REPRESENTATIVE ROAD TRAFFIC FLOW INFORMATION BASED ON HISTORICAL DATA

• US 20080071466A1
• Filed: 08/07/2007
• Published: 03/20/2008
• Est. Priority Date: 08/18/2006
• Status: Active Grant

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Incorporated
• Inventors: Chapman, Craig, Downs, Oliver, Hersch, Jesse
• Granted Patent: US 7,908,076 B2
• Time in Patent Office: Days
• US Class Current: 701/117
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions

Abstract

Techniques are described for automatically analyzing historical information about road traffic flow in order to generate representative information regarding current or future road traffic flow, and for using such generated representative traffic flow information. Representative traffic flow information may be generated for a variety of types of useful measures of traffic flow, such as for average speed at each of multiple road locations during each of multiple time periods. Generated representative traffic flow information may be used in various ways to assist in travel and for other purposes, such as to determine likely travel times and plan optimal routes. The historical traffic data used to generate the representative traffic flow information may include data readings from physical sensors that are near or embedded in the roads, and/or data samples from vehicles and other mobile data sources traveling on the roads.

Claims

1. A method for a computing system to facilitate navigation of roads by vehicles based on generated representative traffic flow information for 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 predefined road links on the roads for which traffic flow is distinctly tracked;

retrieving historical traffic data that reflects prior vehicle travel on the roads in the network, the historical traffic data including numerous data samples that each report a speed of traffic on one of the road links at an indicated prior time, the speed of traffic on the one road link at the indicated prior time being influenced at least in part on one or more traffic-altering conditions at the prior time;

determining multiple traffic flow aggregation classifications for which representative traffic flow information will be distinctly generated for the roads, each aggregation classification corresponding to a distinct combination of one or more time periods based on day-of-week and time-of-day information and of one or more of multiple temporary traffic-altering conditions that affect traffic in the geographic area;

automatically generating representative traffic flow information for the roads by, for each of the identified road links and each of the aggregation classifications;

selecting the data samples of the retrieved historical traffic data for the road link that correspond to the aggregation classification based on the indicated prior times of the selected data samples matching the one or more time periods for the aggregation classification and based on the one or more traffic-altering conditions at the indicated prior times matching the one or more traffic-altering conditions for the aggregation classification; and

determining representative traffic flow information for the road link and aggregation classification by aggregating the selected data samples so as to determine a typical average speed on the road link for the aggregation classification and so as to determine one or more variability measure values for the reported speeds of the selected data samples; and

facilitating navigation of vehicles over the network of roads based on the generated representative traffic flow information for the roads by providing the generated representative traffic flow information to each of multiple client devices so that users of the client devices may determine likely travel times over the roads at various times and for various of the traffic-altering conditions based on the typical traffic speeds on the roads at those times and for those traffic-altering conditions.

2. The method of claim 1 further comprising, under control of one of the client devices:

receiving and storing the provided generated representative traffic flow information; and

at each of multiple times,

determining one or more of the road links of interest for possible travel;

determining current traffic-altering conditions;

determining one or more of the aggregation classifications that correspond to the time and to the determined traffic-altering conditions;

retrieving the stored representative traffic flow information for the determined road links and determined aggregation classifications, so as to identify the determined typical average speed for the determined road links under the determined current traffic-altering conditions; and

presenting the retrieved representative traffic flow information for the determined road links and determined aggregation classifications to a user of the device for use in facilitating navigation over at least some of the determined road links.

3. The method of claim 2 wherein the generating of the representative traffic flow information for the roads is performed under control of an automated representative traffic information provider system, wherein the generated representative traffic flow information is provided to the one client device from the representative traffic information provider system via storage of the generated representative traffic flow information on a physical computer-readable medium that is accessible to the one client device, and wherein the method further comprises, under control of the one client device:

receiving an indication to dynamically obtain updated representative traffic flow information via a network connection;

interacting with the representative traffic information provider system over the network connection to dynamically obtain the updated representative traffic flow information; and

storing the updated representative traffic flow information for use in lieu of the provided representative traffic flow information to which the updated representative traffic flow information corresponds.

