System and method for monitoring waiting times

Abstract

The amusement park attraction system includes a controller configured to receive a first sensor data indicating one or more first prominent attributes associated with a waiting area and a first group of guests in the waiting area, receive a second sensor data indicating one or more second prominent attributes associated with a second group of guests in the waiting area, compare the first sensor data and the second sensor data with each other, determine a confidence score associated with the degree of agreement between the first and second guest groups based on the comparison between the first and second sensor data, and output a control signal in response to the determination that the confidence score exceeds a threshold.

Description

Background Art

[0001] This section is for introducing readers to various aspects of technologies that may be related to various aspects of the present disclosure described and / or claimed below. This discussion is considered useful in showing readers the background circumstances and facilitating a better understanding of the various aspects of the present disclosure. Therefore, it should be understood that these descriptions are not to be regarded as an admission of prior art and should be read from the above perspective.

[0002] Theme parks and other entertainment facilities have various functions for entertaining guests. For example, a theme park may include attraction systems such as rides (e.g., roller coasters), theatrical shows, and augmented reality systems. As the popularity of theme parks is increasing and a large number of guests often visit at any given time, a particular attraction in a theme park may often be at full capacity. For this reason, guests may sometimes have to wait in a queue area (e.g., a line) before experiencing an attraction. The time guests have to wait may depend on various factors such as the number of guests currently waiting in the queue area, the time required for the operation cycle of the attraction system, and / or the number of guests that can be accommodated in the operation cycle of the attraction system.

[0003] The following summarizes specific embodiments corresponding to the scope of the originally claimed subject matter. These embodiments are presented only to provide readers with a brief overview of possible embodiments of these subjects, and it should be understood that these aspects are not intended to limit the scope of the claimed subject matter. In fact, the present disclosure can encompass various forms similar to or different from the embodiments shown below.

Summary of the Invention

Means for Solving the Problems

[0004] In one embodiment, the amusement park attraction system includes a waiting area and a controller configured to receive first sensor data indicating one or more first prominent attributes related to a first guest group in the waiting area, receive second sensor data indicating one or more second prominent attributes related to a second guest group in the waiting area, compare the first and second sensor data with each other, determine a confidence score associated with the match between the first and second guest groups based on the comparison of the first and second sensor data, and output a control signal in response to determining that the confidence score exceeds a threshold.

[0005] In one embodiment, a non-temporary computer-readable medium includes a command configured to perform a procedure, when executed by a processor, that includes: receiving a plurality of sensor data representing one or more prominent attributes related to each group of guests in the waiting area of ​​an amusement park attraction system; comparing a first sensor data from the plurality of sensor data with a second sensor data from the plurality of sensor data; determining whether the confidence score associated with the comparison between the first sensor data from the plurality of sensor data and the second sensor data from the plurality of sensor data exceeds one or more thresholds; determining the time interval between the acquisition of the first sensor data from the plurality of sensor data and the second sensor data from the plurality of sensor data; and outputting a control signal in response to and / or based on the time interval.

[0006] In one embodiment, an amusement park attraction system includes a waiting area and a controller configured to receive first sensor data indicating one or more first prominent attributes associated with a first group of guests at a first position in the waiting area, and second sensor data indicating one or more second prominent attributes associated with a second group of guests at a second position in the waiting area, and to determine a confidence score based on a comparison of the first and second sensor data, the confidence score being associated with the matching of the first and second group of guests, and to compare the confidence score to a threshold, and in response to determining that the confidence score exceeds the threshold, to determine a waiting time based on the time interval between the acquisition of the first sensor data and the acquisition of the second sensor data, the length of the time indicating the time interval elapsed in moving from the first position to the second position in the waiting area, and to adjust the operation of the attraction in response to and / or based on the determined waiting time.

[0007] A better understanding of these and other features, aspects and advantages of this disclosure will be gained by reading the following detailed description while referring to the attached drawings, which indicate the same parts throughout with the same reference numerals. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 is a perspective view of one embodiment of an amusement park attraction system according to an aspect of the present disclosure, which includes a controller configured to determine waiting times.

[0009] [Figure 2] Figure 2 is a schematic diagram showing the operation of a controller configured to determine the waiting time for an amusement park attraction system according to an aspect of this disclosure.

[0010] [Figure 3] Figure 3 is a flowchart of one embodiment of a method for determining the waiting time for an amusement park attraction system according to the present disclosure.

[0011] [Figure 4] Figure 4 is a flowchart of one embodiment of a method for determining the waiting time for an amusement park attraction system according to the present disclosure.

[0012] [Figure 5] Figure 5 is a flowchart of one embodiment of a method for updating sensor data used to determine the waiting time for an amusement park attraction system, according to an aspect of this disclosure.

[0013] [Figure 6] Figure 6 is a flowchart of one embodiment of a method for determining the waiting time for an amusement park attraction system according to the present disclosure.

[0014] [Figure 7] Figure 7 is a flowchart of one embodiment of a method for determining the waiting time of a single passenger according to the present disclosure. [Modes for carrying out the invention]

[0015] One or more specific embodiments are described below. For the sake of brevity, this specification does not describe all features of these embodiments. It should be understood that the development of any such implementation, as seen in any engineering or design project, will require numerous implementation-specific decisions to achieve the developer's specific objectives, such as compliance with system-related and business-related constraints, which may vary by implementation. Furthermore, while such development efforts can be complex and time-consuming, they should be understood by those skilled in the art who benefit from this disclosure as routine design, fabrication, and manufacturing endeavors.

[0016] When describing elements of the various embodiments of this disclosure, the articles “a,” “an,” and “the” mean that there are one or more of these elements. The terms “comprising,” “including,” and “having” are intended to be comprehensive and mean that there may be further elements other than those listed. Furthermore, any reference to “one embodiment” or “one embodiment” in this disclosure should not be interpreted as excluding the existence of further embodiments, including the features described.

[0017] The terms "approximately," "usually," and "substantially" used here are intended to convey that the described characteristic value falls within a relatively small range of that characteristic value, as understood by an average engineer. For example, when a characteristic value is described as being "approximately" equal to (or, for example, "substantially similar to") a particular value, this is intended to convey that the characteristic value is within + / -5%, + / -4%, + / -3%, + / -2%, + / -1%, or closer to that value. Similarly, when a particular feature is described as being "substantially parallel" to another feature, or "usually perpendicular" to another feature, this is intended to convey that the feature has the described property of being parallel to or perpendicular to another feature within + / -5%, + / -4%, + / -3%, + / -2%, + / -1%, or closer to that range. Mathematical terms such as "parallel" and "perpendicular" should not be interpreted rigidly in their strict mathematical sense, but rather in the way that an average engineer would interpret such terms. For example, an average engineer would understand that two lines that are substantially parallel to each other are parallel to a substantial degree, but there may be some deviation from perfect parallelism.

[0018] Amusement parks and theme parks may have attraction systems designed to entertain a variety of guests. For example, an attraction system may include roller coasters, dark rides, log rooms, performance shows, and character meet-and-greets. An attraction system may operate multiple cycles throughout the day. For example, an attraction system may include ride vehicles that travel along a ride track to complete the cycle. Another example is an attraction system that includes actors and / or show effects that provide performances during each cycle. Different guests may experience the attraction system in each cycle.

[0019] However, attraction systems may have capacity limits. As a result, each operating cycle of an attraction system that has reached its capacity limit must accommodate a certain number of guests, up to the park's threshold. Therefore, guests exceeding this threshold may have to wait during the operating cycle, such as until the attraction system's cycle is completed. For example, on a roller coaster attraction, guests may wait until the ride vehicle completes a circuit of the track. Once the vehicle completes the circuit, passengers disembark, and the next guests can board. Guests may also have to physically wait in a waiting area (e.g., a queue) before experiencing an attraction system. However, waiting areas may not provide guests with sufficient entertainment. Furthermore, guests may be unable to experience other attractions while in the waiting area. For this reason, guests may want to know how long the wait time is for an attraction system in order to decide whether to choose a different attraction system with a shorter wait time, or to join the waiting area and experience that attraction system. Therefore, guests can choose a more suitable attraction system based on the wait time. In this way, providing guests with accurate wait times can improve their overall experience.

[0020] Therefore, accurately determining wait times for attraction systems is recognized as beneficial to the operation of amusement parks in order to entertain guests. In other words, this disclosure relates to a system and method for monitoring guest flow in a waiting area and determining wait times based on guest flow. Guest flow is monitored based on one or more prominent attributes of one or more guests. For example, first sensor data indicating a first prominent attribute of a first group of guests at a first location in the waiting area, and second sensor data indicating a second prominent attribute of a second group of guests at a second location in the waiting area may be received.

