APPARATUS AND METHOD FOR TRANSIT PREDICTION

• US 20090005984A1
• Filed: 06/02/2008
• Published: 01/01/2009
• Est. Priority Date: 05/31/2007
• Status: Abandoned Application

• Current Assignee: James Roy Bradley
• Original Assignee: James Roy Bradley
• Inventors: Bradley, James Roy
• Application Number:
  US12/131,830
• Publication Number:
  US 20090005984A1
• Time in Patent Office: Days
• US Class Current: 701/213
• CPC Class Codes: G01C 21/20 Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence

Abstract

A transit predictor can be carried by a person or vehicle when attempting to travel through one or more traffic signals that repeatedly change state. The predictor has a data source with a navigational output indicating carrier location and a timing output indicating time. The transit predictor also has a processor with a memory. The processor can record in the memory a plurality of background data from the data source signifying (a) one or more carrier locations where data from the data source indicate the carrier was substantially stationary, and (b) one or more times when data from the data source indicate the carrier ceased being substantially stationary. The processor can algorithmically derive from the plurality of background data a predicted future event that will occur around a predicted time, at a location where, according to the background data, the carrier previously ceased being substantially stationary.

Claims

1. A transit predictor to be carried by a carrier such as a person or vehicle when attempting to travel through one or more traffic signals that have a repeating change of state, the transit predictor comprising:

a data source having a navigational output indicating carrier location and a timing output indicating time; and

a processor with a memory, said processor being coupled to said data source for recording in said memory a plurality of background data from said data source signifying (a) one or more carrier locations where data from said data source indicate the carrier was substantially stationary, and (b) one or more times when data from said data source indicate the carrier ceased being substantially stationary, said processor being operable to algorithmically derive from said plurality of background data a predicted future event that will occur around a predicted time at a location where, according to said background data, said carrier previously ceased being substantially stationary.

2. A transit predictor according to claim 1 wherein said processor is operable to record in said memory among said plurality of background data one or more times when said navigational output and said timing output indicate the carrier became substantially stationary.

3. A transit predictor according to claim 2 comprising:

an interface for presenting to a user information about said predicted future event in the form of a prediction of when at least one of said one or more traffic signals will change state.

4. A transit predictor according to claim 2 wherein said processor is operable to algorithmically derive from said plurality of background data a plurality of times and locations for a plurality of predicted future events.

5. A transit predictor according to claim 1 wherein said processor is operable to fit into a periodic pattern at least some times from said background data that spatially correlate to equivalent ones of the carrier locations, said periodic pattern having times separated by multiples of a postulated cycle time.

6. A transit predictor according to claim 5 wherein said processor is operable to derive said postulated cycle time by:

(a) selecting time pairs from said background data, restricting selections to times that correlate to equivalent ones of the carrier locations, and

(b) calculating a time difference between times of each selected time pair and normalizing the time difference by effectively dividing it by the greatest integer producing a normalized shift having a magnitude of less than twenty four hours.

7. A transit predictor according to claim 6 wherein said processor is operable to derive said postulated cycle time by:

(a) for each normalized shift, selecting from said background data a plurality of test times that correlate to carrier locations associated with said normalized shift, and

(b) determining if pairs of said test times differ approximately by an integer multiple of the normalized shift, in order to determine a consistency rating for said normalized shift.

8. A transit predictor according to claim 7 wherein said processor is operable to record in said memory among said plurality of background data one or more times when said navigational output and said timing output indicate the carrier became substantially stationary, said processor being operable to derive said postulated cycle time by:

(a) for spatially correlated ones of said carrier locations in said background data, finding a maximum duration during which the carrier remained substantially stationary, and

(b) for each pair of test times, determining if a first one of the test times substantially precedes by less than the maximum duration, a time obtained by arithmetically combining a second one of said test times and an integer multiple of the normalized shift, in order to determine the consistency rating for the normalized shift.

9. A transit predictor according to claim 5 wherein said processor is operable to

(a) fetch from said background data nearby carrier locations within a predetermined distance from a carrier location provided currently by said navigational output, and

(b) calculate an estimated time of arrival for at least some of said nearby carrier locations.