4. The method of claim 1 wherein the time periods each include a distinct combination of a day-of-week and a time-of-day duration of one or more minutes on that day-of-week, and wherein the multiple traffic-altering conditions that affect traffic in the geographic area include multiple weather-related statuses, multiple holiday-related statuses, and multiple season-based statuses, such that the multiple aggregation classifications include a distinct aggregation classification for each distinct combination of day-of-week, time-of-day duration, weather-related status, holiday-related status, and season-based status.

5. The method of claim 4 wherein the determined typical average speed for a road link and aggregation classification is based on a median of the reported speeds of the selected data samples for that road link and aggregation classification, wherein the one or more variability measure values of reported speeds of selected data samples include speeds corresponding to multiple percentiles other than 50^th percentile, and wherein the determined variability measure values of the generated representative traffic flow information are further for use in determining likely variability of typical travel times over the roads at various times and for various of the traffic-altering conditions.

6. The method of claim 5 wherein the determining of representative traffic flow information for a road link and aggregation classification based on aggregated selected data samples includes using the aggregated selected data samples only if the aggregated selected data samples are automatically determined to provide sufficient temporal variability and sufficient statistical error confidence, and, if the aggregated selected data samples are not automatically determined to provide sufficient temporal variability and sufficient statistical error confidence, determining the representative traffic flow information for a road link and aggregation classification based at least in part on other selected data samples that correspond to one or more of a road segment that includes the road link and one or more other road links that have similar traffic flow patterns, other nearby road links, and other aggregation classifications that are related to the aggregation classification.

7. The method of claim 6 wherein the network of roads includes roads for which traffic sensors are available to provide information about current traffic flow, wherein the numerous data samples include data samples provided by the traffic sensors, and wherein the numerous data samples further include data samples provided by multiple vehicles traveling on the roads.

8. A computer-implemented method for generating representative traffic flow information for roads to facilitate future travel, the method comprising:

receiving an indication of a location on a road in a geographic area;

selecting multiple time-based categories for which representative traffic flow information will be distinctly generated for the road location, the multiple time-based categories each corresponding to one or more time periods based on day-of-week and time-of-day information;

selecting multiple other condition-based categories for which representative traffic flow information will be distinctly generated for the road location, the multiple condition-based categories each corresponding to at least one of multiple variable traffic-altering conditions that affect traffic in the geographic area;

obtaining one or more prior traffic flow values for the road location at each of multiple distinct prior times, at least some of the prior traffic flow values corresponding to one or more of the multiple traffic-altering conditions;

automatically generating representative traffic flow information for the road location by;

for each of the at least some prior traffic flow values, associating the prior traffic flow value with at least one of the time-based categories and at least one of the condition-based categories, the at least one time-based categories being determined by matching the prior time to time periods to which the time-based categories correspond, and the at least one condition-based categories being determined by matching the one or more traffic-altering conditions to which the prior traffic flow value corresponds to traffic-altering conditions to which the condition-based categories correspond; and

for each of one or more traffic flow aggregation classifications that each includes at least one of the time-based and condition-based categories, generating representative traffic flow information for traffic at the road location corresponding to the traffic flow aggregation classification, the generating of the representative traffic flow information being based at least in part on aggregating the prior traffic flow values associated with the at least one categories and on determining one or more typical traffic flow values based on the aggregated traffic flow values; and

providing one or more indications of the generated representative traffic flow information for the road location for use in facilitating travel on the road at future times.

9. The method of claim 8 further comprising, after the automatic generating of the representative traffic flow information for the road location, determining likely traffic flow for the road location at an indicated future time by:

determining one of the time-based categories associated with the indicated future time;

determining one of the condition-based categories related to traffic on the road location at the indicated future time;

retrieving generated representative traffic flow information for the road location and for an aggregation classification that includes the determined one time-based category and the determined one condition-based category; and

providing the retrieved representative traffic flow information to indicate the determined likely traffic flow for the road location at the indicated future time.

