Predictive Routing System and Method

• US 20120215432A1
• Filed: 02/18/2011
• Published: 08/23/2012
• Est. Priority Date: 02/18/2011
• Status: Active Grant

• Current Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Original Assignee: Honda Motor Co., Ltd. (Honda Motor Company)
• Inventors: Uyeki, Robert
• Granted Patent: US 8,412,445 B2
• Time in Patent Office: Days
• US Class Current: 701/118
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G08G 1/096827 where the route is computed onboard
• CPC Class Codes: G08G 1/096838 where the user preferences are taken into account or the user selects one route out of a plurality

Abstract

A user of a telematics-navigation device in a vehicle requests a recommendation as to a route to take to reach a destination. Multiple routes are identified from a current location of the vehicle to the destination. For each route, traffic conditions of the route are predicted. Based on the traffic conditions predicted for each route, one route is chosen to recommend to the user for traveling to the destination. The recommended route is presented to the user along with the other identified routes. When the user selects to travel along one of the presented routes, directions to the destination are provided to a driver of the vehicle along the selected route.

Claims

1. A computer-implemented method for predicting traffic conditions of a route, the method comprising:

identifying a route between a starting location and a destination location, the route including at least one road;

receiving message information describing a message being displayed on a variable message sign on or near the route; and

predicting a traffic condition of the route based on the message information.

2. The method of claim 1, wherein the starting location is a current location of a vehicle.

3. The method of claim 1, wherein the traffic condition predicted based on the message information is a travel time for the route.

4. The method of claim 3, wherein the travel time for the route is predicted based on a vehicle speed included in the message.

5. The method of claim 3, wherein the message information includes a condition that caused the display of the message and the travel time for the route is predicted based on a vehicle speed included in the condition.

6. The method of claim 3, wherein the route is comprised of a plurality of segments and predicting the travel time for the route comprises:

calculating a travel time for each of the plurality of segments; and

summing the calculated travel times;

wherein calculating the travel time for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, calculating the travel time for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, calculating the travel time for the segment based on message information that describes a message being displayed on a variable message sign on or near the segment.

7. The method of claim 1, wherein a traffic condition predicted based on the message information is likelihood of congestion on the route.

8. The method of claim 7, wherein the route is comprised of a plurality of segments and predicting the likelihood of congestion on the route comprises:

determining a congestion level for each of the plurality of segments; and

predicting the likelihood of congestion based on the congestion levels determined for the plurality of segments;

wherein determining the congestion level for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, determining the congestion level for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, determining the congestion level for the segment based on message information that describes a message being displayed on a variable message sign on or near the segment.

9. The method of claim 1, wherein a traffic condition predicted based on the message information is likelihood of an accident on the route.

10. The method of claim 9, wherein the route is comprised of a plurality of segments and predicting the likelihood of an accident on the route comprises:

determining an accident level for each of the plurality of segments; and

predicting the likelihood of an accident on the route based on the accident levels determined for the plurality of segments;

wherein determining the accident level for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, determining the accident level for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, determining the accident level for the segment based on message information describing a message being displayed on a variable message sign on or near the segment.

11. The method of claim 1, further comprising:

determining from a plurality of routes between the starting location and destination location whether to recommend the identified route for traveling to the destination location based on the predicted traffic condition.

12. The method of claim 1, further comprising:

directing a driver of a vehicle along the route; and

warning the driver when approaching a segment of the route with a high likelihood of an accident.

13. The method of claim 1, wherein a telematics-navigation device at a vehicle wirelessly receives the message information and predicts the traffic condition of the route based on the message information.

14. The method of claim 1, wherein a server in communication with a telematics-navigation device predicts the traffic condition of the route based on the message information.

15. A computer-implemented method for predicting traffic conditions of a route, the method comprising:

identifying a route between a starting location and a destination location, the route including at least one road;

receiving weather effect information describing effects that weather conditions over a period of time have had on the road in the past; and

predicting a traffic condition of the route based at least in part on the weather effect information.

16. The method of claim 15, wherein the starting location is a current location of a vehicle.

17. The method of claim 15, wherein the weather effect information describes the effects that cumulative weather conditions over a period of time have had on the road of the route in the past.

18. The method of claim 15, wherein the weather effect information describes the effects that continuous weather conditions over a period of time have had on the road of the route in the past.