[0021] As described herein, distinctive attributes may include physical characteristics that differentiate certain guests from one another, such as body dimensions (e.g., height), type of clothing worn, tattoos, jewelry, patterns on clothing, hair color, type of clothing (short-sleeved or long-sleeved, hooded, buttoned, zippered, etc.), color of clothing, symbols, and types of bags (handbags, backpacks, sunglasses, glasses, fanny packs, etc.), which help identify specific guests from one another. In some cases, distinctive attributes may also be semi-unique attributes shared with other guests (e.g., wearing a white hat). Furthermore, distinctive attributes may include dynamic attributes that are likely to change in a relatively short period of time, such as choices of clothing and accessories like hats, jackets, and bags, which are likely to differ on subsequent visits.

[0022] Thus, other guests may share some prominent attributes. Therefore, each individual guest can maintain sufficient anonymity while their prominent attributes are monitored, and these prominent attributes can be used to identify guest groups or combinations. For example, the controller can detect a group with specific prominent attributes including five adults and one child, six different heights, and six different shirt colors. Although other guest groups may share some of these prominent attributes (e.g., one height, one shirt color), the possibility of another group sharing all the prominent attributes is sufficiently low, so the detected group with all these attributes is likely to be the group of interest. In fact, any of the prominent attributes, such as the pattern of height differences among some guests within the group, can be unique enough to identify this group within the waiting area while maintaining the anonymity of each individual. Therefore, even if the prominent attributes of each individual do not uniquely identify a specific individual, the combination of the prominent attributes of the individuals within the group is sufficient to identify that group within the waiting area without using personally identifiable information and / or more unique guest attributes. Anonymity is further protected when at least some of the prominences, such as clothing attributes, are not permanent.

[0023] In one embodiment, the prominent attributes can include uniquely identifiable attributes such as facial placement attributes used for face recognition, biometric features, permanent identification characteristics, the presence of items associated with the guest (e.g., machine-readable cards), or other techniques for monitoring the flow of other guests. Thus, in certain cases, guests can be monitored over a period of time using one or more prominent attributes that may not be uniquely identifiable in one context but may be uniquely identifiable in another context.

[0024] For example, different guest groups can have distinct attributes of various sets or collections (e.g., permutations). Thus, the respective distinct attributes of different guest groups can be unique enough to distinguish a particular guest group from other groups. For this reason, by comparing the distinct attributes of sensor data at each location, the progress of guest groups moving in the waiting area can be identified (e.g., guests can be entertained sequentially based on the operation of the attraction system). As an example, the first distinct attribute indicated by the first sensor data can be compared with the second distinct attribute indicated by the second sensor data. A confidence score associated with the match between distinct attributes is determined based on the comparison, such as the similarity of the respective distinct attributes of the first and second sensor data, and can indicate the likelihood of the first guest group with the first distinct characteristic and the second guest group with the second distinct characteristic matching each other. For example, a relatively high confidence score can indicate a high likelihood that the first guest group and the second guest group contain the same guests. Thus, the second sensor data indicates the progress from the first location to the second location of the guests. Accordingly, in response to determining that the confidence score exceeds a threshold, the time span between the acquisition of the first sensor data and the acquisition of the second sensor data can be determined. The time span can indicate the elapsed time required for the guests to move from the first location to the second location. Thereafter, the waiting time associated with the waiting area (e.g., total waiting time) can be determined based on the time span. For example, the waiting time can be determined based on the length of time and other information such as the operation of the attraction system, the flow of guests in the amusement park, and / or past information, and can provide a more accurate estimated waiting time. Thereafter, a control signal can be output based on the waiting time to more appropriately operate the attraction system (e.g., present the determined waiting time to the guests, adjust the operation cycle).

[0025] Based on the above, Figure 1 is a perspective view of one embodiment of an amusement park attraction system 50. The attraction system 50 may include a ride 52 having a ride vehicle 54 configured to travel along a route or track 56 during the operation cycle of the attraction system 50. The disclosed monitoring technology can be used to track the flow of guests 58 at various points in time within the attraction system. For example, a guest 58a may board a ride vehicle 54 at a boarding / alighting station 60, after which the ride vehicle 54 may begin its operation cycle and travel along the route 56. After the completion of the operation cycle, the ride vehicle 54 may enter the boarding / alighting station 60, and the guest 58a may disembark from the ride vehicle 54. In further or alternative embodiments, the guest 58a may board and disembark at separate boarding and alighting stations during the operation cycle of the attraction system 50, respectively.

[0026] The ride vehicle 54 has a limited capacity and can carry a threshold number of guests 58a during each operating cycle. Therefore, if there are guests 58b in the attraction system 50 that exceed the threshold number of guests that can be accommodated in an operating cycle, guests 58b must wait for the next operating cycle to experience the ride 52. Thus, guests 58b must wait until the ride vehicle 54 has completed its journey along route 56 before boarding the ride vehicle 54. In this way, each operating cycle of the attraction system 50 can entertain different guests.

[0027] Therefore, the attraction system 50 may include a waiting area or waiting system 62 for guests 58 to wait before boarding the ride vehicle 54. The waiting area 62 can guide guests 58 to the ride 52, organize guests 58 to board the ride vehicle 54, and / or provide entertainment (e.g., visual effects, sound effects, fluid effects, tactile effects) to guests 58 within the waiting area 62. For example, the waiting area 62 can allow individual guests 58 and / or groups of guests 58 (e.g., family, friends) to experience the ride 52 in turn, based on the order in which guests 58 entered the waiting area 62.

[0028] In the illustrated embodiment, the waiting area 62 includes a first waiting queue 64, a second waiting queue 66, and a third waiting queue 67. However, it should be understood that more or fewer waiting queues may be considered. The first waiting queue 64 guides guests 58c from the first entrance 68 to the boarding / alighting station 60, the second waiting queue 66 guides guests 58d from the second entrance 70 to the boarding / alighting station 60, and the third waiting queue 67 guides guests 58e from the third entrance 71 to the boarding / alighting station 60. Thus, guests 58 can board the ride 52 using the first waiting queue 64, the second waiting queue 66, or the third waiting queue 67. For example, the first waiting queue 64 may be the primary or regular waiting queue used by a larger number of guests 58c to wait for the ride 52. The second waiting queue 66 may be a secondary or dedicated waiting queue used by a relatively small number of guests 58d. For example, a limited number of guests 58d, such as guests 58d who have paid an additional fee, guests 58d who have won a prize, and / or guests 58d who have received a special invitation, may use the second queue 66 instead of the first queue 64. The third queue 67 can accommodate guests 58e who do not belong to a group (e.g., single riders). The first queue 64 and the second queue 66 merge at a junction 72 (e.g., a connection point), allowing guests 58d in the second queue 66 to merge with guests 58c in the first queue 64. For example, a ride operator or other staff of the attraction system 50 can guide guests 58c and 58d toward the ride 52. The third queue 67 is directly connected to the boarding / alighting station 60, allowing the ride operator to identify one passenger 58e that can be accommodated during the ride 52's operating cycle.

[0029] In one embodiment, a guest 58c in the second queue 66 may experience a shorter wait time compared to a guest 58d in the first queue 64. For example, with a relatively small number of guests 58d in the second queue 66 and / or from the second entrance 70 which is relatively close to the merging point 72, guests 58d can proceed to the merging point 72 more quickly through the second queue 66. Thus, guests 58d can proceed to the boarding / alighting station 60 more quickly through the second queue 66. For this reason, the respective wait times associated with queues 64 and 66 may differ. With respect to the third queue 67, the wait time may depend on the vehicle operator's capacity to accommodate a single-passenger guest 58e. Therefore, depending on the group composition of guests in the first queue 64 and / or the second queue 66, the third queue 67 may have a shorter or longer wait time than either or both of the first queue 64 or the second queue 66. For example, a particular group of guests 58c, 58d may create more single-passenger seats, while other group configurations may completely fill the seats, resulting in fewer single-passenger seats. Specifically, if seven guests are determined to be a family or friends group, i.e., a passenger group, one seat in an eight-seater vehicle will be empty. The technology of this disclosure can enable a more accurate estimation of the likelihood that guests 58 in a queue will sit together as a passenger group. Based on these group configurations, the system 50 can generate an estimated wait time for a third queue 67, as disclosed herein.

[0030] It may be desirable to determine the respective wait times associated with queues 64, 66, and 67. For example, the determined wait times may be presented to guests 58 who have not yet entered the waiting area 62, allowing them to decide whether to enter the waiting area 62 (e.g., or enter the waiting area of ​​another attraction system). Alternatively, the determined wait times may be used to adjust the operation of the attraction system 50, such as by changing the number of ride vehicles 54 in operation to allow guests 58 to enjoy themselves more efficiently (e.g., by reducing the wait time for guests 58 in the waiting area 62) and / or by adjusting how guests 58d in the second waiting queue 66 merge with guests 58c in the first waiting queue 64 at the merging point 72 (e.g., by changing the wait time for guests 58c in the first waiting queue 64), or by adjusting how single-passenger guests 58e are accommodated. As another example, if an individual guest is eligible to enter multiple queues, they may choose the queue with the shortest wait time.

