Combining Road and Vehicle Traffic Information

• US 20100286899A1
• Filed: 08/15/2008
• Published: 11/11/2010
• Est. Priority Date: 08/16/2007
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Neverov, Gregory, Jain, Ravi, Goel, Samir
• Granted Patent: US 8,880,323 B2
• Time in Patent Office: Days
• US Class Current: 701/119
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/127 to a central station ; Indicators in a central station

Abstract

A computer-implemented method includes obtaining road sensor data reflecting speeds of traffic on road segments, transforming the road sensor data using vehicle probe data for the road segments reflecting vehicle speeds, and producing speed estimates for the road segments using the transformed road sensor data. The method can further include determining speeds for road segments between road sensors by smoothing data from sensors near the road segments.

Claims

1. A computer-implemented method, comprising:

obtaining a plurality of sensor data pairs from a road sensor disposed at a first location along a road segment, each of the sensor data pairs comprising a traffic speed value that reflects speed at the first location of traffic on the road segment and a traffic speed time at which the corresponding traffic speed value was captured;

obtaining a plurality of probe data sets from one or more probes, each of the probe data sets comprising a probe speed value for a probe traveling along the road segment, a location indicator specifying a probe location along the road segment for which the probe speed value was determined, and a time indicator specifying a time for which the probe speed value was determined;

matching one or more sensor data pairs with one or more probe data sets;

performing regression analysis on the matched one or more sensor data pairs and one or more probe data sets to determine a transform to apply to the traffic speed values obtained from the road sensor; and

applying the transform to at least one traffic speed value obtained from the road sensor to provide an updated traffic speed value.

2. The method of claim 1, wherein the one or more probes comprise a communication or navigation system outfitted in a vehicle traveling along the road segment.

3. The method of claim 1, wherein the one or more probes comprise a probe vehicle.

4. The method of claim 1, wherein the one or more probes comprise a mobile device carried by a user in a vehicle traveling along the road segment.

5. The method of claim 1, wherein performing regression analysis comprises performing Bayesian linear regression analysis.

6. The method of claim 5, wherein performing Bayesian linear regression analysis comprises performing Bayesian linear regression analysis with a strong prior that favors a solution of vehicle speed value=traffic speed value+0.

7. The method of claim 1, wherein matching comprises identifying a probe data set whose location indicator specifies a probe location within a threshold distance of the first location and whose time indicator specifies a time that is within a threshold period of the traffic speed time.

8. The method of claim 7, further comprising forming a group of multiple matched sensor data pairs and probe data sets, wherein performing regression analysis comprises performing regression analysis on the group.

9. The method of claim 8, wherein forming the group comprises forming the group based on a time of day to which the traffic speed times in the sensor data pairs and the time indicators in the matched probe data sets correspond.

10. The method of claim 9, wherein the time of day is characterized by a period of time that is longer than the threshold period.

11. The method of claim 1, further comprising:

obtaining other pluralities of sensor data pairs from other road sensors disposed at locations other than the first location along the road segment;

matching other probe data sets with other sensor data pairs received from the other pluralities; and

performing linear regression analysis on the matched other probe data sets and other sensor data pairs to determine other transforms to apply to traffic speed values obtained from each of the other road sensors.

12. The method of claim 11, wherein the road sensor and other road sensors provide superior coverage of the road segment but inferior accuracy in traffic speed values, and the one or more probes provide superior accuracy in vehicle speed values but inferior coverage of the road segment.

13. The method of claim 11, wherein performing regression analysis comprises performing regression analysis at a first period of time;

and applying the transform comprises applying the transform at a second period of time that is subsequent to the first period of time and in substantially real time to obtaining the at least one traffic speed value from the road sensor.

14. The method of claim 13, further comprising receiving a request for a current traffic speed value at the first location and subsequently providing, in real time, the updated traffic speed value.

15. A computer-implemented method, comprising:

obtaining a plurality of sensor data sets from multiple road sensors disposed along a road segment, each of the sensor data sets comprising a traffic speed value that reflects speed at a location of the respective road sensor, a traffic location indicator that specifies the location, and a traffic speed time specifying a time at which the corresponding traffic speed value was captured;

obtaining a plurality of probe data sets from one or more probes, each of the probe data sets comprising a probe speed value for a probe traveling along the road segment, a probe location indicator specifying a location along the road segment for which the probe speed value was determined, and a probe time indicator specifying a time for which the probe speed value was determined; and

conducting smoothing operations on the plurality of sensor data sets and the plurality of probe data sets to predict a vehicle speed at a location disposed between two of the multiple road sensors, and providing the predicted vehicle speed.

