Environmental stewardship based on driving behavior

• US 8,812,352 B2
• Filed: 10/14/2009
• Issued: 08/19/2014
• Est. Priority Date: 10/14/2009
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Boss, Gregory Jensen, Hamilton, Rick Allen II, Martino, Jacquelyn Annette, Pickover, Clifford Alan, Sand, Anne R.
• Primary Examiner(s): Chen, George
• Application Number:
  US12/578,653
• Publication Number:
  US 20110087525A1
• Time in Patent Office: 1,770 Days
• Field of Search: 705/1.1, 705/5, 705/13, 705 711- 742, 705400-418, 705 141- 1425
• US Class Current: 705/13
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/04 Billing or invoicing
• CPC Class Codes: G06Q 50/265 Personal security, identity or safety
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G06Q 99/00 Subject matter not provided for in other groups of this subclass

Abstract

A method and system for managing driving patterns by charging a variable incentive and/or disincentive based on individual driving behaviors. Distances detected between a vehicle and other vehicles are detected within a time period. N tailgating incidents are identified based on the detected distances being less than a threshold value for distance. A variable toll is determined by evaluating a function of a base amount and N. The driver of the vehicle is charged the variable toll. Charging the variable toll provides an incentive to the driver to encourage driving that does not include tailgating or a disincentive to the driver to deter tailgating.

Claims

1. A method of managing driving patterns by charging a variable toll based on individual driving behaviors, comprising:

receiving proximity data detected at a first time within a time period P by a plurality of proximity sensor systems included in a plurality of vehicles, wherein said proximity data includes a one or more distances between a vehicle and a plurality of vehicles, and wherein a distance of said one or more distances is a measure of distance between said vehicle and another vehicle immediately in front of said vehicle;

storing said proximity data as part of driving behavior data, wherein said driving behavior data includes other proximity data that includes said one or more distances measured at other times within said time period P;

identifying N tailgating incidents within said time period P, wherein N≧

1, wherein said identifying said N tailgating incidents is based on one or more determinations that said distance is less than a first predefined threshold value for a tailgating distance, and wherein said identifying said N tailgating incidents is based on said proximity data and said other proximity data;

detecting that said vehicle arrives at a toll-charging station;

a processor of a computer system determining a variable toll T by evaluating a function f, wherein said function f is a function of B and said N, wherein the value of f is based at least in part on said N, wherein said B is a base amount, and wherein a result of said evaluating said function f is a value of said T selected from the group consisting of a first value less than said B and a second value greater than said B; and

charging a driver of said vehicle said variable toll T, wherein said charging said driver said variable toll T includes providing an incentive to said driver to encourage a first driving behavior if said result of said evaluating said function f is said first value and providing a disincentive to said driver to deter a second driving behavior if said result of said evaluating said function f is said second value.

2. The method of claim 1, further comprising:

receiving contextual information that includes aggregate distances between other vehicles, wherein said other vehicles are in a predefined proximity to said vehicle;

determining said contextual information indicates that one or more tailgating incidents of said N tailgating incidents are appropriate based on conditions selected from the group consisting of;

road conditions, traffic conditions, and a combination thereof; and

adjusting said variable toll T based on said one or more tailgating incidents being indicated as appropriate.

3. The method of claim 1, further comprising:

retrieving driving history of said driver; and

adjusting said variable toll T based on said driving history.

4. The method of claim 1, further comprising determining a braking frequency over said time period P of said driver applying a braking system in said vehicle, wherein said determining said variable toll T includes evaluating said function f, and wherein said function f is said function of said B and said N and is further a function of said braking frequency.

5. The method of claim 1, further comprising determining an acceleration frequency over said time period P of said driver performing a substantial acceleration according to predefined criteria, wherein said determining said variable toll T includes evaluating said function f, wherein said function f is said function of said B and said N and is further a function of said acceleration frequency.