[0031] Therefore, the attraction system 50 may include a controller 74 (e.g., an automated controller, a programmable controller, an electronic controller, a control circuit, a cloud computing system, a control system) configured to determine the respective wait times associated with the waiting lines 64, 66. The controller 74 may include memory 76 and a processor 78 (e.g., a processing circuit). The memory 76 may include volatile memory such as random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM), optical drives, hard disk drives, solid-state drives, and other non-temporary computer-readable media that store instructions for operating the attraction system 50. The processor 78 may be configured to execute these instructions. For example, the processor 78 may include one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), one or more general-purpose processors, or a combination thereof. The controller 74 may determine the respective wait times based on various parameters. For example, the controller 74 may be communicatively coupled to one or more sensors 80 configured to monitor specific parameters and transmit sensor data indicating the monitored parameters. The controller 74 receives sensor data from the sensor 80 and can determine the respective waiting times based on the parameters indicated in the sensor data.

[0032] In one embodiment, the sensor 80 may include optical sensors (e.g., cameras, visible light sensors, invisible light sensors, color sensors, thermal sensors) and / or position sensors (e.g., laser sensors, Lidar sensors), and the parameters may include various attributes or characteristics related to the guest 58. These characteristics may include distinctive attributes that may differ among specific guests 58. For example, distinctive attributes may include body dimensions (e.g., height, arm length), weight, type of clothing (e.g., shirts, jackets, hats, trousers, shorts), and / or color of clothing. Furthermore, characteristics may include group configurations (e.g., relative positional relationships) determined by a set of individuals moving together within the waiting area 62. However, distinctive attributes may also be similar or identical among other guests 58.

[0033] For example, prominent attributes may or may not include more specific information, such as facial features or designs related to clothing (e.g., printed designs). Thus, guest 58 can maintain sufficient anonymity without being distinguished from other guests 58. In this way, the sensor 80 can avoid having to obtain confidential, personal, or sensitive information related to guest 58, depending on the desired monitoring context.

[0034] The controller 74 can monitor the progress of various groups of guests 58 as they pass through the waiting area 62 and determine the waiting time. For example, the controller 74 can determine an approximate waiting time associated with a first waiting lane 64 by monitoring the time required for a particular group of guests 58c to pass through the first waiting lane 64. The controller 74 can further or alternatively determine an approximate waiting time associated with a second waiting lane 66 by monitoring the time required for a particular group of guests 58d to pass through the second waiting lane 66. For this purpose, the controller 74 can receive sensor data indicating prominent attributes associated with groups of guests 58 at different locations in the waiting area 62. The controller 74 compares each prominent attribute indicated by the sensor data with each other and determines a confidence score (e.g., confidence level, confidence value) associated with the match between guest groups 58 to indicate the progress of guests 58 through the waiting area 62. The controller 74 can activate a timer and determine the time elapsed between the acquisition of different sensor data to indicate the time associated with the progress of guests 58 through the waiting area 62.

[0035] For example, the controller 74 can receive first sensor data containing a first prominent attribute of a guest 58 detected at a first position in the waiting area 62, and second sensor data containing a second prominent attribute of a guest 58 detected at a second position in the waiting area 62. The controller 74 can compare the first prominent attribute and the second prominent attribute with each other to determine whether the first and second sensor data indicate that the same guest 58 moved from the first position in the waiting area 62 to the second position in the waiting area 62. In fact, different guest groups 58 may have common prominent attributes and / or the prominent attributes of a particular guest group 58 may change while progressing through the waiting area 62, so the controller 74 can verify whether the respective prominent attributes shown by the different sensor data are related to the same guest group 58.

[0036] In one embodiment, the controller 74 can determine a confidence score and use the confidence score to determine the progress of one or more guests 58 or guest group 58. For example, if the confidence score exceeds a first threshold (e.g., a high confidence threshold), the confidence score may be designated as "high confidence." If the confidence score is below the first threshold but exceeds a second threshold (e.g., a low confidence threshold), the confidence score may be designated as "intermediate." Scores in the intermediate group between the first and second thresholds (e.g., between the high confidence threshold and the low confidence threshold) may be flagged as described herein. If the confidence score is below the second threshold, the confidence score may be designated as "low confidence." This is just an example, but thresholds may be set such that scores of 80-100 are high confidence, scores of 60-79 are intermediate, scores of 0-59 are low confidence, etc. However, it should be understood that other thresholds and ranges are also possible.

[0037] For example, a confidence score below a low confidence threshold may indicate that the second prominent attribute is unlikely to be identical to the guest 58 associated with the first prominent attribute. A high confidence score may indicate that the first and second prominent attributes are associated with the same guest 58. A high confidence score can trigger the controller 74 to automatically consider guest 58 or guest group 58 and its associated tracking data when calculating latency. A low confidence score can trigger the exclusion of associated tracking data. The score for intermediate groups can trigger a flag for user input, which can be used to prompt the user to reclassify guest 58 into a high-confidence or low-confidence group based on user input. User input can also be used for machine learning to improve future classification accuracy.

[0038] Subsequently, the controller 74 can use a timer to determine the time elapsed between the acquisition of the first sensor data and the acquisition of the second sensor data. This time elapsed can indicate the time required for the guest 58 to move from the first position to the second position in the waiting area 62. Based on this time elapsed, the controller 74 can determine the waiting time associated with the waiting area 62.

[0039] In one embodiment, the controller 74 can receive sensor data from a first sensor 80A configured to provide sensor data indicating prominent attributes related to a guest 58 entering the first waiting lane 64 from the first entrance 68. The controller 74 can also receive sensor data from a second sensor 80B configured to provide sensor data indicating prominent attributes related to a guest 58 at the merging point 72. The controller 74 can further receive sensor data from a third sensor 80C configured to provide sensor data indicating prominent attributes related to a guest 58 entering the second waiting lane 66 from the second entrance 70. Thus, the controller 74 can determine the length of time related to the journey from the first entrance 68 to the merging point 72 for the first waiting lane 64, and / or the width of time related to the journey from the second entrance 70 to the merging point 72 for the second waiting lane 66. Furthermore or alternatively, the controller 74 can receive sensor data from a fourth sensor 80D configured to provide sensor data indicating prominent attributes related to a guest 58 entering the boarding / alighting station 60. In this way, the controller 74 can determine the time intervals associated with travel from the merging point 72 to the boarding / alighting station 60, from the first entrance 68 to the boarding / alighting station 60, and / or from the second entrance 70 to the boarding / alighting station 60. Furthermore or alternatively, if the system includes a third waiting lane 67, the controller 74 can receive sensor data from a fifth sensor 80E configured to provide sensor data indicating prominent attributes associated with a guest 58 entering the third waiting lane 67. The controller 74 can use any of these time intervals to determine the waiting times associated with the first waiting lane 64, the second waiting lane 66, and / or the third waiting lane 67.

[0040] The controller 74 can also use other information to determine accurate wait times. In fact, the determined time range may directly represent past wait times for a particular group of guests 58 in a specific part of the waiting area 62, but may not represent current or future wait times for other groups of guests 58. For example, the controller 74 can determine parameters related to the operation of the ride 52, such as the speed at which the ride vehicle 54 circles the route 56, the driving mode of the ride 52, the time required for boarding and alighting at the boarding / alighting stations 60, and / or the number of guests 58 in a particular operating cycle. As another example, the controller 74 can determine parameters related to the flow of guests in the amusement park, such as the total number of guests 58 in the park, the number of guests 58 in other attraction systems (e.g., an attraction system adjacent to attraction system 50), weather parameters (e.g., a forecast of rain may reduce the number of guests 58 who want to ride outdoor rides), and / or time of day (e.g., the number of guests 58 participating in rides decreases during meal times). As a further example, the controller 74 can determine the wait time using historical information such as previously determined wait times (e.g., those related to similar time periods, operating modes, and the number of guests 58). Therefore, the controller 74 can use multiple different pieces of information to determine the wait time more accurately.

[0041] The controller 74 can output a control signal based on the determined wait time. For example, the attraction system 50 may include a first display device 82 that shows the wait time associated with a first queue 64. The first display device 82 is located near a first entrance 68, so that a guest 58 at the first entrance 68 can decide whether to proceed to the first queue 64 to wait for the ride 52 based on the wait time provided by the first display device 82. The attraction system 50 may also include a second display device 84 that shows the wait time associated with a second queue 66. The second display device 84 is located near a second entrance 70, so that a guest 58 at the second entrance 70 can decide whether to proceed to the second queue 66 to wait for the ride 52 based on the wait time provided by the second display device 84. Furthermore, the attraction system 50 may include a third display device 85 that shows the wait time associated with a third queue 67. A third display device 85 is located near the third entrance 71, allowing a guest 58 at the third entrance 71 to determine whether to proceed to the third queue 67 to wait for a ride 52 based on the wait time provided by the third display device 85. The controller 74 can output respective control signals to the display devices 82, 84, and 85 to update the provided wait times. For example, the controller 74 can output a control signal to instruct the first display device 82 to update the wait time based on at least the time associated with travel from the first entrance 68 to the junction 72, the time associated with travel from the first entrance 68 to the boarding / alighting station 60, and / or the time associated with travel from the junction 72 to the boarding / alighting station 60. As another example, the controller 74 can output a control signal to instruct the second display device 84 to update the wait time based on at least the time associated with travel from the second entrance 70 to the junction 72, the time associated with travel from the second entrance 70 to the boarding / alighting station 60, and / or the time associated with travel from the junction 72 to the boarding / alighting station 60.As yet another example, the controller 74 can output a control signal to instruct the third display device 85 to update the wait time based on the time it takes to travel from the third entrance 71 to the boarding / alighting station 60. Thus, the wait times provided by the display devices 82, 84, and 85 can accurately represent the wait time for each guest 58 entering the waiting lines 64, 66, and 67 from their respective entrances 68, 70, and 71.