10. The method of claim 8 further comprising, at a time after the automatic generating of the representative traffic flow information for the road location, determining likely current traffic flow for the road location by:

determining a current time and a current traffic-altering condition that affects traffic in the geographic area at the current time;

selecting an aggregation classification that includes one of the time-based categories to which the determined current time corresponds and one of the condition-based categories to which the determined current traffic-altering condition corresponds;

retrieving the generated representative traffic flow information for the road location that corresponds to the selected aggregation classification; and

providing the retrieved representative traffic flow information to indicate the determined likely traffic flow for the road location at the current time.

11. The method of claim 8 wherein each of the time-based categories corresponds to one or more days-of-week and to one or more time-of-day periods on the one of more days-of-week, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the time-based categories based at least in part on the prior time being on a day-of-week and during a time-of-day period that matches the corresponding days-of-week and time-of-day periods for that one time-based category.

12. The method of claim 11 wherein each of the time-based categories corresponds to one day-of-week and to one hour-long time-of-day period on the one day-of-week, such that 168 time-based categories are used to represent the 24 one-hour-long time-of-day periods for the 7 day-of-week days.

13. The method of claim 11 wherein the time-based categories each correspond to one of multiple time-of-day periods whose starting times differ by at most 5 minutes, such that at least 288 time-based categories are used to represent times during a day.

14. The method of claim 11 further comprising, before selecting the multiple time-based categories, receiving a request that specifies the one or more days-of-week and the one or more time-of-day periods on the one of more days-of-week for each of the multiple distinct time-based categories, and wherein the selecting of the multiple time-based categories includes defining the multiple time-based categories based on the request.

15. The method of claim 8 wherein at least some of the condition-based categories each corresponds to one of multiple seasons, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the condition-based categories based at least in part on a season at that prior time matching a corresponding season for that one condition-based category.

16. The method of claim 15 further comprising, before selecting the multiple condition-based categories, receiving a request to specify multiple distinct seasons that each correspond to multiple days, and wherein the selecting of the multiple condition-based categories includes defining the seasons for the condition-based categories based on the request.

17. The method of claim 8 wherein each of the time-based categories further corresponds to one or more seasons, such that the associating of a prior traffic flow value for a prior time at the road location further includes associating that prior traffic flow value with one of the time-based categories based at least in part on a season at that prior time matching a corresponding season for that one time-based category.

18. The method of claim 8 wherein at least some of the condition-based categories each corresponds to one of multiple holiday-based conditions, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the condition-based categories based at least in part on a match between a condition at the prior time related to an occurrence of a holiday and a corresponding holiday occurrence condition for that one condition-based category.

19. The method of claim 18 wherein the multiple holiday-based conditions include a first type corresponding to major holiday days observed by a substantial majority of people in the geographic area, a second type corresponding to minor holiday days observed by a substantial minority of people in the geographic area, a third type corresponding to proximate holiday days that are sufficiently close to a major holiday day that a substantial portion of people in the geographic area do not work on the proximate holiday days, and a fourth type corresponding to non-holiday days in the geographic area that are not any of a major holiday day, a minor holiday day, and a proximate holiday day in the geographic area.

20. The method of claim 18 wherein the multiple holiday-based conditions include a first holiday type during which road traffic in the geographic area increases relative to a non-holiday day in the geographic area, a second holiday type during which road traffic in the geographic area decreases relative to a non-holiday day in the geographic area, and a third type for non-holiday days.

21. The method of claim 18 further comprising, before selecting the multiple condition-based categories, receiving a request to specify days that correspond to each of the multiple holiday-based conditions, and wherein the selecting of the multiple condition-based categories includes defining the multiple holiday-based conditions for the condition-based categories based on the request.

