MOBILE TRIP PLANNER AND LIVE ROUTE UPDATE SYSTEM

• US 20150006071A1
• Filed: 06/27/2013
• Published: 01/01/2015
• Est. Priority Date: 06/27/2013
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Cai, Danqing
• Granted Patent: US 9,752,886 B2
• Time in Patent Office: Days
• US Class Current: 701/400
• 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

Abstract

A method for providing trip planning and route update is provided. The method includes receiving traffic demand data including a plurality of planned trips, each including an origin location, a destination location, a planned route, a planned departure time, and an end time. The method further includes building a traffic prediction model by aggregating the traffic demand data, and generating a traffic prediction for a specified trip. Related apparatus, systems, techniques and articles are also described.

Claims

1. A computer-implemented method for trip planning comprising:

receiving traffic demand data comprising a plurality of planned trips, each of the planned trips comprising an origin location, a destination location, a planned route, a departure time in the future, and an end time;

building a traffic prediction model by aggregating the traffic demand data; and

generating a traffic prediction for a specified trip, the specified trip comprising a specified origin location, a specified destination location, a specified planned route, a specified departure time, and a specified end time, the traffic prediction being generated by checking the specified trip against the traffic prediction model for route overlap between the specified planned route and the planned route of each of the plurality of planned trips, and for travel time overlap between the specified departure and end time, and the departure and end time of each of the plurality of planned trips.

2. The computer-implemented method according to claim 1, further comprising one or more sensor data and training the traffic prediction model using the one or more sensor data.

3. The computer-implemented method according to claim 1, wherein if a route overlap is found, the method further comprises generating a new route from the specified origin to the specified destination by avoiding route overlap and travel time overlap with the traffic demand data of the traffic prediction model.

4. The computer-implemented method according to claim 1, further comprising receiving an actual departure time, and updating the traffic prediction using the actual departure time instead of the specified departure time.

5. The computer-implemented method according to claim 4, wherein if a further route overlap is found, the method further comprises generating a further route from the specified origin to the specified destination by avoiding route overlap and travel time overlap with the traffic demand data of the traffic prediction model.

6. The computer-implemented method according to claim 1, wherein the plurality of planned trips is generated based on a plurality of prior recurring trips.

7. A non-transitory computer readable medium comprising instructions, which when executed by one or more processors, perform the method for trip planning comprising:

receiving traffic demand data comprising a plurality of planned trips, each of the planned trips comprising an origin location, a destination location, a planned route, a departure time in the future, and an end time;

building a traffic prediction model by aggregating the traffic demand data; and

generating a traffic prediction for a specified trip, the specified trip comprising a specified origin location, a specified destination location, a specified planned route, a specified departure time, and a specified end time, the traffic prediction being generated by checking the specified trip against the traffic prediction model for route overlap between the specified planned route and the planned route of each of the plurality of planned trips, and for travel time overlap between the specified departure and end time, and the departure and end time of each of the plurality of planned trips.

8. The non-transitory computer readable medium according to claim 7, further comprising one or more sensor data and training the traffic prediction model using the one or more sensor data.

9. The non-transitory computer readable medium according to claim 7, wherein if a route overlap is found, the method further comprises generating a new route from the specified origin to the specified destination by avoiding route overlap and travel time overlap with the traffic demand data of the traffic prediction model.

10. The non-transitory computer readable medium according to claim 7, further comprising receiving an actual departure time, and updating the traffic prediction using the actual departure time instead of the specified departure time.

11. The non-transitory computer readable medium according to claim 10, wherein if a further route overlap is found, the method further comprises generating a further route from the specified origin to the specified destination by avoiding route overlap and travel time overlap with the traffic demand data of the traffic prediction model.

12. The computer-implemented method according to claim 7, wherein the plurality of planned trips is generated based on a plurality of prior recurring trips.

13. A trip planning system comprising:

a database configured for storing traffic demand data comprising a plurality of planned trips, each of the planned trips comprising an origin location, a destination location, a planned route, a departure time in the future, and an end time;

a traffic prediction modeler configured for aggregating the traffic demand data and generating a traffic prediction model; and

a traffic prediction generator configured for generating a traffic prediction for a specified trip, the specified trip comprising a specified origin location, a specified destination location, a specified planned route, a specified departure time, and a specified end time, the traffic prediction being generated by checking the specified trip against the traffic prediction model for route overlap between the specified planned route and the planned route of each of the plurality of planned trips, and for travel time overlap between the specified departure and end time, and the departure and end time of each of the plurality of planned trips.

14. The trip planning system according to claim 13, wherein the database is further configured to store one or more sensor data, and the traffic prediction modeler is further configured for training the traffic prediction model using the one or more sensor data.

15. The trip planning system according to claim 13, wherein if a route overlap is found, the traffic prediction generator is further configured for generating a new route from the specified origin to the specified destination by avoiding route overlap and travel time overlap with the traffic demand data of the traffic prediction model.

16. The trip planning system according to claim 13, wherein the database is further configured for storing an actual departure time, and the traffic prediction generator is further configured for updating the traffic prediction using the actual departure time instead of the specified departure time.

17. The trip planning system according to claim 16, wherein if a further route overlap is found, the traffic prediction generator is further configured for generating a further route from the specified origin to the specified destination by avoiding route overlap and travel time overlap with the traffic demand data of the traffic prediction model.

18. The trip planning system according to claim 13, wherein the plurality of planned trips is generated by the traffic prediction generator based on a plurality of prior recurring trips.