10. A transit predictor according to claim 4 wherein said processor is operable using predetermined criteria to calculate a reliability rating for locations associated with the plurality of predicted future events.

11. A transit predictor according to claim 10 comprising:

an interface for presenting to a user information about the plurality of predicted events in the form of one or more predictions of when said one or more traffic signals will change state, together with information about the reliability rating for one or more locations associated with said plurality of predicted future events.

12. A transit predictor according to claim 2 wherein said interface presents information about the reliability rating for one or more locations associated with said plurality of predicted future events in the form of at least one of (a) a visible signal, (b) a color coded visible signal, (c) an audible signal, and (d) an audible signal with an intensity correlated to the reliability rating.

13. A transit predictor according to claim 2 wherein said data source comprises:

a global positioning satellite system for providing said navigational output in the form of latitude and longitude.

14. A transit predictor according to claim 2 wherein said data source comprises one or more of a global positioning satellite system, an inertial navigational reference, a LORAN sensor, an Omega navigation system sensor, and an RNAV receiver.

15. A transit predictor according to claim 1 wherein said data source comprises:

a device for receiving information from a cellphone infrastructure in order to produce by triangulation said navigational output.

16. A transit predictor according to claim 1 wherein said data source has a first device for providing said navigational output and a second device for independently providing said timing output.

17. A transit predictor according to claim 1 wherein said processor compares successive data from said navigational output to calculate and store a value corresponding to carrier direction.

18. A transit predictor according to claim 1 wherein said processor is operable to store in memory from said data source one or more measured pairs, each member of each pair having a location value and a time value substantially coincident with a change in a carrier state of being substantially stationary.

19. A transit predictor according to claim 18 wherein said one or more measured pairs are divided into one or more intersection groups wherein members from the same one of said one or more intersection groups have a location value that is substantially equivalent to within a predetermined tolerance.

20. A transit predictor according to claim 1 wherein said processor is operable to store in memory one or more measured triplets, each having a location value and two time values corresponding to a commencement and conclusion of an interval wherein the carrier is substantially stationary.

21. A transit predictor according to claim 1 comprising:

a user operable device enabling a user to convey to said processor one or more observations that are compared by said processor with data from said data source in order to refine at least a contemporaneous one of said plurality of background data for said memory.

22. A transit predictor according to claim 21 wherein said user operable device is operable to convey to said processor a time that one of said one or more traffic signals changed state.

23. A transit predictor according to claim 21 wherein said user operable device is operable to convey a time that one of said one or more traffic signals provided a signal permitting non-turning departure.

24. A transit predictor according to claim 23 wherein said user operable device is operable to convey a time that one of said one or more traffic signals provided a signal permitting turning departure.

25. A transit predictor according to claim 1 comprising:

a two dimensional display showing intersections annotated with information about said predicted future event.

26. A transit predictor according to claim 25 wherein said processor is operable to algorithmically derive from said plurality of background data a plurality of times and locations for a plurality of predicted future events, said display being annotated with information about said plurality of predicted future events distributed at spaced positions.

27. A transit predictor according to claim 4 wherein said processor is operable to calculate whether a synchronized group selected from the plurality of predicted future events are synchronized to have times of occurrence that approximate a linear function of distance along a route, said synchronized group being at least three in number.

28. A transit predictor according to claim 27 comprising:

a two dimensional display showing a plurality of linked intersections, some of the linked intersections being displayed as an uninterrupted route that is overlaid with a linear overlay and marks a spatially contiguous series from the synchronized group.

29. A transit predictor according to claim 28 wherein said linear overlay is colored to distinguish at least part of the spatially contiguous series that has the same state, said processor updates and linearly adjusts said overlay to account for state changes within the spatially contiguous series.

30. A transit predictor according to claim 29 wherein said linear overlay has differently colored bands joined end to end in order to segregate the spatially contiguous series into spatially contiguous subgroups distinguished by having the same state and color.

31. A transit predictor according to claim 30 wherein said processor is operable to calculate whether an unsynchronized group from the plurality of predicted future events have times of occurrence that cannot be approximated as a linear function of distance along a route, said synchronized group being at least three in number.

