Methods and systems for tracking the flow of trucking freight and/or other assets using mobile device geolocation data

• US 10,528,604 B2
• Filed: 01/30/2018
• Issued: 01/07/2020
• Est. Priority Date: 01/30/2018
• Status: Active Grant

• Current Assignee: Sunrise Opportunities, LLC
• Original Assignee: Sunrise Opportunities, LLC
• Inventors: Nickerson, Kenneth S.
• Primary Examiner(s): Nguyen, Thu N
• Application Number:
  US15/884,175
• Publication Number:
  US 20190236165A1
• Time in Patent Office: 707 Days
• Field of Search: None
• CPC Class Codes: G01S 19/13 Receivers
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management

Abstract

Presented herein are systems, methods, and architectures related to the use of mobile device geolocation data to infer and record specific detailed freight movements. In certain embodiments, using mobile device geolocation data, a freight movement database is created which may contain thousands of records each day describing in detail inferred individual freight movements. For example, each record in a freight movement database may contain information about origins, stops, and/or destinations such as dates, times, and locations of freight stops and movements. In certain embodiments, mobile device geolocation is used to create, maintain, and use a freight movement database. Such a freight movement database fills an important need in the industry because of the current lack of comprehensive and reliable information related to freight movements.

Claims

1. A method of tracking freight movement by identifying and tagging freight movement data from a subset of mobile device geolocation data recorded over time from a plurality of mobile devices, the method comprising:

receiving, by a processor of a computing device, a collection of mobile device geolocation data from mobile devices of users who have not been identified previously as freight vehicle operators, wherein the collection of mobile device geolocation data comprises global positioning system (GPS) geolocation data comprising position information for each of the plurality of mobile devices;

automatically identifying, by the processor of the computing device, a subset of the collection of mobile device geolocation data as freight vehicle operator data (FVOD) using values of derived variables corresponding, collectively, to (a) patterns of time traveled, (b) distances traveled, and (c) visits to locations of relevance to freight vehicle operations, said values derived from the mobile device geolocation data itself,

wherein the FVOD corresponds to mobile device geolocation data for mobile devices associated with freight vehicle operators;

automatically identifying, by the processor, a subset of (equal in size or smaller than) the FVOD as freight movement data, wherein the freight movement data corresponds to movements of freight;

tagging, by the processor, the freight movement data with searchable lead characteristics; and

storing, by the processor and tagged freight movement database (FMD).

2. The method of claim 1, wherein the mobile device geolocation data further comprise one or more members selected from the group consisting of (i) telecommunications data and (ii) WIFI location data.

3. The method of claim 1, comprising receiving, by the processor, the mobile device geolocation data as an update and storing the mobile device geolocation data in an existing database.

4. The method of claim 1, wherein the step of storing the freight movement data in the freight movement database is performed in real time.

5. The method of claim 1, wherein the mobile device geolocation data are received as one or more data streams from one or more providers of mobile device geolocation data from a source, wherein the source is a member selected from the group consisting of one or more smart phone applications (apps), one or more software development kits (SDKs), and one or more operating systems.

6. The method of claim 1, comprising determining whether the mobile devices associated with freight vehicle operators stop at one or more predetermined places of interest using one or more methods selected from the group consisting of:

(i) converting position information from the mobile device geolocation data to one or more addresses using reverse geocoding and determining whether the one or more addresses correspond to a predetermined place of interest;

(ii) using a proximity of a position of the mobile device to a predetermined place of interest; and

(iii) determining whether the position of the mobile device is within a geofenced boundary associated with a predetermined place of interest.

7. The method of claim 1, comprising:

partitioning the FVOD by operator type; and

tagging one or both members selected from the group consisting of the FVOD and the partitioned FVOD with operator characteristics representative of each operator type.

8. The method of claim 1, comprising identifying professional vehicle operator data (PVOD).

9. The method of claim 8, comprising:

partitioning the professional vehicle operator data (PVOD) by professional operator type; and

tagging one or more members selected from the group consisting of the PVOD and the partitioned PVOD with operator characteristics representative of each operator type.

10. The method of claim 1, comprising identifying operator characteristics, wherein the operator characteristics comprise one or more members selected from the group consisting of:

(i) driver type, (ii) company, (iii) weekly hours driven, (iv) weekly miles driven, and (v) regular routes driven.

11. The method of claim 1, comprising partitioning the FVOD into on-duty and off-duty data using one or more members selected from the group consisting of one or more heuristics and one or more artificial intelligence modules applied to one or more members selected from the group consisting of:

(i) patterns of movement regularity by time and one or more members selected from the group consisting of day of the week and date, (ii) patterns of one or more members selected from the group consisting of road travel route(s), road travel time(s) and road travel distance(s), (iii) patterns of geographic and temporal regularity of movements, (iv) patterns of geographic and temporal irregularity of movements;

(v) operator characteristics;

(vi) a frequency of stops at predetermined places of interest associated with one or more operator characteristics, (vii) a timing of stops at predetermined places of interest associated with one or more operator characteristics;

(viii) a determination of whether one or more members selected from the group consisting of a starting position, an ending position, and intermediary stop(s) are associated with one or more predetermined places of interest;

(ix) a frequency of stops by the mobile device at positions associated with residences, (x) a pattern of stops by the mobile device at positions associated with residences, (xi) a determination of whether movements begin at, end at, or include any locations from a predetermined list associated with a home of a driver, (xii) a predetermined list comprising one or more members selected from the group consisting of rules and laws governing professional drivers.

