Assessing road traffic speed using data from multiple data sources

• US 8,909,463 B2
• Filed: 01/31/2014
• Issued: 12/09/2014
• 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.
• Primary Examiner(s): Beaulieu, Yonel
• Application Number:
  US14/170,179
• Publication Number:
  US 20140149028A1
• Time in Patent Office: 312 Days
• Field of Search: 701117-119, 340/998, 340/995.13
• 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, multiple data samples that reflect speeds of multiple vehicles traveling on a road, the multiple data samples including information reported by multiple mobile devices on the road;

automatically analyzing, by the one or more configured computing systems, the multiple data samples to estimate an average traffic speed of vehicles traveling on the road based at least in part on combining the speeds of the multiple data samples in a weighted manner using multiple distinct weights associated with the multiple data samples; and

providing, by the one or more configured computing systems, an indication of the estimated average traffic speed, to enable use of the indicated estimated average traffic speed in facilitating vehicle travel.

2. The computer-implemented method of claim 1 further comprising determining the multiple distinct weights for the multiple data samples based on one or more attributes of the multiple data samples.

3. The computer-implemented method of claim 2 wherein each of the multiple data samples is associated with a reported speed of a vehicle on the road at a reported time, and wherein the one or more attributes of the multiple data samples that are used for the determining of the multiple distinct weights include recency of the reported times associated with the multiple data samples.

4. The computer-implemented method of claim 3 wherein the determining of the multiple distinct weights for the multiple data samples based on recency of the reported times associated with the multiple data samples is performed such that data samples whose reported times are less recent are given less weight than data samples whose reported times are more recent.

5. The computer-implemented method of claim 2 wherein the multiple data samples include data samples supplied from multiple sources, and wherein the one or more attributes of the multiple data samples that are used for the determining of the multiple distinct weights include the sources of the multiple data samples.

6. The computer-implemented method of claim 5 wherein the multiple sources for the multiple data samples include multiple vehicles that are traveling on the road and that each report one or more data samples to reflect a location and/or speed of the vehicle.

7. The computer-implemented method of claim 5 wherein the multiple sources for the multiple data samples include multiple traffic sensors that monitor the road and that each report one or more data samples based on readings that reflect one or more passing vehicles.

8. The computer-implemented method of claim 7 wherein the multiple traffic sensors include at least one of an embedded loop sensor, a camera, a radar ranging device, a motion sensor, and an RFID-based sensor to detect RFID-based tags in vehicles.

9. The computer-implemented method of claim 5 further comprising assessing reliability of each of the multiple sources for the multiple data samples, and wherein the determining of the multiple distinct weights for the multiple data samples based on the sources of the multiple data samples is performed such that data samples whose sources have lower assessed reliability are given less weight than data samples whose sources have higher assessed reliability.

10. The computer-implemented method of claim 2 wherein the one or more attributes of the multiple data samples that are used for the determining of the multiple distinct weights include a total quantity of the multiple data samples.

11. The computer-implemented method of claim 2 wherein the determining of the multiple distinct weights for the multiple data samples is further based on one or more current conditions.

12. The method of claim 11 wherein the current conditions include at least one of a recent traffic accident, a sporting event, a current time-of-day, a current day-of-week, a current day-of-month, a current week-of-month, and a current month-of-year.

13. The computer-implemented method of claim 1 wherein the multiple data samples are associated with a first road segment of the road and the estimated average traffic speed is further associated with the first road segment, wherein multiple additional data samples are received for each of multiple additional road segments, and wherein the method is performed for each of at least some of the additional road segments by analyzing the multiple additional data samples for the additional road segment to estimate an average traffic speed of vehicles traveling on the additional road segment and by providing an indication of the estimated average traffic speed for the additional road segment.

14. The computer-implemented method of claim 13 wherein, for each of one or more of the additional road segments, estimating of the average traffic speed of vehicles traveling on the additional road segment is performed only if a quantity of the additional data samples associated with the additional road segment is sufficiently large that a statistical validity of the estimated average traffic speed exceeds a threshold.

15. The computer-implemented method of claim 1 wherein estimating of the average traffic speed of vehicles traveling on the road is performed with respect to each of multiple distinct periods of time based on analyzing data samples associated with that period of time.

16. The computer-implemented method of claim 1 wherein estimating of the average traffic speed of vehicles traveling on the road is performed with respect to each of multiple overlapping time windows during a period of time, such that at least some of the multiple data samples are used for each of multiple of the time windows.

