Predicting Human Movement Behaviors Using Location Services Model

• US 20170195854A1
• Filed: 09/16/2014
• Published: 07/06/2017
• Est. Priority Date: 09/16/2014
• Status: Active Grant

• Current Assignee: Singapore Telecommunications Limited
• Original Assignee: Singapore Telecommunications Limited
• Inventors: Shi-Nash, Amy, Decraene, James Christian, Dang, The Agh
• Granted Patent: US 10,003,926 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/14 Details of searching files based on file metadata
• CPC Class Codes: G06N 7/06 Simulation on general purpose computers
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/025 using location based information parameters
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 4/30 Services specially adapted for particular environments, situations or purposes
• CPC Class Codes: H04W 4/42 for mass transport vehicles, e.g. buses, trains or aircraft

Abstract

Human behavior may be predicted by building a model of people'"'"'s physical movements along with a model that represents their affinities to various ontological elements, and their relationships to other people. The model may include a graph of interrelated people, as well as their hobbies, interests, employment, and other elements. The model may be analyzed by simulating people'"'"'s activities as those activities interact with real or hypothetical networks. The real networks may be used to verify the model by comparing measured parameters to predictive parameters derived from simulation to calibrate the models. A calibrated model may then be used with a modified or hypothetical network to analyze the effects of changes to the real network.

Claims

1. A method performed on at least one computer processor, said method comprising:

receiving a mobility pattern model comprising;

a transportation network representation having a plurality of location nodes and transportation links between said location nodes;

a plurality of human behavior representations, each of said human behavior representations derived from a human interaction with transportation mechanisms;

modeling human transportation using said mobility pattern model using a representation of a first transportation network to determine a first traffic pattern on a first location node;

modifying said transportation network representation to represent a second transportation network and modeling said human transportation using said mobility pattern model to determine a second traffic pattern on said second location node.

2. The method of claim 1, said second transportation network having at least one new location node added to said first transportation network.

3. The method of claim 1, said first transportation network representing an actual transportation network and said second transportation network representing a hypothetical transportation network.

4. The method of claim 3 further comprising:

measuring an actual traffic pattern at a physical location represented by said first location node; and

determining that said actual traffic pattern differs from said first traffic pattern and updating at least one of said plurality of human behavior representations.

5. The method of claim 1:

said first transportation network representing an actual transportation network at a first time with a first set of conditions;

said second transportation network representing said actual transportation network at a second time with a second set of conditions;

measuring a first actual traffic pattern observed at said first time and with said first set of conditions;

measuring a second actual traffic pattern observed at said second time and with said second set of conditions; and

modifying at least one human behavior representations based on said first actual traffic pattern and said second actual traffic pattern.

6. The method of claim 5, said first actual traffic pattern comprising transportation patterns measured using a plurality of devices carried by individual humans.

7. The method of claim 6, at least one of said plurality of devices being mobile telephones.

8. The method of claim 1, said human behavior representations being derived from cellular telephone location data.

9. The method of claim 8, said cellular telephone location data being anonymized data.

10. A system comprising:

at least one processor;

said processor configured to perform a method comprising;

receiving a mobility pattern model comprising;

a transportation network representation having a plurality of location nodes and transportation links between said location nodes;

a plurality of human behavior representations, each of said human behavior representations derived from a human interaction with transportation mechanisms;

modeling human transportation using said mobility pattern model using a representation of a first transportation network to determine a first traffic pattern on a first location node;

modifying said transportation network representation to represent a second transportation network and modeling said human transportation using said mobility pattern model to determine a second traffic pattern on said second location node.

11. The system of claim 10, said second transportation network having at least one new location node added to said first transportation network.

12. The system of claim 10, said first transportation network representing an actual transportation network and said second transportation network representing a hypothetical transportation network.

13. The system of claim 12, said method further comprising:

measuring an actual traffic pattern at a physical location represented by said first location node; and

determining that said actual traffic pattern differs from said first traffic pattern and updating at least one of said plurality of human behavior representations.

14. The system of claim 10:

said first transportation network representing an actual transportation network at a first time with a first set of conditions;

said second transportation network representing said actual transportation network at a second time with a second set of conditions;

said method further comprising;

measuring a first actual traffic pattern observed at said first time and with said first set of conditions;

measuring a second actual traffic pattern observed at said second time and with said second set of conditions; and

modifying at least one human behavior representations based on said first actual traffic pattern and said second actual traffic pattern.

15. The system of claim 14, said first actual traffic pattern comprising transportation patterns measured using a plurality of devices carried by individual humans.

16. The system of claim 15, at least one of said plurality of devices being mobile telephones.

17. The system of claim 10, said human behavior representations being derived from cellular telephone location data.

18. The system of claim 17, said cellular telephone location data being anonymized data.