Abstract: Aspects of the disclosure relate to a method of estimating a length of a queue associated with a resource within a venue. Images of the queue may be received, each image corresponding to a segment of the queue. A server may estimate a number of venue guests in the queue by: for each of a first subset of the images, estimating a number of pixels of the image that correspond to venue guests in the queue; and for each of a second subset of the images, detecting and counting individual venue guests visible in the image. A notification including the estimated number of venue guests in the queue, an estimated current wait time, and/or a time at which a venue guest joining the queue is permitted entry to the resource, may be sent to devices corresponding to venue guests.

Abstract: An estimate is provided for how long a guest will have to wait to enter a ride, restaurant, restroom, or other point of interest at a venue. Wait times are computed using one or more algorithms (e.g., Bayesian) that can combine direct measurements around the point of interest, historical measurements around the point of interest, and measurements taken from other systems not directly connected to the point of interest. Supporting systems will vary by venue and may include entry ticketing systems, network traffic, point of sale registers, event schedules, and proximity detectors in other parts of the venue. By comparing probabilities generated from this disparate data, current and future wait times can be estimated and provided to front-end devices corresponding to guests or staff.

Abstract: Journey information is mapped by first identifying an acceptable trigger condition type, such as a photo trigger condition type, of a plurality of trigger condition types. Once a journey has begun, a trigger condition of the acceptable trigger condition type may be detected at a mobile computing device, for example when the mobile computing device captures a photo. A location of the mobile computing device is then identified in response to the detection of the trigger condition, and a marker is placed on a map identifying the location. The marker is also associated with data associated with the trigger condition, for example by allowing the photo captured by the mobile computing device to be viewable at the marker.

Abstract: Journey information is mapped by first identifying an acceptable trigger condition type, such as a photo trigger condition type, of a plurality of trigger condition types. Once a journey has begun, a trigger condition of the acceptable trigger condition type may be detected at a mobile computing device, for example when the mobile computing device captures a photo. A location of the mobile computing device is then identified in response to the detection of the trigger condition, and a marker is placed on a map identifying the location. The marker is also associated with data associated with the trigger condition, for example by allowing the photo captured by the mobile computing device to be viewable at the marker.

Abstract: Once a proximity beacon is installed within a venue, mobile devices receive signals transmitted by the proximity beacon. These mobile devices then report their own locations to an application server. These mobile devices also report a signal strength to the application server, the signal strength associated with the signal from the proximity beacon. The application server determines the location of the proximity beacon by triangulating based on the locations of the mobile devices and on the signal strengths from the mobile devices. The location of the proximity beacon is then transmitted to a front-end device that then displays the location of the proximity beacon on a map along with other locations of other proximity beacons. The map may also display range areas and battery levels of the proximity beacon, and any other data that can be gathered by the application server from the mobile devices or the proximity beacon.

Abstract: Once a proximity beacon is installed within a venue, mobile devices receive signals transmitted by the proximity beacon. These mobile devices then report their own locations to an application server. These mobile devices also report a signal strength to the application server, the signal strength associated with the signal from the proximity beacon. The application server determines the location of the proximity beacon by triangulating based on the locations of the mobile devices and on the signal strengths from the mobile devices. The location of the proximity beacon is then transmitted to a front-end device that then displays the location of the proximity beacon on a map along with other locations of other proximity beacons. The map may also display range areas and battery levels of the proximity beacon, and any other data that can be gathered by the application server from the mobile devices or the proximity beacon.

Abstract: Once a proximity beacon is installed within a venue, mobile devices receive signals transmitted by the proximity beacon. These mobile devices then report their own locations to an application server. These mobile devices also report a signal strength to the application server, the signal strength associated with the signal from the proximity beacon. The application server determines the location of the proximity beacon by triangulating based on the locations of the mobile devices and on the signal strengths from the mobile devices. The location of the proximity beacon is then transmitted to a front-end device that then displays the location of the proximity beacon on a map along with other locations of other proximity beacons. The map may also display range areas and battery levels of the proximity beacon, and any other data that can be gathered by the application server from the mobile devices or the proximity beacon.

Abstract: An estimate is provided for how long a guest will have to wait to enter a ride, restaurant, restroom, or other point of interest at a venue. Wait times are computed using one or more algorithms (e.g., Bayesian) that can combine direct measurements around the point of interest, historical measurements around the point of interest, and measurements taken from other systems not directly connected to the point of interest. Supporting systems will vary by venue and may include entry ticketing systems, network traffic, point of sale registers, event schedules, and proximity detectors in other parts of the venue. By comparing probabilities generated from this disparate data, current and future wait times can be estimated and provided to front-end devices corresponding to guests or staff.

Abstract: Once a proximity beacon is installed within a venue, mobile devices receive signals transmitted by the proximity beacon. These mobile devices then report their own locations to an application server. These mobile devices also report a signal strength to the application server, the signal strength associated with the signal from the proximity beacon. The application server determines the location of the proximity beacon by triangulating based on the locations of the mobile devices and on the signal strengths from the mobile devices. The location of the proximity beacon is then transmitted to a front-end device that then displays the location of the proximity beacon on a map along with other locations of other proximity beacons. The map may also display range areas and battery levels of the proximity beacon, and any other data that can be gathered by the application server from the mobile devices or the proximity beacon.