DETECTING ANOMALOUS ROAD TRAFFIC CONDITIONS

Abstract

Techniques are described for automatically detecting anomalous road traffic conditions and for providing information about the detected anomalies, such as for use in facilitating travel on roads of interest. Anomalous road traffic conditions may be identified using target traffic conditions for a particular road segment at a particular selected time, such as target traffic conditions that reflect actual traffic conditions for a current or past selected time, and/or target traffic conditions that reflect predicted future traffic conditions for a future selected time. Target traffic conditions may be compared to distinct expected road traffic conditions for a road segment at a selected time, with the expected conditions reflecting road traffic conditions that are typical or normal for the road segment at the selected time. Anomalous conditions may be identified based on sufficiently large differences from the expected conditions, and information about the anomalous conditions may be provided in various ways.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1F illustrate examples of travel route selection based on predicted future traffic conditions.

FIGS. 2A-2J illustrate various graphical representations of predictive models for representing knowledge about traffic conditions in a given geographic area.

FIG. 3 is a block diagram illustrating a computing system suitable for executing an embodiment of the described Predictive Traffic Information Provider system.

FIG. 4 is a flow diagram of an embodiment of a Route Selector routine.

FIGS. 5A-5B are flow diagrams of embodiments of a Dynamic Traffic Predictor routine and an associated Generate Predictions subroutine.

FIG. 6 is a flow diagram of an embodiment of a Traffic Prediction Model Generator routine.

FIGS. 7A-7I illustrate example displays of various traffic-related information using predictions of future traffic conditions.

FIGS. 7J-7K illustrate example displays related to anomalous traffic conditions.

FIG. 8 is a flow diagram of an embodiment of an Anomalous Traffic Conditions Detector routine.

Claims

1. A method for a computing system to automatically identify abnormal traffic conditions on roads so as to facilitate travel on the roads, the method comprising: receiving information describing a network of roads in a geographic area;for each of the roads in the network, identifying multiple segments of the road for which traffic conditions are distinctly tracked;for each of multiple users, receiving a request from the user to be notified of abnormal traffic conditions that occur on one or more indicated road segments; andfacilitating navigation of vehicles over the network of roads using information about automatically identified abnormal traffic conditions on the roads by, for each of at least some of the road segments, obtaining information indicating current actual traffic conditions for the road segment, the current actual traffic conditions including an actual average traffic speed of vehicles traveling on the road segment at a current time;obtaining information indicating expected traffic conditions for the current time for the road segment, the expected traffic conditions reflecting a generated forecast of traffic conditions that includes an expected average traffic speed of vehicles traveling on the road segment at the current time;automatically identifying whether the current actual traffic conditions for the road segment at the current time are abnormal with respect to the expected traffic conditions for the road segment for the current time, the identifying being based at least in part on generated comparative information for the road segment that indicates a difference between the actual and expected average traffic speeds of vehicles traveling on the road segment; andif the current actual traffic conditions for the road segment are identified as abnormal, and if one or more users has requested to be notified of abnormal traffic conditions on the road segment, providing information about the abnormal current actual traffic conditions to each of the one or more users.

2. The method of claim 1 wherein at least some of the received requests from the users each indicate road segments of interest by indicating one or more routes on the network of roads, and wherein the at least some road segments include the indicated road segments of interest.

3. The method of claim 2 wherein the at least some received requests each indicate a notification mechanism to use for notifying of abnormal traffic conditions, and wherein the providing of information about abnormal current actual traffic conditions to a user whose request indicates a notification mechanism is performed in a manner so as to use the indicated notification mechanism.

4. The method of claim 3 wherein the at least some received requests each indicate one or more times of interest, and wherein the providing of information about abnormal current actual traffic conditions to a user whose request indicates one or more times of interest is performed only if the current time is one of the indicated times of interest.

5. The method of claim 4 wherein the facilitating of the navigation of vehicles over the network of roads using information about automatically identified abnormal traffic conditions on the roads is performed repeatedly at each of multiple distinct times such that the current time changes for each performance.

6. The method of claim 1 wherein the generated forecast traffic conditions for the at least some road segments are default forecast traffic conditions generated by one or more predictive models using input information related to traffic conditions at the current time, wherein the input information includes information about time-of-day of the current time, about day-of-week of the current time, about school schedules in the geographic area at the current time, and about holiday schedules in the geographic area at the current time, and wherein the input information does not include information about current conditions at a time of generating the forecast traffic conditions, the current conditions including current traffic conditions, current traffic incidents, and current weather conditions.

7. The method of claim 6 wherein at least one of the one or more predictive models uses a Bayesian network to probabilistically generate the forecast traffic conditions.

8. The method of claim 1 wherein the identifying of whether current actual traffic conditions for a road segment are abnormal based at least in part on generated comparative information for the road segment that indicates a difference between the actual and expected average traffic speeds of vehicles traveling on the road segment includes determining whether the difference exceeds a predetermined amount.

9. The method of claim 1 wherein the providing of information about abnormal traffic conditions to each of one or more users includes at least one of sending an electronic message to the user with the information about the abnormal traffic conditions and initiating a display to the user of the information about the abnormal traffic conditions.

10. A computer-implemented method for automatically identifying abnormal traffic conditions on roads so as to facilitate travel, the method comprising: receiving indications of multiple road segments of multiple related roads;obtaining 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 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 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; andproviding indications of the road segments whose target traffic conditions are determined to be abnormal, so as to facilitate travel on the roads.