19. The method of claim 15, wherein the traffic condition predicted based on the weather effect information is a travel time for the route.

20. The method of claim 19, wherein the travel time for the route is predicted based on a vehicle speed included in the weather effect information.

21. The method of claim 19, wherein the route is comprised of a plurality of segments and predicting the travel time for the route comprises:

calculating a travel time for each of the plurality of segments; and

summing the calculated travel times;

wherein calculating the travel time for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, calculating the travel time for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, calculating the travel time for the segment based on weather effect information that describes effects that weather conditions over a period of time have had on the segment in the past.

22. The method of claim 15, wherein a traffic condition predicted based on the weather effect information is likelihood of congestion on the route.

23. The method of claim 22, wherein the route is comprised of a plurality of segments and predicting the likelihood of congestion on the route comprises:

determining a congestion level for each of the plurality of segments; and

predicting the likelihood of congestion based on the congestion levels determined for the plurality of segments;

wherein determining the congestion level for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, determining the congestion level for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, determining the congestion level for the segment based on weather effect information that describes effects that weather conditions over a period of time have had on the segment in the past.

24. The method of claim 15, wherein a traffic condition predicted based on the weather effect information is likelihood of an accident on the route.

25. The method of claim 24, wherein the route is comprised of a plurality of segments and predicting the likelihood of an accident on the route comprises:

determining an accident level for each of the plurality of segments; and

predicting the likelihood of an accident on the route based on the accident levels determined for the plurality of segments;

wherein determining the accident level for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, determining the accident level for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, determining the accident level for the segment based on weather effect information that describes effects that weather conditions over a period of time have had on the segment in the past.

26. The method of claim 15, further comprising:

determining from a plurality of routes between the starting location and destination location whether to recommend the identified route for traveling to the destination location based on the predicted traffic condition.

27. The method of claim 15, further comprising:

directing a driver of a vehicle along the route; and

warning the driver when approaching a segment of the route with a high likelihood of an accident.

28. The method of claim 15, wherein a telematics-navigation device at a vehicle wirelessly receives the weather effect information and predicts the traffic condition of the route based on the weather effect information.

29. The method of claim 15, wherein a server in communication with a telematics-navigation device predicts the traffic condition of the route based on the weather effect information.

30. A computer-implemented method for predicting traffic conditions of a route, the method comprising:

identifying a route between a starting location and a destination location;

receiving accident information describing accidents that have occurred on the route in the past under current circumstances; and

predicting traffic conditions of the route based at least in part on the accident information.

31. The method of claim 30, wherein the starting location is a current location of a vehicle.

32. The method of claim 30, wherein a traffic condition predicted based on the message information is likelihood of congestion on the route.

33. The method of claim 32, wherein the route is comprised of a plurality of segments and predicting the likelihood of congestion on the route comprises:

determining a congestion level for each of the plurality of segments; and

predicting the likelihood of congestion based on the congestion levels determined for the plurality of segments;

wherein determining the congestion level for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, determining the congestion level for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, determining the congestion level for the segment based on accident information that describes accidents that have occurred on the segment in the past under current circumstances.

34. The method of claim 30, wherein a traffic condition predicted based on the message information is likelihood of an accident on the route.

35. The method of claim 34, wherein the route is comprised of a plurality of segments and predicting the likelihood of an accident on the route comprises:

determining an accident level for each of the plurality of segments; and

predicting the likelihood of an accident on the route based on the accident levels determined for the plurality of segments;

wherein determining the accident level for at least one of the plurality of segments comprises;

responsive to real-time traffic information being available for the segment, determining the accident level for the segment based on the real-time traffic information; and

responsive to real-time traffic information being unavailable for the segment, determining the accident level for the segment based on accident information that describes accidents that have occurred on the segment in the past under current circumstances.

36. The method of claim 30, further comprising:

determining from a plurality of routes between the starting location and destination location whether to recommend the identified route for traveling to the destination location based on the predicted traffic condition.

37. The method of claim 30, further comprising:

directing a driver of a vehicle along the route; and

warning the driver when approaching a segment of the route with a high likelihood of an accident.

38. The method of claim 30, wherein a telematics-navigation device at a vehicle wirelessly receives the message information and predicts the traffic condition of the route based on the message information.

39. The method of claim 30, wherein a server in communication with a telematics-navigation device predicts the traffic condition of the route based on the message information.