17. The computer-implemented method of claim 16 wherein, for each of the at least some data samples used for each the multiple overlapping time windows, the data sample is assigned a different weight for each of the multiple overlapping time windows.

18. The computer-implemented method of claim 1 wherein estimating of the average traffic speed of vehicles traveling on the road includes determining a confidence value for the estimated average traffic speed.

19. The computer-implemented method of claim 1 wherein the estimated average traffic speed is associated with a period of time, and wherein the method further comprises inferring traffic volume for the road during the period of time based in part on the estimated average traffic speed.

20. The computer-implemented method of claim 1 wherein estimating of the average traffic speed of vehicles traveling on the road is performed in a substantially realtime manner.

21. The computer-implemented method of claim 1 wherein the providing of the indication of the estimated average traffic speed includes presenting the estimated average traffic speed to operators of vehicles for use in influencing decisions of the operators regarding travel on the road.

22. 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 speeds of multiple vehicles traveling on a road, the multiple data samples including information reported by multiple devices associated with the road;

estimating, by the configured computing device, an average traffic speed of vehicles traveling on the road based at least in part on combining the speeds reflected by the multiple data samples in a weighted manner using multiple distinct weights associated with the multiple data samples; and

providing, by the configured computing device, an indication of the estimated average traffic speed.

23. The non-transitory computer-readable medium of claim 22 wherein each of the multiple data samples is associated with a reported speed of a vehicle on the road at a reported time, and wherein the stored contents further configure the computing device to determine the multiple distinct weights for the multiple data samples in a manner such that data samples whose reported times are less recent are given less weight than data samples whose reported times are more recent.

24. The non-transitory computer-readable medium of claim 22 wherein the multiple data samples include data samples supplied from multiple sources, and wherein the stored contents further configure the computing device to determine the multiple distinct weights for the multiple data samples based at least in part on the sources of the multiple data samples.

25. The non-transitory computer-readable medium of claim 24 wherein the multiple sources for the multiple data samples include multiple vehicles that are traveling on the road and that each report one or more data samples to reflect a location and/or speed of the vehicle.

26. The non-transitory computer-readable medium of claim 24 wherein the multiple sources for the multiple data samples include multiple traffic sensors that monitor the road and that each report one or more data samples based on readings that reflect one or more passing vehicles.

27. The computer-implemented method of claim 24 wherein the stored contents further configure the computing device to assess reliability of each of the multiple sources for the multiple data samples, and wherein the determining of the multiple distinct weights for the multiple data samples is performed such that data samples whose sources have lower assessed reliability are given less weight than data samples whose sources have higher assessed reliability.

28. The computer-implemented method of claim 22 wherein estimating of the average traffic speed of vehicles traveling on the road is performed with respect to each of multiple distinct periods of time based on analyzing data samples associated with that period of time.

29. The computer-implemented method of claim 22 wherein estimating of the average traffic speed of vehicles traveling on the road is performed in a substantially realtime manner.

30. 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 speeds of multiple vehicles traveling on the road segment, the multiple data samples being based at least in part on information reported by multiple mobile devices;

automatically analyze the multiple data samples to estimate an average traffic speed of vehicles traveling on the road segment based at least in part on weighting the speeds reflected by the multiple data samples using multiple distinct weights associated with the multiple data samples; and

provide an indication of the estimated average traffic speed, to enable use of the indicated estimated average traffic speed in facilitating vehicle travel on at least one of the multiple roads.

31. The computing device of claim 30 wherein each of the multiple data samples is associated with a reported speed of a vehicle on the road at a reported time, and wherein the one or more components are further configured to determine the multiple distinct weights for the multiple data samples in a manner such that data samples whose reported times are less recent are given less weight than data samples whose reported times are more recent.

32. The computing device of claim 30 wherein the multiple data samples include data samples supplied from multiple respective sources, and wherein the one or more components are further configured to determine the multiple distinct weights for the multiple data samples based at least in part on the respective sources of the multiple data samples.

33. The computing device of claim 32 wherein the multiple respective sources for the multiple data samples include at least one of a group that includes multiple vehicles that are traveling on the road and multiple traffic sensors that monitor the road.

34. The computing device of claim 32 wherein the one or more components are further configured to assess reliability of each of the multiple sources for the multiple data samples, and wherein the determining of the multiple distinct weights for the multiple data samples is performed such that data samples whose sources have lower assessed reliability are given less weight than data samples whose sources have higher assessed reliability.

35. The computing device of claim 30 wherein the automatic analyzing of the multiple data samples to estimate the average traffic speed is performed in a substantially realtime manner.