Method and system for modeling and processing vehicular traffic data and information and applying thereof

• US 8,918,278 B2
• Filed: 11/08/2005
• Issued: 12/23/2014
• Est. Priority Date: 08/28/2000
• Status: Expired due to Term

• Current Assignee: Inrix Incorporated
• Original Assignee: Inrix Global Services Limited
• Inventors: Feldman, Israel, Trinker, Arie, Meltzer, Yochai, Eshpar, Allon, Lotem, Amnon
• Primary Examiner(s): MANCHO, RONNIE M
• Application Number:
  US11/269,908
• Publication Number:
  US 20060111833A1
• Time in Patent Office: 3,332 Days
• Field of Search: 701/207, 701/211, 701/117, 701/118, 701/200, 701/408, 701/409, 701/410, 701/431, 701/450, 701/119, 701/452, 701/454, 701/461, 701/465, 701/467, 701/472, 340/995, 340/990, 342/457, 342/386, 455/423, 455/422, 455/446
• US Class Current: 701/410
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions
• CPC Class Codes: G08G 1/0125 Traffic data processing
• CPC Class Codes: H04W 12/02 Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A method and system for modeling and processing vehicular traffic data and information, comprising: (a) transforming a spatial representation of a road network into a network of spatially interdependent and interrelated oriented road sections, for forming an oriented road section network; (b) acquiring a variety of the vehicular traffic data and information associated with the oriented road section network, from a variety of sources; (c) prioritizing, filtering, and controlling, the vehicular traffic data and information acquired from each of the variety of sources; (d) calculating a mean normalized travel time (NTT) value for each oriented road section of said oriented road section network using the prioritized, filtered, and controlled, vehicular traffic data and information associated with each source, for forming a partial current vehicular traffic situation picture associated with each source; (e) fusing the partial current traffic situation picture associated with each source, for generating a single complete current vehicular traffic situation picture associated with entire oriented road section network; (f) predicting a future complete vehicular traffic situation picture associated with the entire oriented road section network; and (g) using the current vehicular traffic situation picture and the future vehicular traffic situation picture for providing a variety of vehicular traffic related service applications to end users.

Claims

1. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network;

determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and

predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein said behavior patterns of said road section network are time dependent, and

wherein said time dependent behavior pattern of a road section describes at least one of;

regular changing of associated normalized travel time (NTT) values as a function of time or regular changing of associated speed values as a function of time.

2. The method of claim 1, wherein different behavior patterns are established for a road section at different times, days of the week, holidays and/or special events.

3. The method of claim 1, wherein said time dependent correlation rule determines correlation and interrelation of each said single complete current vehicular traffic situation picture between different road sections as a function of time.

4. The method of claim 1, further comprising the step of:

predicting at least one future time vehicular traffic situation associated with at least a portion of said road section network.

5. The method of claim 4, wherein the current vehicular traffic situation serves as a baseline or starting point for predicting said at least one future time vehicular traffic situation.

6. The method of claim 4, wherein said predicting is performed at a predetermined frequency in a range of from about once per every two minutes to about once per every ten minutes.

7. The method of claim 4, wherein said predicting includes using said time dependent behavior patterns of said road section network and time dependent correlation rules of said road section network.

8. The method of claim 4, wherein said predicting includes identifying unexpected vehicular traffic developments from a said current vehicular traffic situation by comparing said developments to regular time dependent behavior patterns of said road section network.

9. The method of claim 8, wherein said predicting includes predicting propagation of effects of traffic developments identified in time along adjacent and non-adjacent road sections, using time dependent correlation rules of said road section network.

10. The method of claim 1, further comprising the step of providing a vehicular traffic related service application to an end user.

11. The method of claim 10, wherein said vehicular traffic data is used to provide one or more vehicular traffic related service applications selected from:

responding to traffic oriented queries;

finding optimal routes between given points of said road network;

finding alternative routes between given points of said road network;

estimating travel times between given points of said road network;

initiating alerts and route alterations when unexpected traffic events change said current vehicular traffic situation; and

combinations thereof.

12. The method of claim 1, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes.

13. The method of claim 12 wherein tracking locations of said mobile sensors comprises using location data and/or information provided by a server of a mobile wireless telecommunications network.

14. The method of claim 12, wherein said vehicle-carried mobile sensors are selected from the group consisting of:

computer devices;

cellular phone devices;

vehicle antitheft devices; and

combinations thereof.

