Filtering road traffic condition data obtained from mobile data sources

Patent metadata

Field	Value
Publication number	US-9449508-B2
Application number	US-201113194822-A
Country	US
Kind code	B2
Filing date	Jul 29, 2011
Priority date	Mar 3, 2006
Publication date	Sep 20, 2016
Grant date	Sep 20, 2016

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.

First claim

What is claimed is:

1. 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 a first road segment of the one or more road segments,
automatically determining, by one or more configured computing systems, that the multiple data samples associated with the first road segment are not of interest based at least in part on identifying the first road segment as being of a functional road class that is not of interest; and
providing, by the one or more configured computing systems, one or more indications to exclude the multiple data samples associated with the first road segment from later use, to enable other data samples to be available for use in facilitating travel.

2. The method of claim 1 further comprising, for a second road segment of the one or more road segments that is distinct from the first road segment:
automatically analyzing, by the one or more configured computing systems, the multiple associated data samples for the second road segment to determine a subset of those data samples that are unrepresentative of actual vehicle travel on the second road segment, one or more of the data samples of the determined subset each indicating a reported location for an associated vehicle that does not correspond to the actual vehicle travel on the second road segment; and
providing, by the one or more configured computing systems, one or more indications to exclude the data samples of the determined subset from later use, so that the other data samples of the multiple associated data samples for second road segment are available for use in facilitating travel on the second road segment,
and wherein the determining of the subset of data samples for the second road segment includes determining that the reported vehicle locations for the one or more data samples of the subset correspond to a section of road that is not of interest for use in representing the actual vehicle travel on the second road segment.

3. The method of claim 2 wherein the section of road is a subset of lanes from multiple lanes that are part of the second road segment.

4. The method of claim 2 wherein the section of road is at least part of one or more of a ramp for the second road segment, a collector/distributor road associated with the road of the second road segment, a collector/distributor lane associated with the road of the second road segment, a feeder lane associated with the road of the second road segment, a shoulder of the road of the second road segment, and a breakdown area for the road of the second road segment.

5. The method of claim 2 wherein the determining of the subset of data samples from the multiple data samples associated with the second road segment includes selecting those data samples based on those data samples also being associated with one or more other distinct road segments.

6. The method of claim 2 wherein the determining of the subset of data samples includes comparing the data samples of the subset to at least some of other data samples of the multiple data samples associated with the second road segment.

7. The method of claim 2 wherein the determining of the subset of data samples includes identifying a subset of the actual vehicle travel on the second road segment that is of interest or that is not of interest, and determining whether the data samples of the determined subset correspond to the identified subset of the actual vehicle travel.

8. The method of claim 2 wherein the multiple associated data samples for the second road segment further include multiple data samples that each are reported from a traffic sensor monitoring the second road segment and each reflect one or more locations on the second road segment corresponding to the traffic sensor.

9. The method of claim 8 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.

10. The method of claim 2 wherein the determining of the subset of data samples includes identifying multiple data samples that are reported by a single vehicle traveling on the second road segment, and determining that the identified multiple data samples are unrepresentative based on combining information from the identified multiple data samples.

11. The method of claim 2 wherein the multiple associated data samples for the second road segment each further reflect a reported time at which the vehicle for the data sample is at the reported location, and wherein the automatic analyzing of the multiple associated data samples for the second road segment further corresponds to a predetermined period of time such that the actual vehicle travel on the second road segment is travel during the predetermined period of time.

12. The method of claim 1 further comprising automatically determining that the multiple data samples associated with a second road segment of the one or more road segments are not of interest based at least in part on a determination of an amount of actual vehicle traffic on the second road segment, and providing one or more indications to exclude the multiple data samples associated with the second road segment from later use.

13. The method of claim 1 further comprising automatically determining that the multiple data samples associated with a second road segment of the one or more road segments are not of interest based at least in part on a determination of an amount of variability of vehicle traffic on the second road segment, and providing one or more indications to exclude the multiple data samples associated with the second road segment from later use.

14. The method of claim 13 further comprising, before the determining that the multiple data samples associated with the second road segment are not of interest, assessing the variability of the vehicle traffic on the second road segment, and determining that the second road segment is not of interest based at least in part on the assessing.

15. The method of claim 14 wherein the assessing of the variability of the vehicle traffic on the second road segment is based on traffic during each of multiple time periods, and wherein a length of each of the multiple time periods is one of an hour, a day, and a week.

16. The method of claim 14 wherein the determining that the second road segment is not of interest is further based at least in part on an amount of the assessed variability being determined to be low.

17. The method of claim 13 wherein the determined amount of variability of vehicle traffic on the second road segment includes at least one of intra-day variability in the vehicle traffic and inter-day variability in the vehicle traffic.

18. The method of claim 1 further comprising automatically determining that the multiple data samples associated with a second road segment of the one or more road segments are not of interest based at least in part on a determination of an amount of actual traffi

Assignees

• Chapman Craig H
• Downs Oliver B
• Barker Alec
• Burns Jr Mitchel A
• Love Scott R
• Inrix Inc

Inventors

• Chapman Craig H
• Downs Oliver B
• Barker Alec
• Burns Jr Mitchel A
• Love Scott R

Classifications

• G08G1/0133

  for classifying traffic situation · CPC title

• G08G1/0112

  from the vehicle, e.g. floating car data [FCD] · CPC title

• G08G1/052

  with provision for determining speed or overspeed {(speed measuring in general G01P)} · CPC title

• G08G1/056

  with provision for distinguishing direction of travel · CPC title

• H04W88/02

  Terminal devices · CPC title