Venue based real time crowd modeling and forecasting

Patent metadata

Field	Value
Publication number	US-9449121-B2
Application number	US-201213664354-A
Country	US
Kind code	B2
Filing date	Oct 30, 2012
Priority date	Oct 30, 2012
Publication date	Sep 20, 2016
Grant date	Sep 20, 2016

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.

First claim

What is claimed is:

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;
obtaining, at the particular mobile device, an estimated wait time for the particular mobile device to traverse a line;
wherein the estimated wait time was determined by:
utilizing an estimate of a quantity of mobile devices of people currently ahead of the particular mobile device in the line, a history of model data that was generated over time, and the environmental data in order to predict a current wait time in the environment;

wherein the history provides:
amounts of time that one or more mobile devices other than the particular mobile device took to traverse the line; and
quantities of other mobile devices of people that were in the line when the one or more mobile devices entered the line; and

presenting the estimated wait time through a user interface of the particular mobile device.

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, wherein the second environmental data includes at least one of a radio frequency (RF) fingerprint usable to estimate current geographical coordinates of the particular mobile device and the current geographical coordinates of the particular mobile device;
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 the current geographical coordinates of the mobile device automatically; and wherein sending the environmental data comprises the particular mobile device sending the current 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;
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;
generating a history of the model data over time, wherein the history provides:
amounts of time that the two or more mobile devices took to traverse a line; and
quantities of mobile devices of people that were in the line when the two or more mobile devices entered the line;

utilizing the history, an estimate of a quantity of people currently ahead of a particular mobile device in the line, and the environmental data, to determine an estimated wait time for the particular mobile device to traverse the line in order to predict a current wait time in the environment; and
sending the estimated wait time to the particular mobile device.

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, and wherein the second environmental data includes, for each of the two or more mobile devices, at least one of a radio frequency (RF) fingerprint usable to estimate current geographical coordinates and current geographical coordinates;
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, at a server:
generating a history of a model over time of a particular environment based on data received over a network from a plurality of mobile devices located within the particular environment over time;
determining, based on the model, a plurality of queues that currently exist within the particular environment;
wherein the history provides:
amounts of time that mobile devices in the plurality of mobile devices took to traverse queues of the plurality of queues; and
quantities of other mobile devices of people that were in queues of the plurality of queues when each mobile device entered the queues of the plurality of q

Assignees

• Apple Inc

Inventors

• Marti Lukas M

Classifications

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G08G1/012 Primary

  from other sources than vehicle or roadside beacons, e.g. mobile networks · CPC title

• G06F17/5009 Primary

  Physics · mapped topic