non-transitory computer-readable medium of claim 9 wherein each of the plurality of data samples further indicate a reported time associated with the reported speed and reported geographic location and reported travel heading for the data sample, wherein the assessing of the traffic conditions for each of the multiple road segments is performed for each of multiple distinct periods of time, and wherein the identifying of the group of data samples for a road segment for a period of time is further performed such that the identified data samples of the group have reported times that correspond to the period of time.

18. The non-transitory computer-readable medium of claim 9 wherein data samples related to current traffic conditions of the multiple road segments are repeatedly received to reflect changing traffic conditions, wherein the assessing of the traffic conditions for each of the multiple road segments is performed for recently received data samples in a realtime manner, and wherein the using of data samples remaining in a group to infer traffic conditions for all vehicles traveling on a road segment includes providing information about the inferred traffic conditions to one or more people considering upcoming travel on the road segment.

19. The non-transitory computer-readable medium of claim 9 wherein the computer-readable medium is a memory of the configured computing device, and wherein the contents are instructions that when executed program the computing device to perform the method.

20. A computing system configured to assess data samples reported by vehicles traveling on roads, the data samples including information regarding the travel of the vehicles, the computing system comprising:

one or more processors; and

one or more components that are configured to, when executed by the one or more processors,

receive indications of multiple road segments of one or more roads;

receive information related to current traffic conditions of the multiple road segments, the received information including a plurality of data samples that each are reported from one of multiple vehicles and reflect a reported speed of the one vehicle at a reported geographic location and reflect a reported travel heading of the one vehicle; and

for each of the multiple road segments, assess traffic conditions for the road segment based on data samples identified to represent travel on the road segment by,

identifying a group of multiple data samples from the plurality of data samples such that the data samples of the group have reported geographic locations that are within a predefined distance of one or more predefined geographic locations for the road segment and have reported travel headings that are within a predefined difference from one or more predefined headings for the road segment;

automatically determining that one or more of the data samples of the group are unrepresentative of actual vehicle travel on the road segment based at least in part on the reported geographic locations of the determined data samples not corresponding to predefined portions of the road segment for which vehicle travel is of interest and based at least in part on the reported speeds of one or more vehicles that reported the determined data samples not corresponding to one or more expected speeds associated with the road segment; and

wherein the predefined portions of one or more of the multiple road segments that are each part of a highway do not include any ramps on or off the highway;

automatically excluding from the group the data samples determined to be unrepresentative of actual vehicle travel on the road segment; and

after the excluding, automatically using the data samples remaining in the group to infer traffic conditions for all vehicles traveling on the road segment to facilitate travel on the road segments.

21. The computing system of claim 20 wherein one of the multiple road segments corresponds to a first section of a highway having multiple lanes, wherein the predefined geographic locations for the one road segment include a geographic area that covers the multiple lanes of the highway for the first section, wherein the predefined distance by which the geographic area is extended is based at least in part on a degree of accuracy of a type of location determination device that is used to determine the reported geographic locations for at least some of the data samples of the identified group for the one road segment, wherein the predefined headings for the one road segment include one or more headings that correspond to a direction of vehicle travel on the multiple lanes of the highway for the first section, and wherein the predefined difference for the one or more predefined headings for the one road segment is based at least in part on a degree of accuracy of a type of heading determination device that is used to determine the reported travel headings for at least some of the data samples of the identified group for the one road segment.

22. The computing system of claim 21 wherein the predefined portions of the one road segment for which vehicle travel is of interest include one or more lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a ramp on and/or off the highway.

23. The computing system of claim 21 wherein the predefined portions of the one road segment for which vehicle travel is of interest include one or more lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a collector/distributor road or lane near the highway.

24. The computing system of claim 21 wherein the predefined portions of the one road segment for which vehicle travel is of interest include a subset of the lanes of the highway, and wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample whose reported geographic location is determined to correspond to a lane of the highway that is not in the subset of lanes.

25. The computing system of claim 21 wherein one of the data samples of the identified group for the one road segment that is determined to be unrepresentative of actual vehicle travel on the one road segment is a data sample that is from a vehicle traveling on another road segment corresponding to a distinct second section of the highway and that is determined to be incorrectly associated with the one road segment.

26. The computing system of claim 21 wherein the type of location determination device is a type of Global Positioning System (GPS) device, and wherein the predefined distance by which the geographic area is extended corresponds to a distance within which a reading from a GPS device of the type is accurate.

27. The computing system of claim 20 wherein, for each of one or more of the multiple road segments, the automatic determining that the one or more data samples of the group are unrepresentative of actual vehicle travel on the road segment is further based at least in part on one or more travel characteristics of the vehicles that reported those data samples other than reported geographic location and other than reported vehicle speed.

28. The computing system of claim 20 wherein each of the plurality of data samples further indicate a reported time associated with the reported speed and reported geographic location and reported travel heading for the data sample, wherein the assessing of the traffic conditions for each of the multiple road segments is performed for each of multiple distinct periods of time, and wherein the identifying of the group of data samples for a road segment for a period of time is further performed such that the identified data samples of the group have reported times that correspond to the period of time.

29. The computing system of claim 20 wherein data samples related to current traffic conditions of the multiple road segments are repeatedly received to reflect changing traffic conditions, wherein the assessing of the traffic conditions for each of the multiple road segments is performed for recently received data samples in a realtime manner, and wherein the using of data samples remaining in a group to infer traffic conditions for all vehicles traveling on a road segment includes providing information about the inferred traffic conditions to one or more people considering upcoming travel on the road segment.

30. The computing system of claim 20 wherein the one or more components include software instructions for execution by the one or more processors of the computing system.