[0042] Alternatively, the controller 74 may output a control signal to instruct the operation of the ride 52 to be adjusted. For example, in response to determining that the wait time exceeds a threshold time, the controller 74 may instruct the operation of an additional ride vehicle 54, thereby allowing the ride 52 to accommodate more guests 58 (e.g., by running the operating cycle more frequently). As another example, the controller 74 may output a control signal to instruct the operation mode of the attraction system 50 to be adjusted, such as operating the attraction system 50 in an operating mode with an operating time more suitable for the wait time (e.g., operating in a mode with a relatively short operating time in response to determining that the wait time exceeds a threshold time). As yet another example, the controller 74 may output a control signal to instruct the attraction system 50 to temporarily disable the ride 52's online reservation system while the wait time exceeds a threshold time.

[0043] The attraction system 50 may further include an interactive device 86 positioned at an intermediate point in the waiting area 62 (e.g., between entrances 68, 70, 71 and boarding / alighting stations 60). The interactive device 86 can provide entertainment and / or information to guests 58 in the waiting area 62. For example, the interactive device 86 can entertain guests 58 in the waiting area 62 by providing show effects (e.g., visual effects such as lights, sound effects such as noise). The controller 74 can operate the interactive device 86 based on the determined wait time. For example, the controller 74 can output a control signal to instruct the interactive device 86 to provide a specific show effect and / or adjust the frequency of providing the show effect based on the determined wait time. For example, if the interactive device 86 includes an interactive game, the controller 74 can instruct the interactive device 86 to update the game more frequently or add features to allow more players to participate. Alternatively, the controller 74 can output a control signal to activate an interactive device 86 that was previously inactive in response to determining that the wait time exceeds a threshold wait time. Furthermore, in response to determining that the wait time exceeds a threshold wait time, the controller 74 can output control signals to adjust the accessibility of different parts of the attraction system 50, such as opening mechanical gates within the waiting area 62 to allow guests 58 to enter previously inaccessible parts of the waiting area 62 (e.g., to increase the capacity of the waiting area 62). If the controller 74 determines that the wait time has fallen below an additional threshold time, it can output control signals to return to the state before the wait time was determined to have exceeded the threshold time. Thus, the controller 74 can operate the interactive device 86 more appropriately to entertain guests 58 in the waiting area 62 and provide them with a better experience.

[0044] Alternatively, the interactive device 86 can provide a waiting time required for travel from an intermediate position within the interactive device 86 to the boarding / alighting station 60. In other words, the interactive device 86 can provide a waiting time to guests 58 currently waiting in the waiting area 62. Such guests 58 can use the waiting time provided by the interactive device 86 to decide whether to stay in the waiting area 62 or leave the waiting area 62 (e.g., choose to go to another attraction system). The controller 74 can output a control signal to instruct the interactive device 86 to update the provided waiting time based on various time spans related to travel between different positions in the waiting area 62, thereby enabling the interactive device 86 to provide accurate waiting times to waiting guests 58.

[0045] Figure 2 is a schematic diagram showing the operation of a controller 74 for determining the wait time of an attraction system (e.g., attraction system 50 in Figure 1). The controller 74 can receive and store first sensor data 110 (e.g., transmitted by the first sensor) indicating prominent attributes of a first guest group 111 of guests 58 at a first location (e.g., entrance) in a waiting area (e.g., waiting area 62 in Figure 1). The controller 74 can also receive and store second sensor data 114 (e.g., transmitted by the second sensor) indicating prominent attributes of a second guest group 115 of guests 58 at a second location (e.g., junction) in the waiting area. For example, the first sensor data 110 and the second sensor data 114 are acquired as image data, and the controller 74 can determine the prominent attributes indicated by the respective sensor data 110 and 114 based on the acquired image data.

[0046] It should be understood that guest groups 111 and 115 of guest 58 can represent any set of guests waiting in the waiting area. In fact, the guests in each guest group 111 and 115 of guest 58 may belong to the same party in one embodiment. However, in further or alternative embodiments, the guests in each guest group of guest 58 may belong to different parties. In fact, the guests 58 may be positioned in any appropriate manner (e.g., spaced apart), may have any attributes, appearance, and / or features, and / or guests 58 may be in the waiting area at any time during the operation of the controller 74. Therefore, the sensor data 110 and 114 are of any guest in the waiting area, allowing the controller 74 to determine the wait time at any point in the operating time. For this reason, the controller 74 does not need to receive sensor data that includes, for example, specific guest attributes (e.g., specific facial features) or that meets certain conditions before it can compare the sensor data with each other, and can operate more flexibly to determine the wait time by comparing prominent attributes.

[0047] The controller 74 can compare the respective prominent attributes shown by the sensor data 110 and 114 to determine whether the prominent attributes are associated with the same guest group 58. In other words, the controller 74 can determine whether the first guest group 111 of the guest 58 acquired in the first sensor data 110 is identical to the second guest group 115 of the guest 58 acquired in the second sensor data 114. For example, the controller 74 can determine that the first sensor data 110 shows or identifies a first prominent attribute that includes a first guest 58f having a first height (e.g., relatively tall), a second guest 58g having a second height (e.g., relatively short) adjacent to the first guest 58f, and a third guest 58h having a third height (e.g., intermediate height) adjacent to the second guest 58g. The controller 74 can also determine that the first sensor data 110 indicates that the third guest 58h is wearing the first headwear 124 (e.g., a cap). The controller 74 can also determine that the second sensor data 114 indicates that the fourth guest 58i has a fourth height (e.g., a relatively tall height), the fifth guest 58j has a fifth height (e.g., an intermediate height) adjacent to the fourth guest 58i, and the sixth guest 58k has a sixth height (e.g., a relatively short height) adjacent to the fifth guest 58j. The controller 74 can also determine that the second sensor data 114 indicates that the sixth guest 58k is wearing the second headwear 132 (e.g., a cap).

[0048] The controller 74 can compare a first prominent attribute indicated by the first sensor data 110 with a second prominent attribute indicated by the second sensor data 114 in order to determine whether the first guest group 111 of guest 58 and the second guest group 115 of guest 58 belong to the same guest group. For example, the controller 74 can determine that the first height of the first guest 58f is approximately the same as the fourth height of the fourth guest 58i, the second height of the second guest 58g is approximately the same as the sixth height of the sixth guest 58k, and the third height of the third guest 58h is approximately the same as the fifth height of the fifth guest 58j. In this way, the controller 74 can determine that the height of the first guest group 111 of guest 58 is approximately the same as the height of the second guest group 115 of guest 58. The controller 74 can also determine that the first headwear 124 is approximately the same as the second headwear 132. Subsequently, the controller 74 can determine a confidence score indicating the degree to which the first guest group 111 of guest 58 and the second guest group 115 of guest 58 match.

[0049] In one embodiment, the controller 74 can determine that the confidence score exceeds a threshold. For example, the controller 74 can determine that the first sensor data 110 and the second sensor data 114 each contain the height and type of clothing of a common guest. For example, it can determine that the first guest 58f and the fourth guest 58i are the same person, the second guest 58g and the sixth guest 58k are the same person, and the third guest 58h and the fifth guest 58j are the same person. The controller 74 can determine that the sensor data 110 and 114, which show that the prominent attributes of each guest are identical, suggest that the first guest group 111 of guest 58 and the second guest group 115 of guest 58 are the same, even if the relative positions of guests 58f, 58g, and 58h in the first sensor data 110 (e.g., the order of guests 58f, 58g, and 58h) are different from the relative positions of guests 58i, 58j, and 58k in the second sensor data 114 (e.g., the order of guests 58i, 58j, and 58k) and / or the headwear 124 and 132 are held in different ways by different guests 58h and 58k in the sensor data 110 and 114. Therefore, the controller 74 can determine that the first guest group 111 of guest 58 and the second guest group 115 of guest 58 are the same, based on the sensor data 110 and 114, which show that the prominent attributes of each guest are identical and in different orders.