22. The method of claim 8 wherein each of the time-based categories further corresponds to one or more holiday types or to a non-holiday, such that the associating of a prior traffic flow value for a prior time at the road location further includes associating that prior traffic flow value with one of the time-based categories based at least in part on a match between a non-holiday or a type of holiday at the prior time and a corresponding non-holiday or holiday type for that one time-based category.

23. The method of claim 8 wherein at least some of the condition-based categories each corresponds to one of multiple weather-based conditions, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the condition-based categories based at least in part on weather at that prior time matching corresponding weather for that one condition-based category.

24. The method of claim 8 wherein at least some of the condition-based categories each corresponds to one of multiple conditions related to occurrences of non-periodic events, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the condition-based categories based at least in part on a match between a condition at that prior time related to an occurrence of a non-periodic event and a corresponding non-periodic event occurrence condition for that one condition-based category.

25. The method of claim 8 wherein at least some of the condition-based categories each corresponds to one of multiple conditions related to occurrences of traffic accidents, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the condition-based categories based at least in part on a match between a condition at that prior time related to an occurrence of a traffic accident and a corresponding traffic accident occurrence condition for that one condition-based category.

26. The method of claim 8 wherein at least some of the condition-based categories each corresponds to one of multiple conditions related to occurrences of road work, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the condition-based categories based at least in part on a match between a condition at that prior time related to an occurrence of road work and a corresponding road work occurrence condition for that one condition-based category.

27. The method of claim 8 wherein at least some of the condition-based categories each corresponds to one of multiple conditions related to occurrences of school sessions, such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the condition-based categories based at least in part on a match between a condition at that prior time related to an occurrence of school sessions and a corresponding school session occurrence condition for that one condition-based category.

28. The method of claim 8 wherein the time-based categories and the condition-based categories are independent of each other such that the associating of a prior traffic flow value for a prior time at the road location includes associating that prior traffic flow value with one of the time-based categories and with one of the condition-based categories.

29. The method of claim 8 wherein the automatic generating of the representative traffic flow information for the road location and the providing of the indications of the generated representative traffic flow information for the road location is performed under control of a server computing system remote from multiple client devices, wherein the indications of the generated representative traffic flow information for the road location are provided to the multiple client devices for local use by the client devices in facilitating travel on the road at future times, and wherein the method further comprises, after the providing of the indications of the generated representative traffic flow information for the road location and under control of one of the client devices:

for each of multiple future times, determining likely traffic flow at the future time for the road location by retrieving the provided generated representative traffic flow information for the road location from one or more local storage locations; and

for each of one or more other future times, determining at the future time likely traffic flow at the future time for the road location by dynamically interacting with the remote server computing system to obtain updated information regarding likely traffic flow at the future time for the road location.

30. The method of claim 29 wherein the providing of the indications of the generated representative traffic flow information for the road location to a client device includes storing the generated representative traffic flow information for the road location on one or more non-volatile storage devices that are accessible to the client device.

31. The method of claim 29 wherein the obtaining of updated information by dynamic interacting with the remote server computing system has greater costs than the retrieving of information from the one or more local storage locations, and wherein the method further comprises automatically determining for a future time whether benefits from having updated information regarding likely traffic flow at that future time for the road location exceed the greater costs of obtaining that updated information.

32. The method of claim 8 wherein the automatic generating of the representative traffic flow information for the road location and the providing of the indications of the generated representative traffic flow information for the road location is performed under control of a server computing system remote from multiple client devices, wherein the indications of the generated representative traffic flow information for the road location are provided to the multiple client devices for local use by the client devices in facilitating travel on the road at future times, and wherein the method further comprises, at a time after the providing of the indications of the generated representative traffic flow information for the road location and under control of one of the client devices:

determining a current time and a current traffic-altering condition that affects traffic in the geographic area at the current time;

selecting an aggregation classification that includes one of the time-based categories to which the determined current time corresponds and one of the condition-based categories to which the determined current traffic-altering condition corresponds;

retrieving the generated representative traffic flow information for the road location that corresponds to the selected aggregation classification; and

providing the retrieved representative traffic flow information to indicate the determined likely traffic flow for the road location at the current time.

