Automated traffic mapping using sampling and analysis

• US 6,333,703 B1
• Filed: 10/04/2000
• Issued: 12/25/2001
• Est. Priority Date: 11/24/1998
• Status: Expired due to Term

• Current Assignee: TomTom Incorporated (TomTom NV)
• Original Assignee: International Business Machines Corporation
• Inventors: Alewine, Neal J., Maes, Stephane H., Ittycheriah, Abraham P., Moskowitz, Paul A., Colson, James C.
• Primary Examiner(s): Lee, Benjamin C.
• Application Number:
  US09/679,033
• Time in Patent Office: 447 Days
• Field of Search: 340/995, 340/989, 340/905, 455/507, 455/509, 455/575, 701/117, 701/118, 701/119, 701/213, 380/271
• US Class Current: 340/995.13
• CPC Class Codes: G08G 1/01 Detecting movement of traffic to be counted or controlled G08G1/07 - G08G1/14 take precedence
• CPC Class Codes: G08G 1/096716 where the received information does not generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096775 where the origin of the information is a central station
• CPC Class Codes: G08G 1/096811 where the route is computed offboard
• CPC Class Codes: G08G 1/096844 where the complete route is dynamically recomputed based on new data

Abstract

A system of mobile units are installed in multiple vehicles in traffic, the vehicles with mobile units being a sample of all vehicles in traffic. These mobile units include both wireless communications devices and apparatus that determines the location of each vehicle. Monitoring a vehicle'"'"'s position as a function of time also reveals the velocity of the vehicle. Position and speed information is periodically broadcast by the vehicles to a central monitoring station and to neighboring vehicles. At the central monitoring station, the collective input from the sample set of vehicles is processed using statistical analysis methods to provide an instant chart of traffic conditions in the area, the accuracy of said chart being within a range determined by the size of the sample. Warnings of delays or updates on traffic conditions on the road ahead are then automatically returned to subscribers of the information or are used as part of an Intelligent Vehicle Highway System (IVHS). Neighboring vehicles within a region communicating with one another form a network in which the broadcast information is processed locally on the respective vehicles to estimate possible problems ahead and consider computing an alternate road and/or checking with the central monitoring station for more information. If out of range of the central monitoring station, the vehicles in the network form a local area network for the exchange and update of information, and when any vehicle in the network is within range of the central monitoring station, the local area network data is uploaded to help update the overall traffic information.

Claims

1. An automated traffic mapping system comprising:

a plurality of mobile units installed in vehicles in traffic, each said mobile unit having both a wireless communications device and apparatus that determines location of a vehicle on which it is installed;

a central monitoring station receiving data from the plurality of mobile units and generating a map of traffic conditions; and

a plurality of receivers installed in vehicles, each said receiver receiving transmissions from the central monitoring station and displaying traffic information,

wherein said plurality of mobile units comprises a sampling of all vehicles in traffic, and wherein mathematical analysis of data from said plurality of mobile units is used to generate said map of traffic conditions, the results of said mathematical analysis being dependent upon data from each of said plurality of mobile units.

2. The automated traffic mapping system recited in claim 1 wherein the data received by the central monitoring station from the plurality of mobile units includes position data.

3. The automated traffic mapping system recited in claim 2 wherein the data received by the central monitoring station from the plurality of mobile units includes velocity data.

4. The automated traffic mapping system recited in claim 1 wherein said mathematical analysis is Bayesian analysis.

5. The automated traffic mapping system recited in claim 1 wherein said mathematical analysis uses an expert system.

6. The automated traffic mapping system recited in claim 1 wherein said mathematical analysis uses neural networks.

7. The automated traffic mapping system recited in claim 1 wherein said mathematical analysis uses classical statistics.

8. The automated traffic mapping system recited in claim 1 wherein said mathematical analysis uses artificial intelligence techniques.

9. The automated traffic mapping system recited in claim 1 wherein the apparatus that determines location of a vehicle is a Global Positioning Satellite (GPS) system.

10. The automated traffic mapping system recited in claim 1 wherein the central monitoring station includes a warning generation system which transmits warnings to mobile units installed in vehicles about to enter traffic jams.

11. The automated traffic mapping system recited in claim 1 wherein the central monitoring station includes a warning generation system which transmits warnings to an Intelligent Vehicle Highway System (IVHS).

12. A computer implemented method of automated traffic mapping comprising the steps of:

receiving at a central monitoring station data from a plurality of mobile units installed on vehicles;

generating a map of traffic conditions at the central station and transmitting traffic information to the mobile units; and

receiving and displaying the traffic information at the mobile units,

wherein said plurality of mobile units comprises a sampling of all vehicles in traffic, and wherein mathematical analysis of said data from said plurality of mobile units is used to generate said map of traffic conditions, the results of said mathematical analysis being dependent upon data from each of said plurality of mobile units.

13. The method of claim 12 wherein the data received by the central monitoring station from the plurality of mobile units includes position data.

14. The method of claim 13 wherein the data received by the central monitoring station from the plurality of mobile units includes velocity data.

15. The method of claim 12 wherein said mathematical analysis is Bayesian analysis.

16. The method of claim 12 wherein said mathematical analysis uses an expert system.

17. The method of claim 12 wherein said mathematical analysis uses neural networks.

18. The method of claim 12 wherein said mathematical analysis uses classical statistics.

19. The method of claim 12 wherein said mathematical analysis uses artificial intelligence techniques.