Vehicle Prediction Module

• US 20170270790A1
• Filed: 05/13/2016
• Published: 09/21/2017
• Est. Priority Date: 05/15/2015
• Status: Abandoned Application

• Current Assignee: New York City Transit Authority (Metropolitan Transportation Authority)
• Original Assignee: New York City Transit Authority (Metropolitan Transportation Authority)
• Inventors: Neiger, Bruce D., Matuskiewicz, Philip Jon, Laidig, David A., Frumin, Michael S.
• Application Number:
  US15/154,675
• Publication Number:
  US 20170270790A1
• Time in Patent Office: Days
• CPC Class Codes: G08G 1/127 to a central station ; Indicators in a central station

Abstract

The present invention improves the way how a vehicle'"'"'s arrival time at a stop may be determined. When a system receives at least one vehicle location record, it may extract trip data from such report. While previous trip data comprising scheduled travel time, historical travel times, and recent travel times with respect to the same vehicle'"'"'s trip along a route may be extracted from a database, trip data from recent vehicle location records may be uploaded into the database as updates. The most recent stop and at least one future stop may be identified. Using both extracted and updated data, the present invention may establish links between stops, determine a travel distance for each link, and estimate a link travel time for each travel distance based upon a weighted composite. Link travel times may be aggregated to determine estimated times of arrival, which can be uploaded for user retrieval.

Claims

1. A method for determining the estimated time of arrival of a vehicle at a stop, said method comprising:

a. receiving at least one vehicle location record in timed intervals from said vehicle on trip via a processor on a computing device;

b. extracting trip data from said vehicle location record via said processor on a computing device;

c. extracting previous trip data from a database via a processor on a computing device, the previous trip data comprising;

i. a scheduled travel time for each stop on said trip;

ii. historical travel times; and

iii. recent travel times;

d. updating said database with said trip data via said processor on a computing device;

e. identifying a most recent stop and at least one future stop corresponding to said trip based on said trip data via said processor on a computing device;

f. establishing a link via said processor on a computing device;

i. between said most recent stop and a first future stop; and

ii. where there are at least two of said future stop, between each of said future stop;

g. determining a travel distance for each of said link via said processor on a computing device;

h. determining a link travel time estimate, via said processor on a computing device, wherein said link travel time estimate corresponds to each of said travel distance, by using a weighted composite comprising;

i. said scheduled travel time;

ii. said historical travel times;

iii. said recent travel times;

or

iv. a scalable, weighted combination thereof;

i. generating, via said processor on a computing device, said ETA for each of said future stop by cumulatively adding said link travel time estimate of each said future stop to a current time; and

j. uploading, via said processor on a computing device, said ETA for each of said future stop into said database for retrieval.

2. The method according to claim 1, further including uploading, via said processor on a computing device, said ETA for each of said future stop into a memory.

3. The method according to claim 1, wherein said travel distance for said vehicle that has departed said most recent stop is the untraveled distance between the current position of said vehicle and said first future stop.

4. The method according to claim 3, wherein said link travel time estimate for said travel distance is the time it takes for said vehicle to travel said untraveled distance.

5. The method according to claim 1, wherein said scheduled travel time is about 20% of said weighted composite.

6. The method according to claim 1, wherein said historical travel time is about 30% of said weighted composite.

7. The method according to claim 1, wherein said recent travel time is about 50% of said weighted composite.

8. The method according to claim 1, wherein if said most recent stop is a terminal stop, then said first future stop is the first immediate stop of a next trip.

9. A processor on a computing device configured for determining a vehicle'"'"'s estimated time of arrival comprising:

a. a vehicle location record receiving module configured for receiving at least one vehicle location record, at intervals, from said vehicle on a trip;

b. a data processor configured for;

i. extracting trip data from said vehicle location record;

ii. extracting previous trip data from a database, the previous trip data comprising;

1. a scheduled travel time for each stop on said trip;

2. historical travel times; and

3. recent travel times;

iii. updating said database with said trip data;

iv. identifying a most recent stop and at least one future stop corresponding to said trip based on said trip data;

v. establishing a link;

1. between said most recent stop and a first future stop; and

2. where there are at least two of said future stop, between each of said future stop;

vi. determining a travel distance for each of said link;

vii. determining a link travel time estimate corresponding to each of said travel distance using a weighted composite comprising;

1. said scheduled travel time;

2. said historical travel times;

3. said recent travel times;

or

4. a scalable, weighted combination thereof;

viii. generating said ETA for each of said future stop by cumulatively adding said link travel time estimate of each said future stop to a current time set by said server; and

ix. uploading said ETA for each of said future stop into said database for retrieval; and

c. a memory.