[0050] In response to determining that the confidence score exceeds a confidence score threshold, the controller 74 can determine the length of time between the acquisition of the first sensor data 110 and the second sensor data 114. For example, this length of time represents the time elapsed for guests 58f, 58fi, 58h, 58i, 58j, and 58k to move from a first position related to the first sensor data 110 to a second position related to the second sensor data 114. In one embodiment, the controller 74 can determine a first timestamp related to the acquisition of the first sensor data 110 and a second timestamp related to the acquisition of the second sensor data 114. The controller 74 can determine the time width based on the difference between the first and second timestamps. In further or alternative embodiments, the controller 74 can start a timer when the first sensor data 110 is acquired and stop the timer when the second sensor data 114 is acquired, and the elapsed time provided by the timer can represent the time width. The length of this time can indicate the time that guests 58f, 58fi, 58h, 58i, 58j, and 58k waited in the waiting area. Therefore, the controller 74 can determine the waiting time in the waiting queue based on this time interval, in combination with other information that can be received.

[0051] The controller 74 may instead determine that the confidence score is below a threshold. For example, the different positional relationships of guests 58f, 58fi, 58h, 58i, 58j, and 58k (e.g., corresponding guests with similar heights) and / or the fact that headwear 124 and 132 are held differently by different guests 58h and 58k in sensor data 110 and 114 may indicate that the first guest group 111 of guest 58 and the second guest group 115 of guest 58 are different groups. In response to this, the controller 74 may not determine the length of time between the acquisition of the first sensor data 110 and the second sensor data 114. In other words, the controller 74 may not determine the latency based on a comparison of the first sensor data 110 and the second sensor data 114.

[0052] However, in one embodiment, the controller 74 may receive subsequent sensor data, determine the prominent attributes indicated by the subsequent sensor data, compare those prominent attributes with the prominent attributes indicated by the first sensor data 110, and determine a confidence score based on the comparison. For example, a confidence score below a threshold may indicate that the first guest group 111 of guest 58 has not yet passed the second location in the waiting area. Therefore, additional sensor data for the first guest group 111 of guest 58 at the second location may not yet have been received. Thus, the controller 74 may continue to use the first sensor data 110 to determine the potential latency. However, to avoid unnecessarily continuing to use the first sensor data 110, the controller 74 may eventually stop using the first sensor data 110 to determine the potential latency. For example, the first guest group 111 of guest 58 may have passed the second location in the waiting area, but additional data for the first group of guest 58 was not successfully acquired at the second location or was not compared with the first sensor data 110. After the first group of guests 58 has passed the second position in the waiting area, the first sensor data 110 may no longer be valid in determining the waiting time. For example, the controller 74 may delete the first sensor data 110 from memory to reduce resource consumption. In one embodiment, the controller 74 may delete the first sensor data 110 from memory after a threshold time (e.g., 6-12 hours, 12-24 hours, 1-3 days) has elapsed.

[0053] In further or alternative embodiments, the controller 74 may also delete the first sensor data 110 from storage in response to determining a match with another (e.g., subsequent) guest group via different sensor data. For example, after receiving the first sensor data 110, the controller 74 may receive first subsequent sensor data relating to a first location in the waiting queue area and indicating prominent attributes of an additional guest group. The controller 74 may also compare sensor data relating to a second location in the waiting area with the first subsequent sensor data to determine whether an additional guest group is identified at the second location. For example, the controller 74 may compare the second sensor data 114 with the first subsequent sensor data, determine a confidence score relating to the comparison, and determine that a second group 115 of guests 58 relating to the second sensor data 114 matches an additional guest group relating to the first subsequent sensor data, based on the confidence score exceeding a threshold confidence score. The match between the second group 115 of guest 58 and the additional guest group suggests that the first group 111 of guest 58 may have already passed the second position. In response, the controller 74 can delete the first sensor data 110 from storage. That is, the acquisition of the first subsequent sensor data associated with the additional guest group occurring after the acquisition of the first sensor data associated with the first group 111 of guest 58 may indicate that the additional guest group has passed the first position in the waiting queue area after the first group 111 of guest 58, and that the first group 111 of guest 58 is positioned ahead of the additional guest group throughout the waiting queue area. Thus, after the additional guest group (e.g., and possibly the first group 111 of guest 58) has passed the second position, subsequent sensor data may not indicate that the first group 111 of guest 58 has passed the second position. Therefore, the first sensor data 110 can no longer be used to monitor the progress of the first group 111 of guest 58 and can therefore be deleted from storage.Deleting the first sensor data 110 can provide the availability of resources used to receive additional sensor data and monitor the progress of additional guest groups.

[0054] In further or alternative embodiments, the second sensor data 114 may be used to update the first sensor data 110 rather than to determine the time interval associated with the progression from the first position to the second position. For example, the second sensor data 114 may be intermediate sensor data (e.g., sensor data received before subsequent sensor data used to determine the waiting time) that can be used to verify the progress of a guest group. Based on the fact that the confidence score associated with the comparison of the first sensor data 110 and the second sensor data 114 exceeds the threshold confidence score, the controller 74 may update the first sensor data 110 to indicate a prominent attribute of the second sensor data 114, since this fact indicates that the guest has progressed from the first position to the second position. In fact, a prominent attribute of the second sensor data 114 may more accurately represent the guest as they progress through the waiting area. For example, the guest's position may be adjusted, or the clothing the guest is wearing (e.g., headwear 124, 132) may be adjusted. Therefore, updating the first sensor data 110 makes it possible to use the first sensor data 110 more accurately for comparison to identify guests from additional sensor data. In other words, the controller 74 can compare subsequent prominent attributes indicated by subsequent sensor data with prominent attributes indicated by the second sensor data 114 instead of prominent attributes indicated by the first sensor data 110. This allows the controller 74 to respond to changes in prominent attributes of guests, such as changes in clothing or changes in the relative position of guests, as they progress through the waiting area, enabling more accurate identification of guests. Thus, the waiting time can be determined more accurately based on the first sensor data 110 updated to take the second sensor data 114 into account.

[0055] Figures 3 to 6 are flowcharts illustrating methods for operating an attraction system (e.g., the attraction system 50 in Figure 1), referring to the features described in Figures 1 and 2. Any suitable device (e.g., a device including the processor 78 in Figures 1 and 2) can perform each method. In one embodiment, each method can be implemented by executing instructions stored in a tangible, non-temporary computer-readable medium (e.g., the memory 76 of the controller 74 in Figures 1 and 2). For example, each method can be performed, at least in part, by one or more software components, one or more software applications, etc. Each method is described so that operations are performed in a specific order, but additional operations may be performed, the described operations may be performed in an order different from the illustrated order, and / or certain described operations may be omitted or not performed at all. Furthermore, the operations of each method can be performed relative to each other in any manner, for example, sequentially and / or simultaneously.

[0056] Figure 3 is a flowchart of method 160 for determining wait times in the waiting area of ​​an attraction system. Block 162 can receive and store initial sensor data indicating the first prominent attributes of the first guest group. The initial sensor data can be received from a first sensor and may relate to a first location in the waiting area, such as an entrance or other suitable upstream location. For example, the initial sensor data may include acquired images (e.g., still images and / or video data, real-time image data), machine vision, and / or location data related to the first guest group. The initial prominent attributes may include initial values ​​related to the body dimensions, weight, type of clothing, and / or color of clothing of the guests included in the first guest group.

[0057] In block 164, subsequent sensor data indicating subsequent prominent attributes of a subsequent guest group can be received and stored. Subsequent sensor data can be received from a second sensor and may relate to a second location in the waiting area, such as a junction (e.g., a junction of multiple waiting lines), a boarding / alighting station, or other suitable downstream location. Subsequent sensor data may include acquired images, machine vision, and / or location data related to the subsequent guest group. Subsequent prominent attributes may include subsequent values ​​related to body dimensions (e.g., height), type of clothing worn, tattoos, accessories, clothing patterns, hair color, clothing type (short-sleeved or long-sleeved, hooded, buttoned, zippered, etc.), clothing color, symbols, and accessories (bags, backpacks, sunglasses, glasses, fanny packs, etc.).

[0058] In block 166, a confidence score can be determined indicating the degree of similarity between the first guest group and the subsequent guest group. For example, a high confidence score indicates a high probability that the first and subsequent guest groups are the same guests. The first and subsequent sensor data can be compared with each other to determine the confidence score. For example, in response to the reception of subsequent sensor data, the first sensor data can be retrieved from storage for comparison. During the comparison of the first and subsequent sensor data, the first prominent attribute shown by the first sensor data and the subsequent prominent attribute shown by the subsequent sensor data can be compared with each other to determine the confidence score. As an example, the similarity between the first value of the first prominent attribute and the subsequent value of the subsequent prominent attribute can be determined. The confidence score can be determined based on these similarities, for example, using a machine learning algorithm for a regression (e.g., linear regression) module and / or a predictive model.

[0059] In block 168, the confidence score is compared to a threshold. This threshold can be associated with a minimum acceptable threshold indicating that the first guest group and the subsequent guest group are the same guest. In response to the determination that the confidence score does not exceed the threshold, no further processing may be performed. For example, in response to the determination that the confidence score does not exceed the threshold, the output of the control signal may be blocked. In other words, a confidence score below the threshold can indicate that the first guest group and the subsequent guest group are not the same guest. Therefore, the subsequent guest group may not be associated with the first guest group and may not indicate that the first guest group has progressed from the first position to the second position in the waiting area.

