VENUE BASED REAL TIME CROWD MODELING AND FORECASTING

• US 20140122040A1
• Filed: 10/30/2012
• Published: 05/01/2014
• Est. Priority Date: 10/30/2012
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Marti, Lukas M.
• Granted Patent: US 9,449,121 B2
• Time in Patent Office: Days
• US Class Current: 703/6
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30

Abstract

Crowds of people within an environment can be modeled in real time. A multitude of mobile devices located within an environment can periodically transmit their geographical locations over networks to a remote server. The remote server can use these geographical locations to generate a current real-time model of a crowd of people who possess the mobile devices that transmitted the geographical locations. The remote server can transmit the model over networks back to the mobile devices. The mobile devices can use the received model to present useful information to the users of those mobile devices.

Claims

1. A method comprising:

sensing, using sensors of a particular mobile device, environmental data pertaining to an environment in which the particular mobile device is located;

sending the environmental data from the particular mobile device to a server that is separate from the particular mobile device;

receiving, at the mobile device, from the server, model data indicating current characteristics of the environment, which model data the server generated based at least in part upon (a) the environmental data that the server received from the particular mobile device and (b) other environmental data that the server received from one or more other mobile devices also located in the environment; and

presenting, through a user interface of the particular mobile device, information derived from the model data.

2. The method of claim 1, wherein said sensing and said sending are performed no later than a first moment in time, wherein said model data is first model data, and further comprising:

sensing, at a second moment in time that is later than the first moment in time, using the sensors of the particular mobile device, second environmental data pertaining to the environment in which the particular mobile device is located;

sending the second environmental data from the particular mobile device to the server;

receiving, at the mobile device, from the server, second model data indicating current characteristics of the environment, which second model data the server generated based at least in part upon (a) the second environmental data that the server received from the particular mobile device and (b) other environmental data that the server received from one or more other mobile devices after generating the first model data; and

presenting, through the user interface of the particular mobile device, information derived from the second model data.

3. The method of claim 1, wherein said sensing comprises the particular mobile device using a global positioning system of the mobile device to determine geographical coordinates of the mobile device automatically;

and wherein sending the environmental data comprises the particular mobile device sending the geographical coordinates.

4. The method of claim 1, wherein the model data indicates one or more regions within the environment at which mobile device densities and movement speeds indicate the presence of movement bottlenecks within the environment.

5. The method of claim 1, wherein presenting information derived from the model data comprises presenting, for each particular line of two or more lines in the environment, an estimated amount of time that will be required to pass through that particular line.

6. A method comprising:

receiving, at a server, from two or more mobile devices, environmental data pertaining to an environment in which each of the two or more mobile devices is located, wherein each of the two or more mobile devices sensed the environmental data using sensors;

generating, based at least in part on the environmental data received from each of the two or more mobile devices, model data indicating current characteristics of the environment; and

sending the model data to each of the two or more mobile devices;

wherein the model data indicates characteristics of the environment in addition to characteristics indicated within environmental data received from any single one of the two or more mobile devices.

7. The method of claim 6, wherein said environmental data is first environmental data, wherein said model data is first model data, wherein said receiving and said generating are performed no later than a first moment in time, and further comprising:

receiving, at the server, from the two or more mobile devices, second environmental data pertaining to the environment in which each of the two or more mobile devices is located, wherein each of the two or more mobile devices sensed the environmental data after the first moment in time;

generating, based at least in part on the second environmental data received from each of the two or more mobile devices, second model data indicating current characteristics of the environment; and

sending the second model data to each of the two or more mobile devices.

8. The method of claim 6, wherein the environmental data includes first geographical coordinates that a first mobile device of the two or more mobile devices automatically determined using a global positioning system of the first mobile device;

wherein the environmental data includes second geographical coordinates that a second mobile device of the two or more mobile devices automatically determined using a global positioning system of the second mobile device; and

wherein the first geographical coordinates differ from the second geographical coordinates.

9. The method of claim 6, wherein generating the model data further comprises:

determining one or more potential bottleneck regions within the environment at which at least a specified quantity of mobile devices occur with at least a specified density; and

determining one or more bottleneck regions within the environment at which an average rate of movement of mobile devices through the potential bottleneck regions is less than a specified speed.

10. The method of claim 6, wherein the model data indicates, for each particular line of two or more lines in the environment, an estimated amount of time that will be required to pass through that particular line.