12. The method of claim 1, comprising identifying freight movements from the FVOD using one or more members selected from the group consisting of one or more heuristics and one or more artificial intelligence modules applied to one or more members selected from the group consisting of:

(i) the on-duty time of a driver, (ii) the off-duty time of the driver;

(iii) operator characteristics;

(iv) one or more members selected from the group consisting of starting, ending, and intermediary stops at predetermined places of interest associated with a driver type, (v) one or more members selected from the group consisting of starting, ending, and intermediary stops at predetermined places of interest associated with a company;

(vi) a determination of an efficiency of a movement;

(vii) a set of predetermined characteristics associated with one or more members selected from the group consisting of stops and movements based on a driver type, (viii) a set of predetermined characteristics associated with one or more members selected from the group consisting of stops and movements based on a company;

(ix) a proximity of a business to stops and a determination of a probability that said stops correspond to a starting, ending, or intermediary position based on a driver type; and

(x) a determination of a probability that movements are performed in an absence of freight based on operator characteristics.

13. The method of claim 1, comprising identifying, based on the tagged freight movement data, one or more members selected from the group consisting of driver arrival times, driver arrival locations, driver departure times, driver departure locations, and visits to one or more places of interest.

14. The method of claim 13, further comprising performing one or more of the steps selected from the group consisting of recording, counting, aggregating, and summarizing one or more members selected from the group consisting of the driver arrival times, the driver arrival locations, the driver departure times, the driver departure locations, and the visits to one or more places of interest.

27. The method of claim 1, wherein identifying the subset of the collection of mobile device geolocation data for the plurality of mobile devices as FVOD comprises using one or more members selected from the group consisting of one or more heuristics and one or more artificial intelligence modules.

28. The method of claim 1, wherein the derived variables comprise one or more members selected from the group consisting of:

(i) an amount of time traveled in a given interval of time, (ii) a distance traveled in a given interval of time, (iii) a pattern of an amount of time traveled in a given interval of time, (iv) a pattern of a distance traveled in a given interval of time, (v) one or more patterns of geographic regularity of movements in a given interval of time, (vi) one or more patterns of temporal regularity of movements in a given interval of time, (vii) one or more patterns of geographic irregularity of movements in a given time interval, (viii) one or more patterns of temporal irregularity of movements in a given interval of time, (ix) a frequency of stops at predetermined places of interest in a given time interval, (x) a timing of stops at predetermined places of interest in a given interval of time, and (xi) a determination of whether one or more members selected from the group consisting of a starting position, an ending position, and intermediary stop(s) are associated with one or more predetermined places of interest.

29. The method of claim 1, wherein the load characteristics comprise one or more members selected from the group consisting of origin arrival time, origin business, origin business sub-industry, destination departure time, destination business, destination business sub-industry, intermediate stop arrival time, intermediate stop departure time, intermediate stop address, intermediate stop business, intermediate stop business sub-industry, carrier type, carrier company, and inferred contents descriptors.

30. The method of claim 1, wherein the load characteristics comprise origin departure time, origin address, destination arrival time, and destination address.

15. A method of tracking freight movement by operating a freight movement data management platform for characterizing and/or analyzing mobile device geolocation data recorded over time from a plurality of mobile devices, the method comprising:

providing access to a tagged subset of mobile device geolocation data for a plurality of mobile devices, wherein the tagged subset of mobile device geolocation data;

are derived from mobile device geolocation data of mobile devices of users who have not been identified previously as freight vehicle operators via the freight movement data management platform, wherein the mobile device geolocation data comprise global positioning system (GPS) geolocation data comprising position information for each of the plurality of mobile devices;

are identified as being associated with freight vehicle operators and as corresponding to loaded freight movement; and

Comprise searchable load characteristics;

responsive to receipt of a search request, retrieving, by a processor of the freight movement data management platform, a result data set selected or derived from the tagged subset of mobile device geolocation data; and

causing, by the processor of the freight movement data management platform, the result data set to be one or both of (i) and (ii) as follows;

(i)displayed and (ii) stored in memory.

16. The method of claim 15, wherein the mobile device geolocation data further comprise one or more members selected from the group consisting of (i) telecommunications data and (ii) WIFI location data.

17. The method of claim 15, wherein the mobile device geolocation data are received as one or more data streams from one or more providers of mobile device geolocation data from a source, wherein the source is a member selected from the group consisting of one or more smart phone applications (apps), one or more software development kits (SDKs), and one or more operating systems.