10. The processor according to claim 9, wherein the data processor comprises:

a. a data extraction module configured for;

i. the extracting of said trip data from said vehicle location record;

ii. the extracting previous trip data from said database, the previous trip data comprising;

1. said scheduled travel time for each stop on said trip;

2. said historical travel times; and

3. said recent travel times;

b. a data uploading module configured for the updating of said database with said trip data;

c. a stop identifier module configured for the identifying of said most recent stop and said at least one future stop corresponding to said trip based on said trip data;

d. a link generator module configured for the establishing of said link;

1. between said most recent stop and said first future stop; and

2. where there are said at least two of said future stop, between each of said future stop;

e. a travel distance determiner module configured for the determining of said travel distance for each of said link;

f. a link travel time estimation module configured for the determining of said link travel time estimate corresponding to each of said travel distance using said weighted composite comprising;

1. said scheduled travel time;

2. said historical travel times;

3. said recent travel times;

or

4. said scalable, weighted combination thereof;

g. an ETA generator module configured for the generating of said ETA for each of said future stop by cumulatively adding said link travel time estimate of each said future stop to said current time; and

h. an ETA uploading module configured for the uploading of said ETA for each of said future stop into said database for retrieval.

11. The processor according to claim 9, wherein said travel distance for said vehicle that has departed said most recent stop is the untraveled distance between the current position of said vehicle and said first future stop.

12. The processor according to claim 11, wherein said link travel time estimate for said travel distance is the time it takes for said vehicle to travel said untraveled distance.

13. The processor according to claim 9, wherein said scheduled travel time is about 20% of said weighted composite.

14. The processor according to claim 9, wherein said historical travel time is about 30% of said weighted composite.

15. The processor according to claim 9, wherein said recent travel time is about 50% of said weighted composite.

16. The processor according to claim 9, wherein if said most recent stop is a terminal stop, then said first future stop is the first immediate stop of a next trip.

17. A non-transitory, tangible computer readable medium including instructions for performing a method, when executed by at least one processor on a computing device, for determining the estimated time of arrival of a vehicle for a stop, said method comprising:

a. receiving at least one vehicle location record in timed intervals from said vehicle on trip via a processor on a computing device;

b. extracting trip data from said vehicle location record via said processor on a computing device;

c. extracting previous trip data from a database via said processor on a computing device, the previous trip data comprising;

i. a scheduled travel time for each stop on said trip;

ii. historical travel times; and

iii. recent travel times;

d. updating said database with said trip data via said processor on a computing device;

e. identifying a most recent stop and at least one future stop corresponding to said trip based on said trip data via said processor on a computing device;

f. establishing a link via said processor on a computing device;

i. between said most recent stop and a first future stop; and

ii. where there are at least two of said future stop, between each of said future stop;

g. determining a travel distance for each of said link via said processor on a computing device;

h. determining a link travel time estimate, via said processor on a computing device, wherein said link travel time estimate corresponds to each of said travel distance, by using a weighted composite comprising;

i. said scheduled travel time;

ii. said historical travel times;

iii. said recent travel times;

or

iv. a scalable, weighted combination thereof;

i. generating, via said processor on a computing device, said ETA for each of said future stop by cumulatively adding said link travel time estimate of each said future stop to a current time; and

j. uploading, via said processor on a computing device, said ETA for each of said future stop into said database for retrieval.

18. The non-transitory, tangible computer readable medium according to claim 17, wherein said travel distance for said vehicle that has departed said most recent stop is the untraveled distance between the current position of said vehicle and said first future stop.

19. The non-transitory, tangible computer readable medium according to claim 18, wherein said link travel time estimate for said travel distance is the time it takes for said vehicle to travel said untraveled distance.

20. The non-transitory, tangible computer readable medium according to claim 17, wherein about 20% of said weighed composite is scheduled travel time, about 30% of said weighted composite is historical travel time, and about 50% of said weighted composite is said recent travel time.