33. The method of claim 32 wherein the providing of the indications of the generated representative traffic flow information for the road location to a client device includes storing the generated representative traffic flow information for the road location on one or more non-volatile storage devices that are accessible to the client device.

34. The method of claim 32 wherein the determining of the current traffic-altering condition that affects traffic in the geographic area at the current time includes dynamically interacting with the remote server computing system to obtain an indication of the determined current traffic-altering condition.

35. The method of claim 8 wherein the generating of the representative traffic flow information for traffic at the road location for each of the one or more aggregation classifications includes generating one of more indications of reliability of at least one of the determined typical values.

36. The method of claim 35 wherein, for each of the one or more aggregation classifications, the aggregation classification has multiple associated prior traffic flow values for a traffic flow measurement for multiple prior times, the determined typical values for the traffic flow information for the aggregation classification indicating a most likely value for the traffic flow measurement for the aggregation classification, and the one or more indications of reliability are based at least in part on a statistical analysis of the multiple traffic flow measurement values for the multiple prior times.

37. The method of claim 35 wherein, for each of the one or more aggregation classifications, the aggregation classification has multiple associated prior traffic flow values for a traffic flow measurement for multiple prior times, the determined typical values for the traffic flow information for the aggregation classification indicating an average traffic flow measurement value that is based substantially on the 50^th percentile for the multiple prior traffic flow values, and the one or more indications of reliability including multiple determined traffic flow measurement values for the aggregation classification other than the average value that are based substantially on multiple other percentiles for the multiple prior traffic flow values.

38. The method of claim 37 further comprising, before the generating of the representative traffic flow information for traffic at the road location, receiving a request that specifies the multiple other percentiles, and wherein determining of the multiple traffic flow measurement values based substantially on the multiple other percentiles is based on the request.

39. The method of claim 35 wherein, for each of the one or more aggregation classifications, the aggregation classification has multiple associated prior traffic flow values for a traffic flow measurement for multiple prior times, the determined typical values for the traffic flow information for the aggregation classification indicating a median traffic flow measurement value based on the multiple prior traffic flow values, and the one or more indications of reliability including multiple deviation indications that each indicate a likelihood that an actual value for the traffic flow measurement for the road location at a future time that corresponds to the aggregation classification will deviate from a median traffic flow measurement value by at least a specified amount.

40. The method of claim 39 further comprising, before the generating of the representative traffic flow information for traffic at the road location for each of the one or more aggregation classifications, receiving a request that specifies one or more amounts of deviation from a median value and/or one or more degrees of likelihood, and wherein the generating of the multiple deviation indications is based on the request.

41. The method of claim 35 wherein the generated one or more indications of reliability of at least one of the determined typical values for an aggregation classification are for use by a client in determining a route that includes the road location and that is robust when traffic flow conditions vary from average traffic flow conditions.

42. The method of claim 35 wherein the generated one or more indications of reliability of at least one of the determined typical values for an aggregation classification are for use by a client in determining a route that includes the road location and that is a fastest route in an indicated situation in which traffic flow conditions differ from average traffic flow conditions.

43. The method of claim 8 further comprising:

receiving multiple requests that are each from a client regarding at least one indicated type of analysis of at least some of the prior traffic flow values for the road location; and

for each of the requests, after performing one or more analyses that correspond to the at least one indicated type of analysis for the request, providing information to the client for the request based on the one or more performed analyses.

44. The method of claim 8 wherein the generating of the representative traffic flow information for the road location is based at least in part on a request received from a client, the request indicating information on which the generating of the representative traffic flow information for the road location is based that includes at least one of the road location, one or more of the multiple prior times, one or more of the time periods to which one or more of the multiple time-based categories correspond, and one or more of the multiple variable traffic-altering conditions to which one or more of the multiple condition-based categories correspond, and wherein the providing of the one or more indications of the generated representative traffic flow information for the road location includes providing the generated representative traffic flow information for the road location to the client.

