REAL-TIME PREDICTION AND CORRECTION OF SCHEDULED SERVICE BUNCHING

• US 20150039363A1
• Filed: 07/31/2013
• Published: 02/05/2015
• Est. Priority Date: 07/31/2013
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Calabrese, Francesco, Pinelli, Fabio, Nair, Rahul
• Granted Patent: US 9,858,542 B2
• Time in Patent Office: Days
• US Class Current: 705/7.13
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

A method includes: a computer receiving historical data from at least one service vehicle; the computer receiving network definitions for the least one service vehicle; the computer receiving dynamic data from the at least one service vehicle; and the computer developing predictions and corrective actions to prevent service bunching, based upon the historical data, the network definitions, and the dynamic data.

Claims

1. A method comprising:

a computer receiving historical data from at least one service vehicle;

the computer receiving network definitions for the least one service vehicle;

the computer receiving dynamic data from the at least one service vehicle; and

the computer developing predictions and corrective actions to prevent service bunching, based upon the historical data, the network definitions, and the dynamic data.

2. The method according to claim 1, wherein the predictions are potential future service bunching events along a network of scheduled services with associated accuracy metrics.

3. The method according to claim 1, further comprising the computer receiving at least one passenger model associated with the network definitions.

4. The method according to claim 1, wherein the corrective actions are optimizing plans to prevent predicted service bunching along a network of scheduled services.

5. The method according to claim 1, wherein the historical data includes past performance of at least one service vehicle.

6. The method according to claim 1, wherein the dynamic data is real time data from the at least one service vehicle.

7. The method according to claim 4, further comprising the computer sending the optimized plan to the at least one service vehicle.

8. The method according to claim 7, wherein the optimized plan is a vehicle control plan for the at least one service vehicle.

9. The method according to the claim 4, further comprising to the computer sending the optimized plan to a network infrastructure to prevent bunching.