[0060] However, in response to the determination that the confidence score exceeds a threshold, block 170 may determine the waiting time based on the length of time between the acquisition of the first sensor data and the acquisition of the subsequent sensor data. That is, a confidence score exceeding a threshold may indicate that the first guest group and the subsequent guest group are the same guests, and that the first guest group has progressed from a first position to a second position. In this way, the length of time between the acquisition of the first sensor data and the acquisition of the subsequent sensor data can indicate the time that has elapsed for the guests to progress from a first position to a second position. In one embodiment, the length of time may be determined based on the difference between the respective timestamps associated with the acquisition of the first sensor data and the acquisition of the subsequent sensor data. In further or alternative embodiments, the length of time may be determined based on the operation of a timer.

[0061] In the illustrated example, a single threshold comparison may be used to determine whether a score is high or low confidence. However, as described here, high, medium, and low confidence scores can be distinguished by additional thresholds, each causing a different behavior. A high confidence score may proceed to block 170, a low confidence score to return to block 164, and an intermediate score to generate a prompt for user input and / or collection of additional data.

[0062] In block 172, control signals can be output based on the determined wait time. For example, a control signal can instruct a display to update the wait time display (e.g., to guests at the entrance of the attraction system). Another example is that a control signal can provide notifications to users such as operators, guests, and technicians to inform them of the wait time. For example, based on the notification, a user (e.g., an attraction system worker) can adjust operations such as adjusting the flow of guests at merging points (e.g., changing the flow of guests from the second queue to the main queue), adjusting boarding and alighting operations being performed at boarding / alighting stations, or adjusting the progress of guests in the waiting area. As a further example, a control signal can also instruct adjustments to the operation of the attraction system, such as adjusting components used to entertain guests in the waiting area (e.g., interactive devices, show effects), or adjusting the frequency with which the attraction system operates to entertain guests. Thus, a control signal can update the operation of the attraction system based on the determined wait time.

[0063] Method 160 can be performed continuously while the attraction system is in operation. For example, sensor data from a first location can be continuously received and compared with sensor data from a second location. For example, sensor data can be acquired at predetermined frequencies, such as once every 1 to 10 minutes, once every 10 to 30 minutes, or multiple times per minute. Alternatively, sensor data can be acquired based on different criteria, such as the specific type of attraction system (e.g., rides, theatrical performances), the layout of the waiting area (e.g., number of junctions, length of waiting lines), and / or the number of guests in the attraction system and / or the amusement park. Each prominent attribute can also be continuously compared with each other to determine a confidence score for determining wait times. By performing Method 160 frequently, the time intervals related to guest progress can be determined more frequently. Therefore, more accurate wait times can be determined. For example, multiple wait times can be determined and compared with each other to determine the accuracy of the wait times. In fact, if a determined wait time differs significantly from other determined wait times (e.g., previously determined wait times), that wait time may be inaccurate and can be prevented from being used in the output of control signals to avoid operating the attraction system in an undesirable manner.

[0064] In one embodiment, a prominent attribute can be identified from a set of potential prominent attributes. For example, the presence or absence of a hat is an example of a potential prominent attribute. Therefore, based on the presence of hats in a guest group, that guest group may be selected for acquisition by sensor data, potentially increasing its identifiability from other guest groups. In this way, selecting a group based on the presence of a specific prominent attribute within that group allows for more effective or accurate monitoring of the group's progress within the guest area. In fact, if a group does not possess a potentially prominent attribute of interest (e.g., no one in the group is wearing a hat), such a group may be difficult to distinguish from other guest groups. Therefore, such a group may not be selected for sensor data acquisition. Furthermore, multiple guests may have similar prominent attributes throughout the waiting area (e.g., wearing the same colored shirt and pants, being the same height). In such cases, the specific prominent attribute may not be sufficiently identifiable. In such cases, a guest group with such prominent attributes may not be easily distinguishable from other groups and may be an unsuitable candidate for use in determining wait times. For this reason, the guest group may not be selected for sensor data acquisition, or the acquired sensor data associated with the guest group may not be used for determining wait times and may be discarded.

[0065] In one embodiment, individuals or groups may be ranked based on prominent attributes, and a subset of identified individuals or groups with sufficiently high ranks may be selected for latency determination. In other words, individuals or groups with higher identifiability compared to other individuals or groups may be selected for latency determination based on prominent attributes. For example, image data may be received, and the most distinct individuals or groups may be determined from the image data based on a machine learning algorithm using a subset of potential prominent attributes. The most distinct individuals and / or groups may be assigned the highest rank.

[0066] Furthermore, the progress of different guest groups through different areas of the waiting area can be monitored, and wait times can be determined based on their progress in each area. For example, a particular guest may be wearing a highly identifiable hat shaped like a fruit basket. This guest (or the guest group including that individual) can be selected to determine their progress in a first area of ​​the waiting area adjacent to them. Similarly, a guest group wearing highly identifiable bright orange skirts can be selected to determine their progress in a second area of ​​the waiting area adjacent to them. The overall wait time can then be determined based on the local progress in each of the first and second areas of the waiting area. For example, the wait time can be determined on a rolling basis (e.g., each time an update on the length of time is received) using an expression that correlates the different time spans required for progress in each area of ​​the waiting area with the overall wait time.

[0067] Figure 4 is a flowchart of one embodiment of method 200 for determining wait times in the waiting area of ​​an attraction system. In block 202, a first indicator of time intervals related to the identification of guest groups at different locations in the waiting area can be received and stored. As an example, the first indicator can be received by performing method 160 in Figure 3. That is, each sensor data representing prominent attributes of guest groups at different locations in the waiting area is received, and the prominent attributes of each sensor data can be compared with each other to determine a confidence score. Each guest group associated with the sensor data can be determined to be the same guest in response to the determination that the confidence score exceeds a threshold, and the time interval between the acquisition of each sensor data can be determined as the first indicator. The first indicator is associated with the guest group progressing from a first location to a second location in the waiting area, and can therefore directly indicate the wait time associated with the guest group.

[0068] In block 204, a second metric relating to the operation of the attraction system can be received and stored. The second metric can be received via sensor data and / or user input. As an example, the second metric may include the speed at which the attraction system accepts guests from the waiting area, such as the time it takes to complete the operation cycle of the attraction system (e.g., the speed at which the attraction system's ride vehicles complete one rotation). Thus, the second metric can be associated with how quickly the attraction system can accept additional guests waiting in the waiting area. As an example, the second metric may include the operating mode of the attraction system, the time taken for boarding and alighting operations, the number of ride vehicles in operation, other appropriate parameters, or any combination thereof.

[0069] In block 206, a third indicator regarding the flow of guests throughout the amusement park can be received and stored. This third indicator can also be received via sensor data and / or user input. The third indicator may include, for example, the total number of guests in the amusement park, the number of guests in other attraction systems (e.g., adjacent attraction systems), wait times associated with other attraction systems (e.g., adjacent attraction systems), weather parameters, and time of day. The third indicator can provide information about the potential influx of guests into attraction systems that may affect wait times.

[0070] In block 208, a fourth metric of historical information can be received and stored. Historical information may include previously determined wait times, such as wait times determined on the same day (e.g., most recently determined wait times) and / or on different days (e.g., wait times determined at the same time on different days). Historical information can be used to indicate wait time trends and patterns, and to determine current wait times more accurately.

[0071] In block 210, the current wait time can be determined based on a first indicator, a second indicator, a third indicator, a fourth indicator, or any combination thereof. For example, the current wait time may represent the time elapsed from the entrance of the waiting area to the boarding / alighting station and / or the ride vehicle of the attraction system. For another example, the current wait time may represent the time elapsed from the middle of the waiting area to the boarding / alighting station and / or the ride vehicle.

[0072] In block 212, control signals can be output based on the current wait time. These control signals can instruct a display device to adjust the displayed wait time, provide a notification to the user, and / or otherwise adjust the operation of the attraction system. In this way, the attraction system can be operated more appropriately based on the determined current wait time.

[0073] Figure 5 is a flowchart of one embodiment of a method 230 for updating sensor data used to determine wait times for an attraction system. In block 232, initial sensor data showing the first prominent attributes of the first guest group can be received and stored. In block 234, subsequent sensor data showing the subsequent prominent attributes of subsequent guest groups can be received and stored. For example, the initial sensor data and / or subsequent sensor data can be received using the techniques described in relation to blocks 162 and 164 of Figure 3. The initial sensor data can be associated with a first location in the waiting area of ​​the attraction system, and the subsequent sensor data can be associated with a second location in the waiting area.

