Predictive routing system and method

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

identifying, by a computer system, a plurality of routes for a vehicle between a starting location and a destination location;

determining, by a computer system for each of the plurality of routes, predicted traffic conditions that include a travel time for the route, a likelihood of congestion on the route, and a likelihood of an accident on the route, wherein one or more of the traffic conditions are determined based on a message being displayed on a variable message sign external to the vehicle and installed on or near the route; and

presenting to a user of the vehicle, by the computer system for each of the plurality of routes, each of the traffic conditions determined for the route.

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

3. The method of claim 1 , wherein the travel time for one of the plurality of routes is determined based on a vehicle speed included in the variable message sign.

4. The method of claim 1 , wherein determining the travel time for one of the plurality of routes comprises:

receiving message information describing the message being displayed on the variable message sign, the message information including a condition that caused the display of the message; and

predicting the travel time for the route based on a vehicle speed included in the condition.

5. The method of claim 1 , wherein determining the travel time for one of the plurality of routes comprises:

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

summing the calculated travel times.

6. The method of claim 1 , wherein determining the likelihood of congestion for one of the plurality of routes comprises:

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

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

7. The method of claim 1 , wherein

determining the likelihood of an accident for one of the plurality of routes comprises:

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

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

8. The method of claim 1 , further comprising:

determining from the plurality of routes a route to recommend based on the determined traffic conditions; and

indicating to the user which of the plurality of routes is recommended.

9. The method of claim 1 , further comprising:

directing a driver of a vehicle along one of the plurality of routes; and

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

10. The method of claim 1 , wherein a telematics-navigation device at a vehicle wirelessly receives information on the message being displayed on the variable message sign and determines the traffic conditions of the routes.

11. The method of claim 1 , wherein a server in communication with a telematics-navigation device determines the traffic conditions of the routes.

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

identifying, by a computer system, a plurality of routes for a vehicle between a starting location and a destination location;

determining, by the computer system for each of the plurality of routes, predicted traffic conditions that include a travel time for the route, a likelihood of congestion on the route, and a likelihood of an accident on the route, wherein one or more of the traffic conditions are determined based on weather effect information describing effects that weather conditions over a period of time have had on a road of the route in the past; and

presenting to a user of the vehicle, by the computer system for each of the plurality of routes, each of the traffic conditions determined for the route.

13. The method of claim 12 , wherein the starting location is a current location of the vehicle.

14. The method of claim 12 , 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.

15. The method of claim 12 , 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.

16. The method of claim 15 , wherein the travel time for one of the plurality of routes is determined based on the weather effect information, the weather effect information including a vehicle speed.

17. The method of claim 12 , wherein each of the plurality of routes is comprised of a plurality of segments and determining the travel time for one of the plurality of routes comprises:

calculating a travel time for each of the plurality of segments of the route; and summing the calculated travel times;

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

responsive to the segment including a sensor and traffic information generated by the sensor being available, calculating the travel time for the segment based on the traffic information; and

responsive to the segment not including a sensor, 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.

18. The method of claim 12 , wherein the likelihood of congestion for the plurality of routes is determined based on the weather effect information.

19. The method of claim 12 , wherein each of the plurality of routes is comprised of a plurality of segments and determining the likelihood of congestion on one of the plurality of routes comprises:

determining a congestion level for each of the plurality of segments of the route; 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 the segment including a sensor and traffic information generated by the sensor being available, determining the congestion level for the segment based on the traffic information; and

responsive to the segment not including a sensor, 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.

20. The method of claim 12 , wherein the likelihood of an accident for the plurality of routes is determined based on the weather effect information.

21. The method of claim 12 , wherein each of the plurality of routes is comprised of a plurality of segments and determining the likelihood of an accident on one of the plurality of routes comprises:

determining an accident level for each of the plurality of segments of the route; 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 the segment including a sensor and traffic information generated by the sensor being available, determining the accident level for the segment based on the traffic information; and

responsive to the segment not including a sensor, 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.

22. The method of claim 12 , further comprising:

determining from the plurality of routes a route to recommend based on the determined traffic conditions; and

indicating to the user which of the plurality of routes is recommended.

23. The method of claim 12 , further comprising:

directing a driver of a vehicle along one of the plurality of routes; and

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

24. The method of claim 12 , wherein a telematics-navigation device at the vehicle wirelessly receives the weather effect information and determines the traffic conditions of the routes.

25. The method of claim 12 , wherein a server in communication with a telematics-navigation device determines the traffic condition of the routes.

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

identifying, by a computer system, a plurality of routes for a vehicle between a starting location and a destination location;

determining, by the computer system for each of the plurality of routes, predicted traffic conditions that include a travel time for the route, a likelihood of congestion on the route, and a likelihood of an accident on the route, wherein one or more of the traffic conditions are determined based on accident information describing accidents that have occurred on the route in the past under current circumstances; and

presenting to a user of the vehicle, by the computer system for each of the plurality of routes, each of the traffic conditions determined for the route.

27. The method of claim 26 , wherein the starting location is a current location of the vehicle.

28. The method of claim 26 , wherein each of the plurality of routes is comprised of a plurality of segments and determining the likelihood of congestion on one of the plurality of routes 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 the segment including a sensor and traffic information generated by the sensor being available, determining the congestion level for the segment based on the traffic information; and

responsive to the segment not including a sensor, 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.

29. The method of claim 26 , wherein each of the plurality of routes is comprised of a plurality of segments and determining the likelihood of an accident on one of the plurality of routes comprises:

determining an accident level for each of the plurality of segments of the route; 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 the segment including a sensor and traffic information generated by the sensor being available, determining the accident level for the segment based on the traffic information; and

responsive to the segment not including a sensor, 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.

30. The method of claim 26 , further comprising:

determining from the plurality of routes a route to recommend based on the determined traffic conditions; and

indicating to the user which of the plurality of routes is recommended.

31. The method of claim 26 , further comprising:

directing a driver of a vehicle along one of the plurality of routes; and

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

32. The method of claim 26 , wherein a telematics-navigation device at the vehicle wirelessly receives the accident information and determines the traffic conditions of the routes.

33. The method of claim 26 , wherein a server in communication with a telematics-navigation device determines the traffic conditions of the routes.