6. The method of claim 1, further comprising determining an average speed of said vehicle over said time period P, wherein said determining said variable toll T includes evaluating said function f, wherein said function f is said function of said B and said N and is further a function of said average speed.

7. A computer system comprising a processor and a computer-readable memory unit coupled to said processor, said memory unit containing instructions executable by said processor to implement a method of managing driving patterns by charging a variable toll based on individual driving behaviors, said method comprising:

receiving proximity data detected at a first time within a time period P by a plurality of proximity sensor systems included in a plurality of vehicles, wherein said proximity data includes a one or more distances between a vehicle and a plurality of vehicles, and wherein a distance of said one or more distances is a measure of distance between said vehicle and another vehicle immediately in front of said vehicle;

storing said proximity data as part of driving behavior data, wherein said driving behavior data includes other proximity data that includes said one or more distances measured at other times within said time period P;

identifying N tailgating incidents within said time period P, wherein N≧

1, wherein said identifying said N tailgating incidents is based on one or more determinations that said distance is less than a first predefined threshold value for a tailgating distance, and wherein said identifying said N tailgating incidents is based on said proximity data and said other proximity data;

detecting that said vehicle arrives at a toll-charging station;

determining a variable toll T by evaluating a function f, wherein said function f is a function of B and said N, wherein the value of f is based at least in part on said N, wherein said B is a base amount, and wherein a result of said evaluating said function f is a value of said T selected from the group consisting of a first value less than said B and a second value greater than said B; and

charging a driver of said vehicle said variable toll T, wherein said charging said driver said variable toll T includes providing an incentive to said driver to encourage a first driving behavior if said result of said evaluating said function f is said first value and providing a disincentive to said driver to deter a second driving behavior if said result of said evaluating said function f is said second value.

8. The computer system of claim 7, wherein said method further comprises:

receiving contextual information that includes aggregate distances between other vehicles in a predefined proximity to a second vehicle;

determining said contextual information indicates that one or more tailgating incidents of said N tailgating incidents are appropriate based on road conditions or traffic conditions; and

adjusting said variable toll T based on said one or more tailgating incidents being indicated as appropriate.

9. The computer system of claim 7, wherein said method further comprises:

retrieving driving history of said driver; and

adjusting said variable toll T based on said driving history.

10. The computer system of claim 7, wherein said method further comprises determining a braking frequency over said time period P of said driver applying a braking system in said vehicle, wherein said determining said variable toll T includes evaluating said function f, and wherein said function f is said function of said B and said N and is further a function of said braking frequency.

11. The computer system of claim 7, wherein said method further comprises determining an acceleration frequency over said time period P of said driver performing a substantial acceleration according to predefined criteria, wherein said determining said variable toll T includes evaluating said function f, wherein said function f is said function of said B and said N and is further a function of said acceleration frequency.

12. The computer system of claim 7, wherein said method further comprises determining an average speed of said vehicle over said time period P, wherein said determining said variable toll T includes evaluating said function f, wherein said function f is said function of said B and said N and is further a function of said average speed.

13. A computer program product, comprising a computer readable storage medium having a computer readable program code stored therein, said computer readable program code containing instructions executable by a processor of a computer system to implement a method of managing driving patterns by charging a variable toll based on individual driving behaviors, said method comprising:

receiving proximity data detected at a first time within a time period P by a plurality of proximity sensor systems included in a plurality of vehicles, wherein said proximity data includes a one or more distances between a vehicle and a plurality of vehicles, and wherein a distance of said one or more distances is a measure of distance between said vehicle and another vehicle immediately in front of said vehicle;

storing said proximity data as part of driving behavior data, wherein said driving behavior data includes other proximity data that includes said one or more distances measured at other times within said time period P;

identifying N tailgating incidents within said time period P, wherein N≧

1, wherein said identifying said N tailgating incidents is based on one or more determinations that said distance is less than a first predefined threshold value for a tailgating distance, and wherein said identifying said N tailgating incidents is based on said proximity data and said other proximity data;