[0074] In block 236, a confidence score indicating the match between the initial guest group and the subsequent guest group can be determined using techniques such as those described in relation to blocks 166 and 168 in Figure 3, and compared to a threshold. If the confidence score is determined to be above the threshold, it can be indicated that the initial guest group and the subsequent guest group are the same guests. Therefore, the subsequent sensor data can indicate that the guest group has moved from the first position to the second position in the waiting area.

[0075] In block 238, the initial sensor data can be updated based on subsequent sensor data. For example, the subsequent sensor data may be an updated representation of the guest group. Therefore, the subsequent prominent attributes indicated by the subsequent sensor data can more accurately represent the guest group and should be used for comparison to determine the progress of the guest group in the waiting area. Accordingly, the initial sensor data can be updated to include the subsequent prominent attributes indicated by the subsequent sensor data. In this way, additional sensor data is received, and the additional prominent attributes indicated by the additional sensors can be compared with the subsequent prominent attributes instead of the initial prominent attributes, enabling a more accurate comparison for identifying the guest group at a different location in the waiting area. By updating the initial sensor data in this manner, it becomes possible to more accurately identify the progress of the guest group and more accurately determine the waiting time.

[0076] Figure 6 is a flowchart of one embodiment of a method 260 for determining the wait time of an attraction system. In block 262, first sensor data indicating a first prominent attribute of a first guest group can be received and stored. The first sensor data can be associated with a first position in the waiting area of ​​the attraction system. In block 264, after receiving the first sensor data, second sensor data indicating a second prominent attribute of a second guest group different from the first guest group can be received and stored. The second sensor data can also be associated with a first position in the waiting area. The first sensor data may indicate that the first guest group has passed the first position, and the second sensor data may indicate that the second guest group has passed the first position after the first guest group has passed the first position. In this way, while each guest group is in the waiting area, the first guest group can be positioned ahead of the second guest group.

[0077] In block 266, third sensor data indicating a third prominent attribute of a third guest group can be received and stored. The third sensor data can be associated with a second location in a waiting area, such as a merging point or boarding / alighting station. In other words, the third prominent attribute indicated by the third sensor data can be compared with a first prominent attribute indicated by the first sensor data, and / or with a second prominent attribute indicated by the second sensor data. Based on the comparison, confidence scores can be determined to determine whether the third guest group matches the first or second guest group.

[0078] In block 268, it can be determined that the confidence score indicating the degree to which the second guest group and the third guest group match exceeds a threshold. In this way, the third sensor data can indicate that the second guest group has passed the second location. Therefore, in block 270, the waiting time can be determined based on the time interval between the acquisition of the second sensor data and the acquisition of the third sensor data. This time interval can indicate the time that has elapsed for the second guest group to move from the first location to the second location. Thus, this time interval can be used to determine the waiting time associated with the waiting area.

[0079] In block 272, the first sensor data may be deleted from storage in response to a determination that the confidence score between the second and third guest groups exceeds a threshold. For example, based on the fact that the first sensor data was received before the second sensor data, it is presumed that the first guest group is located ahead of the second guest group in the waiting area, and therefore the first guest group will pass the second location before the second guest group. Thus, if the confidence score exceeds the threshold and indicates that the second guest group has passed the second location, it can also indicate that the first guest group has already passed the second location. For this reason, additional sensor data indicating prominent attributes of the first guest group may no longer be received. In other words, after the third sensor data indicating the third guest group is received, the first guest group may not be identified by subsequent sensor data. For example, the first guest group may have passed the second location without being identified by sensor data associated with the second location. Therefore, the first sensor data may no longer be used to determine the time elapsed for the first guest group to progress from the first location to the second location. In other words, the first sensor data may no longer be relevant in determining latency. In this way, the first sensor data can be deleted from storage, reducing resource consumption associated with managing the first sensor data, such as the storage space used to store the first sensor data. Thus, the process for determining latency can be performed more efficiently.

[0080] The specific embodiments of guest group tracking disclosed herein are performed to estimate wait times, as generally described herein. Thus, in the specific embodiments, guest groups 58 within a waiting area used to track wait times may be referred to as wait time groups. Wait time groups may include individual guests, whether they are family groups or whether they know each other. For example, a group of three people sitting close together in a queue may or may not know each other. However, the group of three people may be usefully grouped as a wait time group for the purpose of tracking progress in the queue based on a combination of prominent attributes. In the specific embodiments, the system may further or alternatively identify or classify guest groups as boarding groups based on the likelihood that the guests know each other and wish to sit together in the vehicle. Wait time groups and boarding groups may or may not overlap. In other words, the groups being tracked may include some or all of the boarding groups. For example, a wait time group may include fewer guests than boarding groups, so that the system can efficiently estimate wait times with sufficient accuracy by selecting a small number of guests with highly identifiable prominent attributes from among the local potential guests. However, since passenger groups are self-selected, each group could contain 6, 7, or even more guests, which may exceed the number needed for efficient wait time tracking.

[0081] Figure 7 is a flow diagram of process 300 for identifying passenger groups and estimating wait times using the identified passenger groups. In block 302, system 50 can receive sensor data indicating prominent attributes of guests in at least one queue. For example, at least one queue could be a first queue 64, a second queue 66, and / or a merging queue or boarding / alighting area. Based on prominent attributes, guests can be tracked to estimate throughput within the queue. Alternatively, in block 304, system 50 can identify a group of guests 58 who are likely to wish to sit together in the vehicle 54 as a passenger group.

[0082] In one embodiment, ride groups can be identified based on prominent attributes indicated by sensor data. For example, prominent attributes may include the same shirt, the same hat, or other matching clothing items. A group of guests 58 with matching clothing items can be pre-identified as a ride group. In one embodiment, ride groups can be identified based on other data sources, such as guests scanned together at the entrance to the attraction or guests who self-declare themselves as a family group through guest profile information or linked accounts. When scanned, biometric data (e.g., facial features, fingerprints) may be acquired for use in tracking ride group guests throughout the queue. In one embodiment, ride groups can be identified based on operator or user input (e.g., scanning together, entering a group number into the queue guest interface or operator interface). For example, users can stand together in a designated area and be scanned as a ride group. As another example, an operator can provide input to identify a ride group to the user interface by scanning user profile information from the guests' mobile devices or by entering the number of guests in each group passing through. In one embodiment, this input can be a group photograph from which prominent attributes can be extracted and subsequently tracked using sensor data acquired within the queue. In one embodiment, ride groups can be identified based on data acquired within the amusement park to identify guests who participated in other activities together, such as dining together at a restaurant or riding other attractions together. These features can be used to generate confidence scores associated with the wait time groups generally described here. High confidence scores above a threshold may automatically pass as ride groups, scores in the middle range may be flagged for operator input, and low confidence scores may be automatically rejected.

[0083] In one embodiment, the attraction may include a single-passenger queue (e.g., a third queue 67, see Figure 1) that merges with one or more other queues, such as a boarding / alighting area. In block 306, data from one or more other queues can be used to estimate the wait time for a single-passenger queue by identifying boarding groups and estimating the boarding arrangement of the ride vehicle that would result in vacancies based on the tracked boarding groups. If nearby groups in the queue cannot fill the ride vehicle, one passenger is used. Thus, the ride vehicle boarding model logic can use the input of the number of guests in a group and the total number of guests to determine whether one passenger is needed and / or the number of seats for one passenger in the ride vehicle. Thus, the wait time for a single-passenger queue is a function based on the estimation of the number of available seats for one passenger. Groups can be identified based on the information provided here and the tracking information within the queue (e.g., guests standing close to each other, talking) as well as tracking data before entering the queue.

[0084] In one embodiment, identified and tracked groups can be used to propose or estimate passenger seating arrangements for a vehicle. For example, the controller 74 can use passenger group logic to estimate how different groups might fill the seats in a vehicle and identify available seats for one passenger. The vehicle's passenger model can incorporate information about a subset of guests present in a boarding / alighting area or waiting area. Based on the collected identification data of different tracked groups, guests can be identified as groups of two, three, four, five, etc.

[0085] For example, a vehicle can be configured with a total of 8 seats in each carriage. Therefore, each carriage can be filled with two groups of 4, a group of 5 and a group of 3, a group of 6 and a group of 2, and so on. If a group of 7 is identified, that group is likely to leave one empty seat, which can be flagged as an available seat in the waiting line for one passenger. Similarly, if a group of 5 sits with a group of 2, there is likely to be one empty seat, which can be flagged as an available seat in the waiting line for one passenger.

[0086] As another example, if there are four seats side-by-side, a group of three (58 people) is likely to leave one seat empty. This likely empty seat can be flagged as an available seat in the single-passenger queue and used to track estimated wait times for both the regular queue and special queues such as the single-passenger queue. In this way, guests are provided with estimated wait times for both the regular queue and the single-passenger queue (e.g., via push notifications or displays), allowing them to determine which queue is faster or slower.