32. A transit predictor according to claim 3 comprising:

a power sensor coupled to said processor for signaling thereto a decline in power supplied to said processor, said memory having a volatile section and a non-volatile section, said processor being operable in response to signaling from said power sensor to transfer data from the volatile section to the non-volatile section of said memory.

33. A transit predictor according to claim 1 comprising:

a wireless transceiver coupled to said processor for establishing communications with an external network, said processor being operable to download from said network at least one of;

(a) supplementary data that can be used to supplement the background data, and (b) a schedule of state changes for said one or more traffic signals.

34. A transit predictor according to claim 33 wherein said processor is operable to upload to said network at least one of:

(a) time and location of said predicted event, (b) at least a portion of said background data, and (c) information derived from said background data.

35. A predictive method employing a memory for use with a carrier such as a person or vehicle when attempting to travel through one or more traffic signals that have a repeating change of state, the method including the steps of:

recurrently providing a navigational output indicating carrier location;

recurrently providing a timing output indicating time;

using said navigational and said timing outputs, recording in said memory a plurality of background data signifying (a) one or more carrier locations where the carrier was substantially stationary, and (b) one or more times when the carrier ceased being substantially stationary; and

algorithmically deriving from said plurality of background data a predicted future event that will occur around a predicted time at a location where, according to said background data, said carrier previously ceased being substantially stationary.

36. A predictive method according to claim 35 comprising the step of:

recording in said memory among said plurality of background data one or more times when said navigational output and said timing output indicate the carrier became substantially stationary.

37. A predictive method according to claim 36 comprising:

presenting to a user information about said predicted future event in the form of a prediction of when at least one of said one or more traffic signals will change state.

38. A predictive method according to claim 36 comprising the step of:

algorithmically deriving from said plurality of background data a plurality of times and locations for a plurality of predicted future events.

39. A predictive method according to claim 35 comprising the step of:

fitting into a periodic pattern at least some times from said background data that correlate to equivalent ones of the carrier locations, said periodic pattern having times separated by multiples of a postulated cycle time.

40. A predictive method according to claim 39 wherein said postulated cycle time is derived by:

selecting time pairs from said background data, restricting selections to times that correlate to equivalent ones of the carrier locations, and

calculating a time difference between times of each selected time pair and normalizing the time difference by effectively dividing it by the greatest integer producing a normalized shift having a magnitude of less than twenty four hours.

41. A predictive method according to claim 40 wherein said postulated cycle time is derived by:

for each normalized shift, selecting from said background data a plurality of test times that correlate to carrier locations associated with said normalized shift, and

determining if pairs of said test times differ approximately by an integer multiple of the normalized shift, in order to determine a consistency rating for said normalized shift.

42. A predictive method according to claim 41 comprising the step of:

recording in said memory among said plurality of background data one or more times when said navigational output and said timing output indicate the carrier became substantially stationary, said postulated cycle time being derived by;

for spatially correlated ones of said carrier locations in said background data, finding a maximum duration during which the carrier remained substantially stationary, and

for each pair of test times, determining if a first one of the test times substantially precedes by less than the maximum duration, a time obtained by arithmetically combining a second one of said test times and an integer multiple of the normalized shift, in order to determine the consistency rating for the normalized shift.

43. A predictive method according to claim 39 comprising the steps of:

fetching from said background data nearby carrier locations within a predetermined distance from a carrier location provided currently by said navigational output, and

calculating an estimated time of arrival for at least some of said nearby carrier locations.

44. An arrangement for a carrier that attempts intersection crossings exploiting GPS for time and position information, supplements data and timing information with network data, shares data with network elements, collects, filters, with artificial intelligence, deduces meta information, annunciates, annotates on a display, proximal and subsequeal, including projected light phase, remaining time, synchronizing aspects including publishable speed to be made good to make the next green phase, traffic implications thereof, high reliability deductions in conventional traffic light colors of amber, red, green and less than reliable information in non-conventional colors such as blue, and removing such colors a slight time prior to state change, for the present intersection.