16. The method of claim 15, further comprising:

matching one or more sensor data sets with one or more probe data sets for each of the multiple road sensors; and

performing regression analysis on the matched one or more sensor data sets and one or more probe data sets to determine a transform for each of the multiple road sensors;

wherein conducting smoothing operations comprises conducting smoothing operations using values to which one or more determined transforms have been applied.

17. The method of claim 16, wherein performing regression analysis comprises performing Bayesian linear regression analysis.

18. The method of claim 15, wherein conducting smoothing operations comprises determining weights to apply, for a particular road segment portion, to vehicle speeds from each of multiple road sensors disposed along the road segment.

19. The method of claim 18, wherein determining the weights comprises employing a kernel function.

20. The method of claim 19, wherein employing a kernel function comprises employing an exponential kernel function.

21. The method of claim 18, wherein the determined weights comprise a spatial component corresponding to a location of the road segment portion relative to the multiple road sensors, and a temporal component.

22. The method of claim 15, wherein the one or more probes comprise a communication or navigation system outfitted in a vehicle traveling along the road segment.

23. The method of claim 15, wherein the one or more probes comprise a probe vehicle.

24. The method of claim 15, wherein the one or more probes comprise a mobile device carried by a user in a vehicle traveling along the road segment.

25. A computer-implemented method, comprising:

obtaining a plurality of sensor data sets from multiple road sensors disposed along a road segment, each of the sensor data sets comprising a traffic speed value that reflects speed at a location of the respective road sensor, a traffic location indicator that specifies the location, and a traffic speed time specifying a time at which the corresponding traffic speed value was captured;

obtaining a plurality of probe data sets from one or more probes, each of the probe data sets comprising a probe speed value for a probe traveling along the road segment, a probe location indicator specifying a probe location along the road segment for which the probe speed value was determined, and a probe time indicator specifying a time for which the probe speed value was determined;

matching one or more sensor data sets with one or more probe data sets for each of the multiple road sensors;

performing regression analysis on the matched one or more sensor data sets and one or more probe data sets to determine a transform for each of the multiple road sensors; and

applying the transforms to traffic speed values from each of the respective multiple road sensors to form adjusted traffic speed values;

employing machine learning to determine weights to apply, for a particular road segment portion, to adjusted traffic speed values from each of multiple road sensors disposed near the particular road segment portion, in order to predict vehicle speed at the particular road segment portion; and

outputting the predicted vehicle speed.

26. The method of claim 25, wherein determining and applying transforms, and predicting vehicle speed comprise determining and applying transforms, and predicting vehicle speed for a range of times that are within a threshold period of the traffic speed times and probe time indicators of the underlying matched sensor data sets and probe data sets.

27. The method of claim 25, wherein the one or more probes comprise a communication or navigation system outfitted in a vehicle traveling along the road segment.

28. The method of claim 25, wherein the one or more probes comprise a probe vehicle.

29. The method of claim 25, wherein the one or more probes comprise a mobile device carried by a user in a vehicle traveling along the road segment.

30. A computer-implemented method, comprising:

obtaining road sensor data reflecting speeds of traffic on road segments;

transforming the road sensor data using vehicle probe data for the road segments reflecting vehicle speeds; and

producing speed estimates for the road segments using the transformed road sensor data.

31. The method of claim 30, wherein obtaining the road sensor data comprises combining road sensor data from a plurality of data providers

32. The method of claim 30, wherein transforming the road sensor data comprises cleaning the road sensor data using Bayesian linear regression against the probe data.

33. The method of claim 30, wherein the vehicle probe data used to transform the road sensor data is matched in time and location to the road sensor data.

34. The method of claim 30, further comprising determining speeds for road segments between road sensors by smoothing data from sensors near the road segments.

35. The method of claim 34, wherein the speeds for road segments between road sensors are determined using machine learning from training data.

36. A computer-implemented method, comprising:

obtaining traffic speed data from a first type of traffic sensors and a different, second type of traffic sensors;

transforming the traffic speed data from the first type of traffic speed sensors using the traffic speed data from the second type of traffic speed sensors; and

producing speed estimates for the road segments using the transformed traffic speed data.

37. The method of claim 36, wherein the first type of traffic sensor has superior coverage but inferior accuracy, and the second type of traffic sensor has inferior coverage but superior accuracy.

38. A computer-implemented system, comprising:

computer memory holding road sensor data reflecting speeds of traffic on road segments;

computer memory holding vehicle probe data; and

a processor programmed to transform the road sensor data using the vehicle probe data.