Detecting anomalous road traffic conditions

• US 8,682,571 B2
• Filed: 06/20/2013
• Issued: 03/25/2014
• Est. Priority Date: 03/03/2006
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Chapman, Craig H., Parikh, Kush G., Downs, Oliver B., Cahn, Robert C., Hersch, Jesse S.
• Primary Examiner(s): Beaulieu, Yonel
• Application Number:
  US13/923,273
• Publication Number:
  US 20130289862A1
• Time in Patent Office: 278 Days
• Field of Search: 701117-119
• US Class Current: 701/118
• 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, indications of multiple road segments of multiple related roads;

obtaining, by the one or more configured computing systems, information about expected traffic conditions for each of the road segments for a current time, the expected traffic conditions reflecting traffic conditions that are normal for the road segments at the current time;

obtaining, by the one or more configured computing systems, information about target traffic conditions for each of the road segments for the current time for comparison to the expected traffic conditions for the road segments, the target traffic conditions reflecting actual traffic conditions on the road segments;

for each of the multiple road segments, comparing, by the one or more configured computing systems, the target traffic conditions for the road segment for the current time to the expected traffic conditions for the road segment for the current time to automatically determine whether the target traffic conditions are abnormal with respect to normal traffic conditions for the current time; and

providing, by the one or more configured computing systems, indications of the road segments whose target traffic conditions are determined to be abnormal for the current time to facilitate travel on the roads.

2. The computer-implemented method of claim 1 wherein the automatic determining that target traffic conditions for a road segment are abnormal with respect to normal traffic conditions for the road segment includes determining that the target traffic conditions are better than the normal traffic conditions by at least a minimum amount.

3. The computer-implemented method of claim 1 wherein the automatic determining that target traffic conditions for a road segment are abnormal with respect to normal traffic conditions for the road segment includes determining that the target traffic conditions are worse than the normal traffic conditions by at least a minimum amount.

4. The computer-implemented method of claim 1 wherein, for each of the multiple road segments, the comparing of the target traffic conditions for the road segment for the current time to the expected traffic conditions for the road segment for the current time includes generating comparative information that includes a numerical difference between the target and expected traffic conditions for the road segment.

5. The computer-implemented method of claim 4 wherein, for each of one or more of the multiple road segments, the target traffic conditions are determined to be abnormal with respect to normal traffic conditions if the numerical difference between the target and expected traffic conditions for the road segment exceeds a predetermined quantity.

6. The computer-implemented method of claim 4 wherein the providing of the indications of the road segments whose target traffic conditions are determined to be abnormal includes providing indications of the generated comparative information for at least some of the multiple road segments.

7. The computer-implemented method of claim 1 wherein, for each of the multiple road segments, the comparing of the target traffic conditions for the road segment for the current time to the expected traffic conditions for the road segment for the current time includes generating comparative information for the road segment by selecting one of multiple enumerated labels indicating a degree of difference between the target and expected traffic conditions for the road segment.

8. The computer-implemented method of claim 1 wherein the obtained information about expected traffic conditions for at least some of the road segments includes forecasted traffic conditions information based on use of at least one predictive model whose input information includes information about conditions affecting traffic on the roads.

9. The computer-implemented method of claim 8 wherein the obtaining of the information about the expected traffic conditions for the road segments includes generating the information about the expected traffic conditions based at least in part on use of the at least one predictive models.

10. The computer-implemented method of claim 8 wherein the input information to the at least one predictive model does not include multiple of current traffic conditions, current weather conditions, current traffic incidents, future expected weather conditions, and future events that are scheduled to occur.

11. The computer-implemented method of claim 8 wherein the input information to the at least one predictive model includes multiple of a time-of-day for the current time, a day-of-week for the current time, and a school schedule.

12. The computer-implemented method of claim 8 wherein the at least one predictive model includes a probabilistic Bayesian network.

13. The computer-implemented method of claim 1 wherein the obtained information about expected traffic conditions for each of at least some of the road segments includes information about historical average traffic conditions based on an aggregation of actual traffic conditions that have been previously observed on the road segment.

14. The computer-implemented method of claim 1 further comprising receiving an indication of a selected future time, comparing predicted traffic conditions on each of one or more road segments at the selected future time to normal traffic conditions on that road segment at that future time to automatically determine whether the predicted traffic conditions at that future time on that road segment are abnormal with respect to the normal traffic conditions at that future time on that road segment, and providing indications of one or more road segments whose predicted traffic conditions at the selected future time are determined to be abnormal.

15. The computer-implemented method of claim 14 wherein the predicted traffic conditions on each of the one or more road segments at the selected future time are predictions that are generated for the road segment for the future time based in part on current conditions at a time of the generating.

16. The computer-implemented method of claim 14 wherein the normal traffic conditions on each of the one or more road segments at the selected future time are forecasts that are generated for the road segment for the future time without using current traffic conditions at a time of the generating.

17. The computer-implemented method of claim 1 wherein the expected traffic conditions for each of the road segments include expected average traffic speed for the road segment, and wherein the target traffic conditions for each of the road segments include actual average traffic speed for the road segment.