18. The method of claim 15, wherein the mobile device geolocation data are sequentially recorded from a mobile device.

19. The method of claim 15, wherein the result data set comprises one or more members selected from the group consisting of a time of travel, a distance of travel, a speed of travel, and routes of travel.

20. The method of claim 15, wherein the result data set comprises one or more members selected from the group consisting of time periods of vehicle operation, a distance of travel, and a speed of travel.

21. The method of claim 15, wherein the result data set comprises a probability that one or more of the plurality of mobile devices stop at one or more predetermined places of interest, wherein the probability is determined using one or more methods selected from the group consisting of:

converting position information from the mobile device geolocation data to one or more addresses using reverse geocoding and determining whether the one or more addresses correspond to a predetermined place of interest;

(ii) using a proximity of a position of the mobile device to a predetermined place of interest; and

(iii) determining whether the position of the mobile device is within a geofenced boundary associated with a predetermined place of interest.

22. The method of claim 15, comprising identifying a subset of (equal in size or smaller than) the tagged subset of mobile device geolocation data as FVOD by applying one or more members selected from the group consisting of one or more heuristics and one or more artificial intelligence modules to one or more members selected from the group consisting of:

(i) an amount of time traveled in a given interval of time, (ii) a distance traveled in a given interval of time, (iii) a pattern of an amount of time traveled in a given interval of time, (iv) a pattern of a distance traveled in a given interval of time, (v) one or more patterns of geographic regularity of movements in a given interval of time, (vi) one or more patterns of temporal regularity of movements in a given interval of time, (vii) one or more patterns of geographic irregularity of movements in a given interval of time, (viii) one or more patterns of temporal irregularity of movements in a given interval of time, (ix) a frequency of stops at predetermined places of interest in a given interval of time, (x) a timing of stops at predetermined places of interest in a given interval of time, and (xi) a determination of whether one or more members selected from the group consisting of a starting position, an ending position, and intermediary stop(s) are associated with one or more predetermined places of interest.

23. The method of claim 15, comprising identifying a subset of the tagged subset of mobile device geolocation data as professional vehicle operator data (PVOD) using one or more members selected from the group consisting of one or more heuristics and one or more artificial intelligence modules applied to one or more members selected from the group consisting of:

(i) an amount of time traveled in a given interval of time, (ii) a distance traveled in a given interval of time, (iii) a pattern of an amount of time traveled in a given interval of time, (iv) a pattern of a distance traveled in a given interval of time, (v) one or more patterns of geographic regularity of movements in a given interval of time, (vi) one or more patterns of temporal regularity of movements in a given interval of time, (vii) one or more patterns of geographic irregularity of movements in a given interval of time, (viii) one or more patterns of temporal irregularity of movements in a given interval of time, (ix) a frequency of stops at predetermined places of interest in a given interval of time, (x) a timing of stops at predetermined places of interest in a given interval of time, (xi) a determination of whether one or more members selected from the group consisting of a starting position, an ending position, and intermediary stop(s) are associated with one or more predetermined places of interest, (xii) a frequency of stops at positions associated with residences in a given time interval, and (xiii) a pattern of stops at positions associated with residences in a given interval of time.

24. The method of claim 15, wherein the result data set comprises operator characteristics, wherein the operator characteristics comprise one or more members selected from the group consisting of:

(i) driver type, (ii) company, (iii) weekly hours driven, (iv) weekly miles driven, and (v) regular routes driven.

25. The method of claim 15, wherein the result data set comprises one or more members selected from the group consisting of origin arrival time, origin departure time, origin address, origin business, origin business sub-industry, destination arrival time, destination departure time, destination address, destination business, destination business sub-industry, intermediate stop arrival time, intermediate stop departure time, intermediate stop address, intermediate stop business, intermediate stop business sub-industry, carrier type, carrier company, inferred contents descriptors, a driver arrival time, a driver arrival location, a driver departure time, a driver departure location, and a visit to a predetermined place of interest.

26. A freight movement data management platform for identifying and tagging freight movement data from a subset of mobile device geolocation data recorded over time from a plurality of mobile devices, the platform comprising:

a processor of a computing device; and

a memory, the memory storing instructions that, when executed by the processor, cause the processor to;

receive a collection of mobile device geolocation data from mobile devices of users who have not been identified previously as freight vehicle operators, wherein the collection of mobile device geolocation data comprises global positioning system (GPS) geolocation data comprising position information for each of the plurality of mobile devices;

automatically identify a subset of the collection of mobile device geolocation data as freight vehicle operator data (FVOD) using values of derived variables corresponding, collectively, to (a) patterns of time traveled, (b) distances traveled, and (c) visits to locations of relevance to freight vehicle operations, said values derived from the mobile device geolocation data itself;

automatically identify a subset of (equal in size or smaller than) the FVOD as freight movement data movement data corresponds to movement of freight;

tag the freight movement data with searchable load characteristics; and

store, in the memory the tagged freight movement data as a freight movement database (FMD).