DE-NOISING SCHEDULED TRANSPORTATION DATA

• US 20140094988A1
• Filed: 09/28/2012
• Published: 04/03/2014
• Est. Priority Date: 09/28/2012
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Bouillet, Eric P., Calabrese, Francesco, Verscheure, Olivier, Pinelli, Fabio
• Application Number:
  US13/629,939
• Publication Number:
  US 20140094988A1
• Time in Patent Office: Days
• US Class Current: 701/1
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/083 Shipping
• CPC Class Codes: G06Q 10/08355 Routing methods
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

Claims

1. A computer system for de-noising data in a scheduled transportation system, the computer system comprising:

a scheduling device having a processor, the processor configured to perform a method comprising;

receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system;

identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes;

identifying a plurality of stops made by the piece of transportation equipment during each of the plurality of journeys;

classifying each of the plurality of identified stops into a type of stop;

identifying a route map comprising at least a portion of the plurality of identified stops;

identifying a schedule for the scheduled transportation system; and

updating the route map and the schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

2. The computer system of claim 1, wherein each of the plurality of digital traces comprises a location, a time-stamp, and an identification of the piece of equipment in the scheduled transportation system.

3. The computer system of claim 1, wherein the type of stop comprises at least one of a scheduled stop and a non-scheduled stop.

4. The computer system of claim 1, wherein classifying each of the set of potential stops includes calculating a confidence level.

5. The computer system of claim 1, wherein the classifying comprises applying a partial ground truth.

6. The computer system of claim 1, wherein the identification of the schedule includes the identification of the arrival times of the piece of transportation equipment at scheduled stops.

7. The computer system of claim 1, wherein updating the route map and schedule includes removing one or more scheduled stops from the route map and schedule.

8. The computer system of claim 1, wherein updating the route map and schedule includes adding one or more scheduled stops to the route map and schedule.

9. The computer system of claim 1, wherein updating the route map and schedule includes correcting a characteristic of one or more scheduled stops of the route map and schedule.

10. The computer system of claim 9, wherein the characteristics of a scheduled stop include at least one of a location a list of lines serving the scheduled stop, a time of arrival of vehicles at the scheduled stop.

11. The computer system of claim 1, wherein classifying each of the plurality of identified stops into the type of stop further comprises:

clustering the plurality of stops along one of the plurality of routes into a set of potential stops;

computing a feature set for each of the set of potential stops; and

classifying each of the set of potential stops into the type of stop based on the feature set and a classification model.

12. A computer program product for de-noising data in a scheduled transportation system, the computer program product comprising:

a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising;

computer readable program code configured for;

a scheduling device having a processor, the processor configured to perform a method comprising;

receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system;

identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes;

identifying a plurality of stops made by the piece of transportation equipment during each of the plurality of journeys;

classifying each of the plurality of identified stops into a type of stop;

identifying a route map comprising at least a portion of the plurality of identified stops;

identifying a schedule for the scheduled transportation system; and

updating the route map and the schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.

13. The computer program product of claim 12, wherein each of the plurality of digital traces comprises a location, a time-stamp, and an identification of the piece of equipment in the scheduled transportation system.

14. The computer program product of claim 12, wherein the type of stop comprises at least one of a scheduled stop and a non-scheduled stop.

15. The computer program product of claim 12, wherein classifying each of the set of potential stops includes calculating a confidence level.

16. The computer program product of claim 12, wherein the classifying comprises applying a partial ground truth.

17. The computer program product of claim 12, wherein the identification of the schedule includes the identification of the arrival times of the piece of transportation equipment at scheduled stops.

18. The computer program product of claim 12, wherein updating the route map and schedule includes removing one or more scheduled stops from the route map and schedule.

20. The computer program product of claim 12, wherein updating the route map and schedule includes correcting a characteristic of one or more scheduled stops of the route map and schedule.

21. The computer program product of claim 20, wherein the characteristics of a scheduled stop include at least one of a location a list of lines serving the scheduled stop, a time of arrival of vehicles at the scheduled stop.

22. The computer program product of claim 12, wherein classifying each of the plurality of identified stops into the type of stop further comprises:

clustering the plurality of stops along one of the plurality of routes into a set of potential stops;

computing a feature set for each of the set of potential stops; and

classifying each of the set of potential stops into the type of stop based on the feature set and a classification model.

19. The computer program product of claim 23, wherein updating the route map and schedule includes adding one or more scheduled stops to the route map and schedule.