18. The computer-implemented method of claim 1 wherein the expected traffic conditions for each of the road segments include expected traffic volume for the road segment during a period of time, and wherein the target traffic conditions for each of the road segments include actual traffic volume for the road segment during the period of time.

19. The computer-implemented method of claim 1 wherein the expected traffic conditions for each of the road segments include expected traffic occupancy percentage for at least one location of the road segment during a period of time, and wherein the target traffic conditions for each of the road segments include actual traffic occupancy percentage for at least one location of the road segment during the period of time.

20. The computer-implemented method of claim 1 wherein the providing of the indications of one or more of the road segments whose target traffic conditions are determined to be abnormal includes initiating display to each of one or more users of a map that includes representations of the one or more road segments, the map indicating for each of the one or more road segments a numerical difference between the target traffic conditions for the road segment and the expected traffic conditions for the road segment.

21. The computer-implemented method of claim 1 wherein at least some of the multiple road segments are part of an indicated route.

22. The computer-implemented method of claim 21 wherein the indicated route has previously been used by an indicated user, and wherein the obtained information about the expected traffic conditions for the at least some road segments of the indicated route includes information about actual historical traffic conditions for those at least some road segments.

23. The computer-implemented method of claim 22 wherein the received indications of the multiple road segments includes a request, for the indicated user, for determined information about whether the target traffic conditions for the at least some road segments of the indicated route are abnormal with respect to the normal traffic conditions for the at least some road segments of the indicated route.

24. The computer-implemented method of claim 21 wherein the automatic determining of whether the target traffic conditions are abnormal with respect to normal traffic conditions for the current time for the multiple road segments includes determining that the target traffic conditions are abnormal with respect to normal traffic conditions for one or more of the at least some road segments of the indicated route, and wherein the providing of the indications of the road segments whose target traffic conditions are determined to be abnormal includes electronically sending information, for use by a user, to indicate that traffic conditions for the indicated route are abnormal.

25. The computer-implemented method of claim 24 further comprising automatically determining a future time at which predicted traffic conditions for the future time for the at least some road segments of the indicated route are no longer abnormal with respect to the normal traffic conditions for the current time for the at least some road segments of the indicated route, and wherein the information sent electronically includes an indication of the determined future time.

26. The computer-implemented method of claim 24 further comprising automatically determining a future time at which predicted traffic conditions for the future time for the at least some road segments of the indicated route are not abnormal with respect to normal traffic conditions for the future time for the at least some road segments of the indicated route, and wherein the information sent electronically includes an indication of the determined future time.

27. The computer-implemented method of claim 24 further comprising automatically determining an alternate route for the indicated route for which target traffic conditions for a determined time are not abnormal with respect to normal traffic conditions for the determined time for the alternate route, and wherein the information sent electronically includes an indication of at least the determined alternate route.

28. The computer-implemented method of claim 21 wherein the indicated route is automatically determined based at least in part on the indicated route having previously been used by an indicated user.

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

obtaining, by the configured computing device, first and second sets of traffic conditions data for a segment of a road at an indicated time, the data of the first and second sets being for a same type of traffic condition but being generated in distinct manners such that at least one of the first and second sets reflects expected traffic conditions for the road segment at the indicated time;

automatically identifying, by the configured computing device, an abnormal traffic condition associated with the road segment at the indicated time based at least in part on one or more differences between the first and second sets of traffic conditions data; and

providing, by the configured computing device, an indication of the identified abnormal traffic condition for the road segment at the indicated time.

30. The non-transitory computer-readable medium of claim 29 wherein only one of the first and second sets reflects expected traffic conditions for the road segment at the indicated time, and wherein the other of the first and second sets of traffic conditions data includes actual traffic conditions data for the road segment at the indicated time.

31. The non-transitory computer-readable medium of claim 29 wherein the first set of traffic conditions data includes forecasted traffic conditions data generated based on use of a predictive model, and wherein the second set of traffic conditions data reflects the expected traffic conditions based at least in part on historical average traffic conditions derived from traffic conditions previously observed on the road segment.

32. The non-transitory computer-readable medium of claim 29 wherein the computer-readable medium is a memory of the configured computing device, and wherein the contents are instructions that when executed program the configured computing device to perform the method.

33. 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 in a geographic area, identify anomalous traffic conditions on the road segment by;

obtaining a first set of expected traffic conditions data for the road segment for an indicated time;

obtaining a second set of target traffic conditions data for the road segment for the indicated time;

detecting an anomalous traffic condition associated with the road segment at the indicated time based at least in part on a comparison between the target traffic conditions data and the expected traffic conditions data; and

providing an indication of the detected anomalous traffic condition associated with the road segment.

34. The computing device of claim 33 wherein, for each of one or more of the at least one road segments, the first set of expected traffic conditions data for the road segment reflects normal traffic conditions for the road segment at the indicated time, and the second set of target traffic conditions data for the road segment reflects actual traffic conditions for the road segment at the indicated time.

35. The computing device of claim 33 wherein the first component is an anomalous traffic condition detector system that includes software instructions for execution in memory of the computing device.

36. The computing device of claim 33 wherein the one or more components consist of means for performing the identifying of the anomalous traffic conditions on each of the at least one road segments.