METHOD FOR ENHANCING TRANSIT SCHEDULE

• US 20130096969A1
• Filed: 06/17/2011
• Published: 04/18/2013
• Est. Priority Date: 06/17/2010
• Status: Abandoned Application

• Current Assignee: Clever Devices, Ltd.
• Original Assignee: Clever Devices, Ltd.
• Inventors: Karanicolas, Christos
• Application Number:
  US13/704,915
• Publication Number:
  US 20130096969A1
• 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 50/40 Business processes related to the transportation industry shipping G06Q10/083

Abstract

A method and apparatus are provided for generating an enhanced transit schedule. Schedule deviations are calculated using an existing transit schedule. The schedule deviations are grouped in accordance with a plurality of schedule parameters. A group average deviation is computed for each group of schedule deviations. Each group average deviation is applied to a corresponding set of passing times of the existing transit schedule having corresponding schedule parameters to generate the enhanced transit schedule.

Claims

1. A method for generating an enhanced transit schedule, the method comprising the steps of:

calculating schedule deviations using an existing transit schedule;

grouping the schedule deviations in accordance with a plurality of schedule parameters;

computing a group average deviation for each group of schedule deviations;

applying each group average deviation to a corresponding set of passing times of the existing transit schedule having corresponding schedule parameters to generate the enhanced transit schedule; and

outputting one or more elements of the enhanced transit schedule to a display.

2. The method of claim 1, wherein calculating schedule deviations comprises:

accessing the existing transit schedule;

accessing historical passing times;

compiling a schedule adherence data set that stores average schedule deviations for every route, stop and direction combination using the existing transit schedule and the historical passing times.

3. The method of claim 1, wherein computing the group average deviation comprises:

calculating an average schedule deviation for each date in each group; and

calculating the group average deviation for each group by exponentially weighting the average schedule deviations for each date.

4. The method of claim 3, wherein applying each group average deviation comprises:

applying each exponentially weighted average deviation to a corresponding set of passing times of the existing transit schedule having a corresponding time interval, route, direction and stop; and

generating the enhanced transit schedule based on the application of the exponentially weighted average deviations to the existing transit schedule.

5. The method of claim 1, wherein the plurality of schedule parameters comprise one or more of route, direction, stop and time interval.

6. The method of claim 5 wherein the time interval is an hour.

7. The method of claim 2, wherein the historical passing times are collected by an application in real-time.

8. The method of claim 1, wherein grouping the schedule deviations comprises:

grouping schedule adherence data for a predetermined number of weekdays, when a current transit day begins on a weekday;

grouping schedule adherence data for a predetermined number of Saturdays, when the current transit day begins on a Saturday; and

grouping schedule adherence data for a predetermined number of Sundays, when the current transit day begins on a Sunday or a holiday.

9. The method of claim 1, wherein the existing transit schedule is received from a transit authority.

10. The method of claim 3, wherein a smoothing factor of an exponentially weighted average is a number substantially close to 1.

11. The method of claim 3, wherein an exponentially weighted average gives more weight to more recent data.

12. An apparatus for generating an enhanced transit schedule, comprising:

a user input device;

a memory for storing an existing transit schedule and schedule deviations;

a processor for calculating schedule deviations using the existing transit schedule, grouping the schedule deviations in accordance with a plurality of schedule parameters, computing a group average deviation for each group of schedule deviations, and applying each group average deviation to a corresponding set of passing times of the existing transit schedule having corresponding schedule parameters to generate the enhanced transit schedule; and

a display for displaying at least a portion of the enhanced transit schedule.

13. The apparatus of claim 12, wherein the processor calculates the schedule deviations by accessing the existing transit schedule, accessing historical passing times, and compiling a schedule adherence data set that stores average schedule deviations for every route, stop and direction combination using the existing transit schedule and the historical passing times.

14. The apparatus of claim 12, wherein the processor computes the group average deviation by calculating an average schedule deviation for each date in each group, and calculating the group average deviation for each group by exponentially weighting the average schedule deviations for each date.

15. The apparatus of claim 14, wherein the processor applies each group average deviation by applying each exponentially weighted average deviation to a corresponding set of passing times of the existing transit schedule having a corresponding time interval, route, direction and stop, and generating the enhanced transit schedule based on the application of the exponentially weighted average deviations to the existing transit schedule.

16. The apparatus of claim 12, wherein the plurality of schedule parameters comprise one or more of route, direction, stop and time interval, and the time interval is an hour.

17. The method of claim 13, wherein the historical passing times are collected by an application in real-time.

18. The method of claim 12, wherein the processor groups the schedule deviations by grouping schedule adherence data for a predetermined number of weekdays, when a current transit day begins on a weekday, grouping schedule adherence data for a predetermined number of Saturdays, when the current transit day begins on a Saturday, and grouping schedule adherence data for a predetermined number of Sundays, when the current transit day begins on a Sunday or a holiday.

19. The method of claim 14, wherein a smoothing factor of an exponentially weighted average is a number substantially close to 1.

20. The method of claim 14, wherein an exponentially weighted average gives more weight to more recent data.