11. The method of claim 10 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.

12. The method of claim 10 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.

13. The method of claim 10 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.

14. The method of claim 13 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.

15. The method of claim 13 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.

16. The method of claim 10 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 using one or more statistical measures to determine whether the target traffic conditions for the road segment are abnormal.

17. The method of claim 16 wherein the target and expected traffic conditions for the current time for the multiple road segments are each represented as a distribution of traffic speeds of vehicles traveling on the road segment at the current time, and wherein the one or more statistical measures include at least one statistical difference measure to determine an amount of difference between the target and expected traffic speed distributions for a road segment.

18. The method of claim 16 wherein the target and expected traffic conditions for the current time for the multiple road segments each have an associated probability distribution, and wherein the one or more statistical measures used to determine whether the target traffic conditions for a road segment are abnormal are applied at least in part to the associated probability distributions for the target and expected traffic conditions for the road segment.

19. The method of claim 10 wherein, for each of the multiple road segments, the automatic determining of whether the target traffic conditions for the current time for the road segment are abnormal is further based at least in part on information about traffic conditions for one or more other road segments adjoining the road segment.

20. The method of claim 19 wherein the information about traffic conditions for one or more other road segments adjoining a road segment includes information about abnormal traffic conditions for the current time for the one or more other road segments.

21. The method of claim 10 wherein, for each of the multiple road segments, the automatic determining of whether the target traffic conditions for the current time for the road segment are abnormal is further based at least in part on use of an automated classifier, the classifier using at least one of a probabilistic Bayesian network, a decision tree, a neural network, and a support vector machine.

22. The method of claim 10 wherein the obtained information about the target traffic conditions for at least some of the road segments that reflect actual traffic conditions for the at least some road segments includes measurements of actual traffic conditions on the at least some road segments that are taken within a predetermined amount of time from the current time.

23. The method of claim 10 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.

24. The method of claim 23 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.

25. The method of claim 23 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.

26. The method of claim 23 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, a month-of-year for the current time, a holiday schedule, and a school schedule.

27. The method of claim 23 wherein the at least one predictive model includes a probabilistic Bayesian network.

28. The method of claim 23 wherein the automatic determining that target traffic conditions for one of the road segments are abnormal with respect to normal traffic conditions for the one road segment is performed on behalf of a user, and wherein at least some of the input information to the at least one predictive model is selected by the user.

29. The method of claim 10 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.

30. The method of claim 10 further comprising receiving an indication of a selected past time, comparing actual traffic conditions on each of one or more road segments at the selected past time to normal traffic conditions on that road segment at that past time so as to automatically determine whether the actual traffic conditions at that past time on that road segment are abnormal with respect to the normal traffic conditions at that past time on that road segment, and providing indications of the road segments whose actual traffic conditions at the selected past time are determined to be abnormal.

31. The method of claim 10 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 so as 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 the road segments whose predicted traffic conditions at the selected future time are determined to be abnormal.

32. The method of claim 31 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.

33. The method of claim 31 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.

34. The method of claim 10 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.

35. The method of claim 10 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.

36. The method of claim 10 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.

37. The method of claim 10 wherein one or more users have each requested notification of abnormal traffic conditions for at least one selected road segment, and wherein the providing of the indications of the road segments whose target traffic conditions are determined to be abnormal includes sending one or more electronic messages to each of the one or more users who have selected at least one of the road segments whose target traffic conditions are determined to be abnormal.

38. The method of claim 10 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.

39. The method of claim 10 further comprising, after automatically determining that the target traffic conditions for one or more of the road segments are abnormal, automatically inferring an occurrence of a traffic incident based at least in part on the determination of the abnormality of the target traffic conditions for the one or more road segments.

40. A computer-readable medium whose contents enable a computing device to automatically identify abnormal traffic conditions on roads so as to facilitate travel, by performing a method comprising: obtaining 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 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; andproviding an indication of the identified abnormal traffic condition for the road segment at the indicated time.

41. The computer-readable medium of claim 40 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 as least in part on historical average traffic conditions derived from traffic conditions previously observed on the road segment.

42. The computer-readable medium of claim 40 wherein the first set of traffic conditions data reflects the expected traffic conditions based as least in part on forecasted traffic conditions data generated based on use of a predictive model, and wherein the second set of traffic conditions data includes predicted traffic conditions data based on use of a predictive model that considers current transient conditions.

43. The computer readable medium of claim 40 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.

44. The computer-readable medium of claim 40 wherein the computer-readable medium is at least one of a memory of a computing device and a data transmission medium transmitting a generated data signal containing the contents.

45. The computer-readable medium of claim 40 wherein the contents are instructions that when executed cause the computing device to perform the method.

46. A computing device configured to automatically identify anomalous traffic conditions on roads, comprising: a memory; anda first component configured to, for each of at least one of multiple segments of multiple roads in a geographic area, obtain a first set of expected traffic conditions data for the road segment for an indicated time;obtain a second set of target traffic conditions data for the road segment for the indicated time;detect 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; andprovide an indication of the detected anomalous traffic condition associated with the road segment.

47. The computing device of claim 46 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.

48. The computing device of claim 46 wherein the first component is an anomalous traffic condition detector system.

49. The computing device of claim 46 wherein the first component includes software instructions for execution in the memory of the computing device.

50. The computing device of claim 46 wherein the first component consists of means for, for each of at least one of multiple segments of multiple roads in a geographic area, 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.