45. The method of claim 8 wherein the generating of the representative traffic flow information for the road location and an aggregation classification based on aggregated prior traffic flow values includes using the aggregated traffic flow values only if the prior times for the aggregated traffic flow values are automatically determined to include sufficient temporal diversity.

46. The method of claim 45 wherein automatic determining that the prior times for the aggregated traffic flow values include sufficient temporal diversity includes calculating a temporal statistical entropy of the aggregated traffic flow values and determining that the calculated temporal statistical entropy exceeds a minimum threshold.

47. The method of claim 45 further comprising, if the prior times for the aggregated traffic flow values for the road location and an aggregation classification are not determined to include sufficient temporal diversity, generating the representative traffic flow information for the road location and the aggregation classification based at least in part on an expanded group of traffic flow values that corresponds to one or more other related road locations proximate to the road location.

48. The method of claim 45 further comprising, if the prior times for the aggregated traffic flow values for the road location and an aggregation classification are not determined to include sufficient temporal diversity, generating the representative traffic flow information for the road location and the aggregation classification based at least in part on an expanded group of traffic flow values that corresponds to one or more other related aggregation classifications that include one or more other time-based categories corresponding to one or more other time periods related to the one or more time periods to which the aggregation classification corresponds.

49. The method of claim 45 further comprising, if the prior times for the aggregated traffic flow values for the road location and an aggregation classification are not determined to include sufficient temporal diversity, generating the representative traffic flow information for the road location and the aggregation classification based at least in part on an expanded group of traffic flow values that corresponds to one or more other related aggregation classifications that include one or more other condition-based categories corresponding to one or more other traffic-alerting conditions related to the at least one traffic-altering condition to which the aggregation classification corresponds.

50. The method of claim 8 wherein the road location includes at least one of a road link and a road segment.

51. The method of claim 8 wherein the automatic generating of the representative traffic flow information is performed for each of multiple locations on multiple roads that are part of a network of roads in the geographic area.

52. The method of claim 8 wherein the traffic flow values each correspond to speed of vehicle travel on the road location.

53. The method of claim 8 wherein the generated representative traffic flow information for the road location reflects predictions of future traffic flow values for the road location.

54. The method of claim 8 wherein the prior traffic flow values include traffic flow values generated by one or more road sensors for the road location and traffic flow values provided by one or more vehicles traveling on the road proximate to the road location.

55. A computer-readable medium whose contents enable a computing device to generate representative traffic flow information for roads, by performing a method comprising:

selecting multiple condition-based categories for which representative traffic flow information will be distinctly generated for a road location, the multiple condition-based categories each corresponding to at least one of multiple variable traffic-altering conditions;

obtaining historical traffic flow values indicating prior traffic flow for the road location at each of multiple distinct prior times, each of at least some of the historical traffic flow values corresponding to one or more of the multiple traffic-altering conditions;

associating the obtained traffic flow values with the condition-based categories by, for each of the at least some historical traffic flow values, associating the historical traffic flow value with at least one condition-based category having a corresponding traffic-altering condition that matches at least one of the one or more traffic-altering conditions to which the historical traffic flow value corresponds; and

for each of one or more of the condition-based categories, generating representative traffic flow information for traffic at the road location that occurs during the one or more traffic-altering conditions corresponding to the category by aggregating the traffic flow values associated with the category and by determining one or more typical traffic flow values based on the aggregated traffic flow values.

56. The computer-readable medium of claim 55 wherein the method further comprises selecting multiple time-based categories for which representative traffic flow information will be distinctly generated for the road location, the multiple time-based categories each corresponding to a time period based on day-of-week and time-of-day information, wherein the generating of the representative traffic flow information for a condition-based category further includes determining a typical traffic flow value for traffic at the road location that occurs during the at least one traffic-altering condition corresponding to the category for each of the multiple time periods corresponding to the multiple time-based categories, and wherein the method further comprises providing one or more indications of the generated representative traffic flow information for the road location for use in facilitating travel on the road.