[0087] The vehicle passenger model can generate proposed group configurations for each vehicle in real time based on the vehicle's placement and capacity. The proposed passenger configurations based on available guest groups can be transmitted to the operator interface to provide guidance for directing different groups to different vehicles. Since boarding and alighting areas may not be as orderly as waiting lines (e.g., guests standing in groups), this guidance can help operators identify tracked groups within large groups of guests. In one embodiment, the interface can be an augmented reality (AR) interface that can assist identification by overlaying displays for identified groups. The vehicle passenger model can also use logic based on the vehicle's capacity and characteristics (e.g., age / height restrictions) to identify group members who may utilize the child swap option. Furthermore, the vehicle passenger model can identify children who may wish to sit next to their parents, and absent group members who may return before boarding (e.g., guests using the restroom).

[0088] In one embodiment, the ride group logic can identify members within a proposed ride group who are moving through the queue together but may not be boarding the ride vehicle. Thus, the identified ride group may include non-participating members. For example, non-participating members are tracked with the ride group but are not included in the ride vehicle's passenger configuration. As an example, these members may self-declare at the ride entrance. In one embodiment, these members may be identified by sensor data. In one embodiment, if bags are not permitted on the ride vehicle, sensor data may identify guests carrying the group's bags as non-participating members. In one embodiment, if a non-participating member is presumed to be too young for the ride, another member of the ride group may be considered as a guardian for a child swap. As another example, a park guide who is determined to be a non-participating member may be identified by specific clothing items or identifying accessories that are identifiable by sensor data.

[0089] Only certain features of the present invention are shown and described herein, but many modifications and changes can be made by those skilled in the art. Therefore, it should be understood that the appended claims are intended to cover all modifications and changes that are in line with the true spirit of the invention.

[0090] The technologies described and claimed herein are not abstract, intangible, or purely theoretical, but are applied to and apply to tangible objects and specific examples of a practical nature, and certainly improve the art. Furthermore, if any of the claims appended to the end of this specification contain one or more elements designated as "...means for performing [function]" or "...steps for performing [function]," such elements are intended to be construed in accordance with 112(f) of the U.S. Patent Act. On the other hand, any claim containing elements designated in any other form is not intended to be construed in accordance with 112(f) of the U.S. Patent Act.

Claims

1. It is an amusement park attraction system. Waiting area and It is a controller, The system receives first sensor data indicating one or more first prominent attributes associated with a first guest group within the waiting area. The system receives second sensor data indicating one or more second prominent attributes associated with a second guest group within the waiting area. The first sensor data and the second sensor data are compared with each other. Based on a comparison of the first sensor data and the second sensor data, a confidence score associated with the degree of agreement between the first guest group and the second guest group is determined. In response to determining that the confidence score exceeds a threshold, a control signal is output. A controller configured as follows, Equipped with, Amusement park attraction system.

2. The aforementioned controller, The time interval between the acquisition of the first sensor data and the acquisition of the second sensor data is determined. The amusement park attraction system according to claim 1, configured to output the control signal based on the aforementioned time interval.

3. The aforementioned controller, Based on the aforementioned time range, the waiting time associated with the waiting area is determined, The amusement park attraction system according to claim 2, configured to output the control signal based on the waiting time.

4. The amusement park attraction system according to claim 3, comprising a display, wherein the controller is configured to output the control signal to instruct the display to provide an image based on the waiting time.

5. The amusement park attraction system according to claim 3, wherein the waiting area comprises an interactive device, and the controller is configured to output the control signal to instruct the interactive device to output either or both visual effects and / or sound effects based on the waiting time.

6. A first sensor configured to detect one or more prominent first attributes of a first position in the waiting area and to transmit the first sensor data to the controller, The amusement park attraction system according to claim 1, further comprising: a second sensor configured to detect one or more second prominent attributes of a second location in the waiting area and to transmit the second sensor data to the controller.

7. The amusement park attraction system according to claim 1, wherein the one or more first prominent attributes and the one or more second prominent characteristics include one or more body dimensions, one or more weights, one or more types of clothing, one or more colors of clothing, or any combination thereof.

8. When executed by a processor, that processor will Receiving multiple sensor data representing one or more prominent attributes associated with each guest group in the waiting area of ​​an amusement park attraction system, Comparing the first sensor data from the plurality of sensor data with the second sensor data from the plurality of sensor data, Determining that the confidence score associated with the comparison between the first sensor data and the second sensor data among the plurality of sensor data exceeds one or more thresholds, The time interval between the acquisition of the first sensor data from the plurality of sensor data and the acquisition of the second sensor data from the plurality of sensor data is determined. Outputting a control signal in response to and / or based on the aforementioned time width, A non-temporary computer-readable medium containing instructions configured to cause a procedure including the execution of a procedure.

9. When the aforementioned command is executed by the processor, the processor will: Determining the waiting time associated with the waiting area based on the aforementioned time width, A non-temporary computer-readable medium according to claim 8, configured to perform a procedure including outputting the control signal based on the waiting time.

10. When the aforementioned command is executed by the processor, the processor will: To receive a first indicator of the operation of the amusement park attraction system, Receiving the second indicator of guest flow, Receiving a third indicator of previously determined waiting times for the amusement park attraction system, A non-temporary computer-readable medium according to claim 9, configured to perform a procedure including determining the waiting time based on the first indicator, the second indicator, the third indicator, or any combination thereof, plus the time interval between the acquisition of the first sensor data and the second sensor data.

11. When the aforementioned command is executed by the processor, the processor will: A non-temporary computer-readable medium according to claim 9, configured to perform a procedure including identifying at least one passenger group from each of the aforementioned guest groups and generating instructions to board a vehicle based on the identified at least one passenger group.

12. When the aforementioned command is executed by the processor, the processor will: The first sensor data from the aforementioned multiple sensor data is received and stored. The process involves receiving and storing the initial sensor data, and then receiving the first sensor data. A non-temporary computer-readable medium according to claim 8, configured to perform a procedure including: in response to determining that the confidence score associated with a comparison of the first sensor data and the second sensor data exceeds a first threshold among the one or more thresholds; and deleting the first sensor data from storage based on the receipt of the first sensor data after the receipt of the first sensor data.

13. The non-temporary computer-readable medium according to claim 8, wherein the command, when executed by the processor, is configured to cause the processor to output the control signal for instructing the adjustment of the operation of the amusement park attraction system based on the time interval.

14. When the aforementioned command is executed by the processor, the processor will: After receiving the first sensor data, the intermediate sensor data among the plurality of sensor data is received, It is determined that the first confidence score associated with the comparison between the first sensor data and the intermediate sensor data among the plurality of sensor data exceeds an additional threshold, A non-transient computer-readable medium according to claim 8, configured to perform a procedure including adjusting the first sensor data based on the intermediate sensor data in response to determining that the initial confidence score exceeds the additional threshold.

15. The non-temporary computer-readable medium according to claim 14, wherein the first sensor data represents one or more initial prominent attributes, the intermediate sensor data represents one or more subsequent prominent attributes, and the command, when executed by the processor, causes the processor to adjust the first sensor data by changing the one or more initial prominent attributes represented by the first sensor data to the one or more subsequent prominent attributes represented by the intermediate sensor data in response to the determination that the initial confidence score exceeds the additional threshold.

16. It is an amusement park attraction system. Waiting area and It is a controller, The system receives first sensor data indicating one or more first prominent attributes associated with a first guest group within a first location in the waiting area. Receiving second sensor data indicating one or more second prominent attributes associated with a second guest group within a second location in the waiting area, The reliability score is determined based on a comparison of the first sensor data and the second sensor data, wherein the reliability score is associated with the degree of agreement between the first guest group and the second guest group. The confidence score is compared with a threshold, In response to the determination that the confidence score exceeds the threshold, the waiting time is determined based on the time interval between the acquisition of the first sensor data and the acquisition of the second sensor data, wherein the time interval represents the time elapsed during the movement of the waiting area from the first position to the second position. The operation of the attraction is adjusted in response to and / or based on the determined waiting time. A controller configured as follows, Equipped with Amusement park attraction system.

17. The attraction system according to claim 16, wherein the waiting area comprises a first waiting line and a second waiting line, the first waiting line and the second waiting line are configured to meet at a confluence point, and the second position of the waiting area constitutes the confluence point.

18. The attraction system according to claim 16, wherein the waiting area comprises a queue of one passenger.

19. The aforementioned controller, Identify one or more passenger groups, including at least one guest from the first guest group and / or the second guest group. Based on the identified passenger group, the number of passengers in one vehicle of the attraction system is estimated. The attraction system according to claim 18, configured to generate an estimated wait time for a single passenger queue based at least partially on the estimated number of passengers per passenger.

20. The attraction system according to claim 19, wherein the one or more passenger groups are identified based on the one or more first attributes, the one or more second attributes, the profile information of the first guest group or the second guest group and / or a common waiting entrance timestamp.

21. The attraction system according to claim 19, wherein the one or more passenger groups are identified based on data from outside the attraction system in the amusement park.

22. The attraction system according to claim 19, wherein the one or more passenger groups, including the first passenger group, are identified based on the one or more first attributes, or the one or more second attributes, including identified matching clothing items unique to the first passenger group.

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