Generating vehicle traffic data from raw location data for mobile units

• US 20040220721A1
• Filed: 04/29/2003
• Published: 11/04/2004
• Est. Priority Date: 04/29/2003
• Status: Active Grant

• Current Assignee: IDPA Holdings Incorporated (InterDigital, Inc.)
• Original Assignee: Lucent Technologies, Inc. (Nokia Corporation)
• Inventors: Chiang, Tung Ching
• Granted Patent: US 6,988,032 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

Where raw location data representing a position of the one or more mobile units is available, vehicular traffic data representing a position of one or more vehicles can be generated based on the raw location for the one or more mobile units. Such generation of vehicular traffic data can include speed-based filtering and/or position-based filtering of the raw location data.

Claims

1. A method for gathering vehicular traffic data based upon location data for one or more mobile units, the method comprising:

generating vehicle traffic data representing a position of one or more vehicles based on raw location data representing a position of the one or more mobile units.

2. The method of claim 1, wherein the generating step includes:

deriving speed data for the mobile units based upon the raw location data, respectively; and

filtering, based upon the speed data, to discriminate which of the mobile units represent vehicles.

3. The method of claim 2, wherein the deriving speed data step includes:

deriving a distance moved between two successive positions of the mobile unit; and

dividing the distance by an amount of time elapsed between when the two successive positions were determined.

4. The method of claim 2, wherein the filtering step includes:

comparing the speed data against a predetermined reference value; and

treating, for those mobile units which the comparing step indicates are moving with vehicles, data for the mobile units as representing data for the vehicles, respectively.

5. The method of claim 4, wherein the predetermined reference value is a minimum speed (SMIN) for a typical vehicle, the comparing step compares the speed data against SMIN, and the treating step treats data for mobile units whose speed (S) is one of S>

SMIN or S≧

SMIN as representing data for the corresponding vehicles.

6. The method of claim 4, wherein the predetermined reference value is a maximum speed (SMAX) for a typical pedestrian, the comparing step compares the speed data against SMAX, and the treating step treats data for mobile units whose speed (S) is one of S>

SMAX or S≧

SMAX as representing data for the corresponding vehicles.

7. The method of claim 4, wherein

the raw location data for a mobile unit includes an identifier of the mobile unit, position coordinates, and a time stamp indicating when the coordinates were determined;

and

the treating step includes;

assigning an identifier of the vehicle;

adopting, as vehicle data, the position coordinates and time stamps, respectively, of the mobile unit as position coordinates and time stamps for the vehicle;

incorporating the speed data for the vehicle as part of the vehicle data;

the vehicle traffic data representing an aggregate of the vehicle data resulting from the treating step.

8. The method of claim 7, wherein the assigning step assigns the vehicle identifier is based upon the mobile unit identifier.

9. The method of claim 4, wherein, for remaining mobile units not found to correspond to vehicles by the speed-based filtering step, the generating step further includes:

filtering, based upon the positions of the remaining mobile units, to discriminate which of the remaining mobile units represent vehicles.

10. The method of claim 9, wherein the position-based filtering step includes comparing the positions of the remaining mobile units against at least one of a road database of known vehicular traffic areas and a footpath database of known pedestrian traffic areas.

11. The method of claim 1, wherein the generating step includes:

filtering, based upon the positions of the mobile units, to discriminate which of the mobile units represent vehicles.

12. The method of claim 11, wherein the filtering step, for each mobile unit, includes:

comparing the position of the mobile unit against the contents of one or more databases of vehicle-relevant areas.

13. The method of claim 12, wherein

the one or more databases of vehicle-relevant areas include a road database of known vehicular traffic areas;

and

the filtering step, for each mobile unit, includes;

comparing the position of the mobile unit against the road database; and

treating, for those mobile units whose position is on or within a predetermined distance of a vehicular traffic area listed in the road database, data for the mobile units as representing data for the vehicles, respectively.

14. The method of claim 12, wherein

the one or more databases of vehicle-relevant areas include a footpath database of known pedestrian traffic areas;

and

the filtering step, for each mobile unit, includes;

comparing the position of the mobile unit against the footpath database; and

treating, for those mobile units whose respective position is either not on or not within a predetermined distance of a pedestrian traffic area listed in the footpath database, data for the mobile units as representing data for the vehicles, respectively

15. The method of claim 11, wherein

the raw location data for a mobile unit includes an identifier of the mobile unit, position coordinates, and a time stamp indicating when the coordinates were determined;

and

the position-based filtering step identifies ones of the mobile units that can be treated as representing vehicles;

the method further comprising;

treating each mobile unit identified by the position-based filtering step as a vehicle by

assigning, an identifier of the vehicle; and

adopting, as vehicle data, the position coordinates and time stamps, respectively, of the mobile unit as position coordinates and time stamps for the vehicle;

the vehicle traffic data representing an aggregate of the vehicle data resulting from the treating step.

16. The method of claim 1, wherein the generating step includes:

deriving distances moved between successive positions of the mobile units, respectively; and

filtering, based upon the distances-moved, to discriminate which of the mobile units represent vehicles.

17. The method of claim 16, wherein the filtering step includes:

comparing the distances against a predetermined reference value; and

treating, for those mobile units which the comparing step indicates are moving with vehicles, data for the mobile units as representing data for the vehicles, respectively.

18. The method of claim 17, wherein the predetermined reference value is a minimum distance (DMIN) moved by a typical vehicle, the comparing step compares the distances against DMIN, and the treating step treats data for mobile units whose distance (D) is one of D>

DMIN or D≧

DMIN as representing data for the corresponding vehicles.

19. The method of claim 17, wherein the predetermined reference value is a maximum distance (DMAX) moved by a typical pedestrian, the comparing step compares the speed data against DMAX, and the treating step treats data for mobile units whose distance (D) is one of D>

DMAX or D≧

DMAX as representing data for the corresponding vehicles.

20. The method of claim 1, wherein:

the raw location data for a mobile unit includes an identifier of the mobile unit, position coordinates, and a time stamp indicating when the coordinates were determined; and

the traffic data for a vehicle includes an identifier of the vehicle, position coordinates for at least one position, and at least one time stamp corresponding to the position coordinates, respectively.

21. The method of claim 20, wherein the traffic data for a vehicle also includes speed data for the vehicle.

22. A method for gathering vehicular traffic data based upon location data for one or more mobile units, the method comprising:

receiving raw location data representing a position of the one or more mobile units;

filtering the raw location data to discriminate which of the mobile units represent vehicles, the filtering being based upon at least one of the following criteria

speed data as derived from the raw location data for the mobile units,

positions of the mobile units represented by the raw location data, and

distances-moved as derived from the raw location data for the mobile units; and

generating vehicular traffic data, for each mobile unit identified as a vehicle according to the filtering step, by

assigning an identifier of the vehicle, and

adopting, as vehicle data, at least the position coordinates and time stamps, respectively, of the mobile unit as position coordinates and time stamps for the vehicle.