57. The computer-readable medium of claim 55 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.

58. The computer-readable medium of claim 55 wherein the contents are instructions that when executed cause the computing device to perform the method.

59. The computer-readable medium of claim 55 wherein the generating of the representative traffic flow information for a condition-based category further includes determining a typical traffic flow value for traffic at the road location that occurs during each of multiple time periods with the one or more traffic-altering conditions corresponding to the category, and wherein the contents include one or more data structures including multiple entries corresponding to generated representative traffic flow information, each of the entries corresponding to a road location and one or more traffic-altering conditions and one or more time periods so as to store one or more determined typical traffic flow values for the road location during the one or more time periods and during the one or more traffic-altering conditions.

60. A computing device configured to generate representative traffic flow information for roads, comprising:

one or more memories; and

a representative traffic information provider system configured to automatically provide representative traffic flow information for multiple locations on one or more roads by;

associating historical traffic flow values that indicate prior traffic flow for the multiple road locations at multiple prior times with multiple traffic flow aggregation classifications that represent distinct representative traffic flow information, each of at least some of the historical traffic flow values being associated with one of the road locations and corresponding to prior traffic flow at the one road location that reflects one or more of multiple traffic-altering conditions at one of the multiple prior times, each aggregation classification corresponding to at least one time period and to at least one of the multiple variable traffic-altering conditions, the associating including, for each of the at least some historical traffic flow values, associating the historical traffic flow value with at least one aggregation classification having a corresponding time period to which the prior time for the historical traffic flow value corresponds and having a corresponding traffic-altering condition that matches at least one of the one or more traffic-altering conditions reflected by the prior traffic flow to which the historical traffic flow value corresponds;

for each of one or more combinations of one of the multiple road locations and one of the multiple aggregation classifications, generating representative traffic flow information for traffic at the road location that occurs during the time period and reflects the one or more traffic-altering conditions corresponding to the aggregation classification by aggregating the traffic flow values associated with the aggregation classification and with the road location and by determining one or more typical traffic flow values based on the aggregated traffic flow values; and

providing one or more indications of the generated representative traffic flow information for use in facilitating travel on the one or more roads.

61. The computing device of claim 60 wherein the representative traffic information provider system includes software instructions for execution by the computing device.

62. The computing device of claim 60 wherein the representative traffic information provider system consists of a means for automatically providing representative traffic flow information for multiple locations on one or more roads by:

associating historical traffic flow values that indicate prior traffic flow for the multiple road locations at multiple prior times with multiple traffic flow aggregation classifications that represent distinct representative traffic flow information, each of at least some of the historical traffic flow values being associated with one of the road locations and corresponding to prior traffic flow at the one road location that reflects one or more of multiple traffic-altering conditions at one of the multiple prior times, each aggregation classification corresponding to at least one time period and to at least one of the multiple variable traffic-altering conditions, the associating including, for each of the at least some historical traffic flow values, associating the historical traffic flow value with at least one aggregation classification having a corresponding time period to which the prior time for the historical traffic flow value corresponds and having a corresponding traffic-altering condition that matches at least one of the one or more traffic-altering conditions reflected by the prior traffic flow to which the historical traffic flow value corresponds;

for each of one or more combinations of one of the multiple road locations and one of the multiple aggregation classifications, generating representative traffic flow information for traffic at the road location that occurs during the time period and reflects the one or more traffic-altering conditions corresponding to the aggregation classification by aggregating the traffic flow values associated with the aggregation classification and with the road location and by determining one or more typical traffic flow values based on the aggregated traffic flow values; and

providing one or more indications of the generated representative traffic flow information for use in facilitating travel on the one or more roads.