11. A computer-readable memory storing instructions which, when executed by one or more processors, cause the one or more processors to perform:

based on data received over a network from a plurality of mobile devices currently located within a particular environment, determining, at a server, a plurality of queues that currently exist within the particular environment;

based on the data, determining, at the server, a wait time for each queue of the plurality of queues; and

sending, over the network, to one or more mobile devices of the plurality of mobile devices, information indicating a wait time for a particular queue of the plurality of queues.

12. The computer-readable memory of claim 11, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

determining that movements of the plurality of mobile devices within the environment are correlated with each other to an extent that exceeds a specified threshold; and

based at least in part on a determination that the movements of the plurality of mobile devices within the environment are correlated with each other to an extent that exceeds the specified threshold, determining that a queue exists at a location in which the correlated movements of the plurality of mobile devices occurred.

13. The computer-readable memory of claim 11, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

determining an amount of time taken for a particular mobile device to move from a first location to a second location;

based at least in part on the amount of time taken for the particular mobile device to move from the first location to the second location, estimating a predicted amount of time that will be required for a person to move through a particular queue that includes the first location and the second location; and

reporting the predicted amount of time to one or more mobile devices.

14. The computer-readable memory of claim 11, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

based at least in part on the wait times determined for each queue of the plurality of queues, determining a location at which an advertisement will be presented.

15. The computer-readable memory of claim 11, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

based at least in part on wait times determined for each queue of the plurality of queues over an interval of time, estimating a future wait time for each queue of the plurality of queues at a specified future time.

16. A computer-readable memory storing instructions which, when executed by one or more processors, cause the one or more processors to perform:

detecting a first radio frequency pattern at a first location using sensors of a first mobile device;

based on a match between the first radio frequency pattern and a second radio frequency pattern that is mapped to a first location, determining that the first mobile device is currently located at the first location; and

using the information that the mobile device is currently located at the first location in a determination of the presence of a motion bottleneck within an area containing the first location.

17. The computer-readable memory of claim 16, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

detecting a third radio frequency pattern at a second location using sensors of the first mobile device; and

uploading the third radio frequency pattern and coordinate data of the first mobile device to a server that is remote from the first mobile device.

18. The computer-readable memory of claim 16, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

detecting a third radio frequency pattern at a second location using sensors of the first mobile device;

uploading the third radio frequency pattern and coordinate data of the first mobile device to a server that is remote from the first mobile device;

uploading the first radio frequency pattern to the server; and

receiving, from the server, in response to uploading the first radio frequency pattern, an indication that the first mobile device is located at the first location.

19. The computer-readable memory of claim 16, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

uploading the first radio frequency pattern to a server; and

receiving, from the server, in response to uploading the first radio frequency pattern, an indication that the first mobile device is located at the first location;

wherein the server previously received, from a second mobile device that is separate from the first mobile device, information that correlated coordinates of the second mobile device with the second radio frequency pattern.

20. A computer-readable memory storing instructions which, when executed by one or more processors, cause the one or more processors to perform:

receiving, at a server, from each particular mobile device of a plurality of mobile devices, information that indicates a current location, direction, and speed of that particular mobile device;

aggregating, at the server, information received from each particular mobile device in order to estimate current crowd movement behaviors; and

transmitting, from the server, aggregated information regarding the current crowd movement behaviors to one or more recipients.

21. The computer-readable memory of claim 20, wherein the one or more recipients include at least one mobile device of the plurality of mobile devices.

22. The computer-readable memory of claim 20, wherein the aggregated information includes an amount of time that travel from a first specified location to a second specified location is currently estimated to require at the time of the transmitting.

23. The computer-readable memory of claim 20, wherein aggregating the information received from the mobile devices includes decreasing an influence of particular information received from mobile devices upon the aggregated information based at least in part on an age of that particular information.

24. The computer-readable memory of claim 20, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

receiving, at the server, from a particular mobile device, a planned route that a user of the particular mobile device plans to travel;

determining, based at least in part on the aggregated information, for each particular area of a plurality of areas through which the planned route passes, a quantity of time currently taken by other mobile device users to pass through that particular area;

based on the quantity of time determined for each area, calculating a total amount of time estimated to be required for the user of the particular mobile device to travel the planned route; and

transmitting, from the server to the particular mobile device, data including the total amount of time.

25. The computer-readable memory of claim 20, wherein the instructions, when executed by the one or more processors, cause the one or more processors to perform:

determining, based at least in part on the aggregated information, for each particular area of a plurality of areas contained with an environment, a quantity of time currently taken by other mobile device users to pass through that particular area; and

transmitting, from the server to a computing device of a manager of the environment, data indicating the quantities of time determined for each particular area of the plurality of areas.