detecting that said vehicle arrives at a toll-charging station;

determining a variable toll T by evaluating a function f, wherein said function f is a function of B and said N, wherein the value of f is based at least in part on said N, wherein said B is a base amount, and wherein a result of said evaluating said function f is a value of said T selected from the group consisting of a first value less than said B and a second value greater than said B; and

charging a driver of said vehicle said variable toll T, wherein said charging said driver said variable toll T includes providing an incentive to said driver to encourage a first driving behavior if said result of said evaluating said function f is said first value and providing a disincentive to said driver to deter a second driving behavior if said result of said evaluating said function f is said second value.

14. The program product of claim 13, wherein said method further comprises:

receiving contextual information that includes aggregate distances between other vehicles in a predefined proximity to a second vehicle;

determining said contextual information indicates that one or more tailgating incidents of said N tailgating incidents are appropriate based on road conditions or traffic conditions; and

adjusting said variable toll T based on said one or more tailgating incidents being indicated as appropriate.

15. The program product of claim 13, wherein said method further comprises:

retrieving driving history of said driver; and

adjusting said variable toll T based on said driving history.

16. The program product of claim 13, wherein said method further comprises determining a braking frequency over said time period P of said driver applying a braking system in said vehicle, wherein said determining said variable toll T includes evaluating said function f, and wherein said function f is said function of said B and said N and is further a function of said braking frequency.

17. The program product of claim 13, wherein said method further comprises determining an acceleration frequency over said time period P of said driver performing a substantial acceleration according to predefined criteria, wherein said determining said variable toll T includes evaluating said function f, wherein said function f is said function of said B and said N and is further a function of said acceleration frequency.

18. The program product of claim 13, wherein said method further comprises determining an average speed of said vehicle over said time period P, wherein said determining said variable toll T includes evaluating said function f, wherein said function f is said function of said B and said N and is further a function of said average speed.

19. A process for supporting computing infrastructure, said process comprising providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable code in a computing system comprising a processor, wherein said processor carries out instructions of said code to cause the computing system to perform a method of managing driving patterns by charging a variable toll based on individual driving behaviors, said method comprising:

receiving proximity data detected at a first time within a time period P by a plurality of proximity sensor systems included in a plurality of vehicles, wherein said proximity data includes a one or more distances between a vehicle and a plurality of vehicles, and wherein a distance of said one or more distances is a measure of distance between said vehicle and another vehicle immediately in front of said vehicle;

storing said proximity data as part of driving behavior data, wherein said driving behavior data includes other proximity data that includes said one or more distances measured at other times within said time period P;

identifying N tailgating incidents within said time period P, wherein N≧

1, wherein said identifying said N tailgating incidents is based on one or more determinations that said distance is less than a first predefined threshold value for a tailgating distance, and wherein said identifying said N tailgating incidents is based on said proximity data and said other proximity data;

detecting that said vehicle arrives at a toll-charging station;

said processor of said computing system determining a variable toll T by evaluating a function f, wherein the value of f is based at least in part on said N, wherein said function f is a function of B and said N, wherein said B is a base amount, and wherein a result of said evaluating said function f is a value of said T selected from the group consisting of a first value less than said B and a second value greater than said B; and

charging a driver of said vehicle said variable toll T, wherein said charging said driver said variable toll T includes providing an incentive to said driver to encourage a first driving behavior if said result of said evaluating said function f is said first value and providing a disincentive to said driver to deter a second driving behavior if said result of said evaluating said function f is said second value.

20. The process of claim 19, wherein said method further comprises:

receiving contextual information that includes aggregate distances between other vehicles in a predefined proximity to a second vehicle;

determining said contextual information indicates that one or more tailgating incidents of said N tailgating incidents are appropriate based on road conditions or traffic conditions; and

adjusting said variable toll T based on said one or more tailgating incidents being indicated as appropriate.