METHOD AND SYSTEM FOR EXTRACTING ROUTE CHOICE PREFERENCE OF A USER

• US 20130191314A1
• Filed: 12/04/2012
• Published: 07/25/2013
• Est. Priority Date: 01/21/2012
• Status: Abandoned Application

• Current Assignee: NEC Company Limited
• Original Assignee: NEC Company Limited
• Inventors: Hu, Weisong, Liu, Xiaowei, Li, Man
• Application Number:
  US13/693,683
• Publication Number:
  US 20130191314A1
• Time in Patent Office: Days
• US Class Current: 706/46
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

A system for extracting route choice preference of a user is provided. The system includes: a key route analysis unit configured to analyze history routes of all users to obtain key routes and their costs; and a user preference extraction unit configured to obtain feature routes of a certain user and their costs based upon the key routes and their costs as well as history routes of the certain user, and to extract route choice preference of the certain user based upon the feature routes of the certain user and their costs. Also provided is a method for extracting route choice preference of a user. The present embodiments enable automatic extraction of route choice preference of a user from history data, without requiring the user to preset his/her own preference.

Claims

1. A system for extracting route choice preference of a user, the system comprising:

a key route analysis unit configured to analyze history routes of all users to obtain key routes and their costs; and

a user preference extraction unit configured to obtain feature routes of a user and their costs based upon the key routes and their costs as well as history routes of a user, and to extract route choice preference of the user based upon the feature routes of the user and their costs.

2. The system according to claim 1, wherein the key route analysis unit comprises:

a key route extraction subunit configured to select pairs of key nodes by calculating a number of history routes of all users between any pair of nodes, and to extract the key routes based upon the selected pairs of key nodes; and

a key route cost calculation subunit configured to group the key routes and to calculate cost values of the key routes in the same group for each cost.

3. The system according to claim 2, wherein the key route extraction subunit is configured to select two nodes which have two or more routes connected therebetween as a pair of key nodes, and to select routes represented by a pair of key nodes and two or more of which having a frequency of occurrence that is larger than a first threshold as key routes.

4. The system according to claim 2, wherein the key route cost calculation subunit is configured to put key routes having the same original node and destination node into a group, to calculate cost values of the key routes in the same group for each cost, and to compare the calculated cost values to assign the costs to the key routes.

5. The system according to claim 4, wherein the key route cost calculation subunit is configured to assign a cost to a key route having an optimal value for the cost if the cost has a numerical value, and to assign a cost to each of the key routes if the cost has a non-numerical value.

6. The system according to claim 2, wherein

the key route analysis unit further comprises a first route classification subunit configured to classify the history routes of all users in accordance with a certain criterion, and to provide the classified history routes to the key route extraction subunit;

the user preference extraction unit further comprises a second route classification subunit configured to classify the history routes of the user in accordance with the certain criterion, and to provide the classified history routes to the feature route extraction subunit.

7. The system according to claim 6, wherein the certain criterion comprises any of time periods in a day, date type, and weather condition.

8. The system according to claim 1, wherein the user preference extraction unit comprises:

a feature route extraction subunit configured to extract the feature routes of the user from the key routes based upon the history routes of the user; and

a user preference calculation subunit configured to calculate weights for the route choice preference of the user based upon the costs of the feature routes of the user, and to calculate the route choice preference of the user based upon the weights.

9. The system according to claim 8, wherein the feature route extraction subunit is configured to extract, from the history routes of the user, a path which is identical to a key route and has a frequency of occurrence that is larger than a second threshold as a feature route of the user.

10. The system according to claim 8, wherein the user preference extraction unit further comprises:

a third route classification subunit configured to classify the history routes of the user in accordance with geographical regions, and to provide the classified history routes to the feature route extraction subunit.

11. The system according to claim 10, wherein the third route classification subunit is configured to divide a map into a plurality of regions, and to classify the plurality of regions as active regions and non-active regions in accordance with a number of the history routes of the user and a number of total travel times for the history routes of the user.

12. A method for extracting route choice preference of a user, the method comprising:

analyzing history routes of all users to obtain key routes and their costs; and

obtaining feature routes of a user and their costs based upon the key routes and their costs as well as history routes of the user, and extracting route choice preference of the user based upon the feature routes of the user and their costs.

13. The method according to claim 12, wherein the step of analyzing comprises:

selecting pairs of key nodes by calculating a number of history routes of all users between any pair of nodes, and extracting the key routes based upon the selected pairs of key nodes; and

grouping the key routes and calculating cost values of the key routes in the same group for each cost.

14. The method according to claim 13, wherein the steps of selecting and extracting comprise:

selecting two nodes which have two or more routes connected therebetween as a pair of key nodes, and routes represented by a pair of key nodes and two or more of which having a frequency of occurrence that is larger than a first threshold as key routes.

15. The method according to claim 13, wherein the steps of grouping and calculating comprise:

putting key routes having the same original node and destination node into a group, calculating cost values of the key routes in the same group for each cost, and comparing the calculated cost values to assign the costs to the key routes.

16. The method according to claim 15, wherein the step of comparing comprises:

assigning a cost to a key route having an optimal value for the cost if the cost has a numerical value, and a cost to each of the key routes if the cost has a non-numerical value.

17. The method according to claim 13, further comprising:

classifying the history routes of all users in accordance with a certain criterion before the step of extracting the key routes; and

classifying the history routes of the user in accordance with the certain criterion before the step of extracting the feature routes.

18. The method according to claim 17, wherein the certain criterion comprises any of time periods in a day, date type, and weather condition.

19. The method according to claim 12, wherein the step of obtaining and extracting comprises:

extracting the feature routes of the user from the key routes based upon the history routes of the user; and

calculating weights for the route choice preference of the user based upon the costs of the feature routes of the user, and the route choice preference of the user based upon the weights.

20. The method according to claim 19, wherein the step of extracting comprises:

extracting, from the history routes of the user, a path which is identical to a key route and has a frequency of occurrence that is larger than a second threshold as a feature route of the user.

21. The method according to claim 19, further comprising:

classifying the history routes of the user in accordance with geographical regions before the step of extracting the feature routes.

22. The method according to claim 21, wherein the step of classifying comprises:

dividing a map into a plurality of regions, and classifying the plurality of regions as active regions and non-active regions in accordance with a number of the history routes of the user and a number of total travel times for the history routes of the user.