15. The method of claim 12, wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network.

16. The method of claim 12, wherein said vehicle-carried mobile sensors include one or more of:

computer devices;

cellular phone devices and combinations thereof; and

wherein selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors includes the step of identifying said mobile sensors having a fast movement indicating vehicular movement.

17. The method of claim 12, wherein said vehicle-carried mobile sensors include one or more of:

computer devices;

cellular phone devices; and

combinations thereof; and

wherein selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors includes identifying said mobile sensors connected to said wireless telecommunications network whose cell transition or handover rates indicate a fast movement indicative of vehicular movement.

18. The method of claim 12, wherein said vehicle-carried mobile sensors include anti-theft devices, and wherein selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors includes identifying ignition activation for said mobile sensors comprising mobile vehicle anti-theft devices.

19. The method of claim 12, wherein said tracking is performed by polling said locations of said mobile sensors in known time intervals.

20. The method of claim 12, further comprising preventing said tracking of said mobile sensors that stop moving for a predetermined time interval.

21. The method of claim 12, further comprising preventing said tracking of said mobile sensors that go beyond the bounds of a sensible travel route.

22. The method of claim 12, further comprising maintaining said tracking within the locating capacity of said wireless telecommunications network.

23. The method of claim 12, wherein said tracking is controlled according to a predefined policy selected from the group consisting of:

not to track too many said vehicles in a same region of said road network;

focus said tracking on a certain problematic region of said road network;

stop said tracking said vehicles that stopped for a pre-determined time interval;

collect the vehicular traffic data and information within a certain limited capacity of said wireless telecommunication networks; and

, combinations thereof.

24. The method of claim 12, further including a procedure for protecting privacy of individuals associated with said vehicle-carried mobile sensors.

25. The method of claim 24, wherein the mobiles sensors are in communication with a cellular telecommunications network and the procedure for protecting privacy of individuals associated with said vehicle-carried mobile sensors includes keeping the identities of relevant mobile sensors within said wireless cellular telecommunications network.

26. The method of claim 24, further including deleting identities of said identified mobile sensors and/or deleting said locations of said tracked mobile sensors following processing.

27. The method of claim 1, further comprising filtering out said mobile sensors that are recognized as noise.

28. The method of claim 1, further comprising filtering out said mobile sensors whose behavior indicates they are not relevant.

29. The method of claim 1, comprising acquiring vehicular traffic data by a procedure selected from the group consisting of:

a push procedure;

a pull procedure; and

, combinations thereof;

wherein said push procedure is a mode of acquiring the vehicular traffic data initiated by said mobile sensors, and, wherein said pull procedure is a mode of acquiring the vehicular traffic data initiated by a software module sampling said mobile sensors.

30. The method of claim 1, wherein said road network is represented as a geographical information system (GIS) road network.

31. The method of claim 1, wherein each said road section of said road section network represents a road section having a single vehicular traffic continuation option.

32. The method of claim 31, wherein said single vehicular traffic continuation option is located at a head end road junction, said road section represents a unit featuring a plurality of consecutive road segments of said road network, said consecutive road segments positioned head-to-tail relative to each other are located between two road junctions, a tail end road junction and said head end road junction, within said road network, and are characterized by similar vehicular traffic data and information, and, said single vehicular traffic continuation option refers to one of various vehicular traffic flow options said each vehicle may take within said road network.

33. The method of claim 32, wherein, when lanes in various said road segments within said road section network are assigned to turning traffic, respective said road sections may yield different vehicular traffic data and information.

34. The method of claim 31, wherein said vehicular traffic continuation option is selected from the group consisting of:

continuing to travel straight;

taking a right turn; and

, taking a left turn;

from a said road segment joined or linked to a said head end road junction.

35. The method of claim 1, further comprising additionally processing data and/or information from a variety of sources selected from the group consisting of:

fixed sensors;

traffic reports by police;

radio broadcasts of the vehicular traffic data and information;

historical and event related vehicular traffic data and information;

other sources of the vehicular traffic data and information; and

combinations thereof.

36. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of sensors and predicting a future traffic situation; and

determining a current vehicular traffic situation based on current vehicular traffic data for a road section,

wherein predicting a traffic situation comprises predicting normalized travel times over a plurality of road sections based on time dependent vehicle behavior patterns, and

wherein the normalized travel times comprise travel times normalized with respect to a pre-determined distance, such that the predicted traffic parameter for a particular road section takes into account the time of passage of that road section.

37. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network and predicting a future traffic situation; and

determining a current vehicular traffic situation based on current vehicular traffic data for a road section,

wherein predicting a future traffic situation comprises predicting travel times over a plurality of road sections based on time dependent vehicle behavior patterns,

the method being further operable to compare detected traffic developments to regular time dependent behavior patterns of said road section network, and to identify discrepancies from said regular time dependent behavior patterns,

wherein propagation in time along adjacent and non-adjacent road sections of traffic events identified by discrepancies from said time dependent behavior patterns can be determined using time dependent correlation rules of said road section network and said time dependent correlation rule determines correlation of and interrelation between different road sections as a function of time,

wherein said behavior patterns of said road section network are time dependent, and

wherein said time dependent behavior pattern of a road section describes at least one of;

regular changing of associated normalized travel time (NTT) values as a function of time or regular changing of associated speed values as a function of time.

38. The method of claim 37, wherein acquiring vehicular traffic data comprises tracking locations of a sample of said mobile sensors for modelling a current vehicular situation.

39. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network;

determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted, and

predicting a future vehicular traffic situation by using correlation rules based on historic vehicular traffic data and vehicular traffic behavior patterns, wherein said behavior patterns of said road section network are time dependent, and wherein said time dependent behavior pattern of a road section describes at least one of;

regular changing of associated normalized travel time (NTT) values as a function of time or regular changing of associated speed values as a function of time.

40. The method of claim 39, wherein gaps in the current traffic situation are filled by using vehicular traffic behavior patterns and/or correlation rules based on historical vehicular traffic data.

41. The method of claim 39, wherein said behavior patterns of said road section network are time dependent, and said time dependent correlation rule determines correlation and interrelation of between different road sections as a function of time.

42. The method of claim 39, wherein said correlation rules of said road section network are time dependent, and said time dependent correlation rule determines correlation and interrelation of between different road sections as a function of time.

43. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and

wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network;

determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and

predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors.

44. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and

wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network;

determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and

predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors, and

wherein a mean normalized travel time (NTT) for a road section is calculated by statistically processing data from said mobile sensors traveling on said road section during a time period of an assessment cycle to indicate the possibility of different velocities on different lanes of said road section.

45. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and

wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network;

determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and

predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors,

wherein a mean normalized travel time (NTT) for a road section is calculated by statistically processing data from said mobile sensors traveling on said road section during a time period of an assessment cycle to indicate the possibility of different velocities on different lanes of said road section, and

wherein said mean normalized travel time (NTT) values are calculated using a confidence factor.

46. A method for modeling and processing vehicular traffic data, comprising the steps of:

generating a representation of a road network associated with the vehicular traffic data, the road network comprising a plurality of road sections;

acquiring vehicular traffic data associated with said road network from at least a plurality of mobile sensors in communication with a wireless telecommunication network, wherein acquiring vehicular traffic data comprises selecting a sample comprising vehicle-carried mobile sensors from the plurality of mobile sensors and tracking locations of mobile sensors of said sample for modelling and processing purposes, and

wherein locations of said vehicle-carried mobile sensors are obtained from said wireless telecommunication network in known time intervals, such that a path of each said vehicle is determined, said path being a sequence of connected road sections constituting a logical route to take between two points within said represented road network;

determining a current vehicular traffic situation based on current vehicular traffic data for a road section, wherein gaps in current vehicular traffic data for a road section are filled by using values that are predicted; and

predicting a future vehicular traffic situation with aid of vehicular traffic behavior patterns and correlation rules, wherein said correlation rules of said road section network are based on historic vehicular traffic data and are time dependent, and said time dependent correlation rules determine correlation of and interrelation between different road sections as a function of time, wherein normalized travel times (NTT) of said road sections of said determined path are calculated by using assumptions as to reasonable vehicle behavior and said tracked footprints of said mobile sensors,

wherein a mean normalized travel time (NTT) for a road section is calculated by statistically processing data from said mobile sensors traveling on said road section during a time period of an assessment cycle to indicate the possibility of different velocities on different lanes of said road section,

wherein said mean normalized travel time (NTT) values are calculated using a confidence factor, and

wherein the confidence factor is a function of one or more of;

accuracy of said footprints;

amount of said footprints; and

, error rate of said footprints.