A system and method for managing virtual queues. A cloud-based queue service manages a plurality of queues hosted by one or more entities. The queue service is in constant communication with the entities providing queue management, queue analysis, and queue recommendations. The queue service is likewise in direct communication with queued persons. Sending periodic updates while also motivating and incentivizing punctuality and minimizing wait times based on predictive analysis. The predictive analysis uses “Big Data” and other available data resources, for which the predictions assist in the balancing of persons across multiple queues for the same event or multiple persons across a sequence of queues for sequential events.

The invention relates to a control device of a cable transport facility, comprising a controller capable of organizing an embarking period of a nominal duration (Tn_max) for vehicles at a station. This embarking period (Tn_max) ends with a prohibition of access to the vehicle. The controller comprises an input for receiving at least one variable representative of a vehicle occupancy. The controller is arranged to react upon receipt of such a variable during the embarking period (Tn_max) by comparing it to a threshold value and by triggering the prohibition of access to the vehicle in an anticipated manner and before the end of the embarking period (Tn_max) as a function of the result of the comparison between said variable and the threshold value.

The invention relates to a control device of a cable transport facility, comprising a controller capable of organizing an embarking period of a nominal duration (Tn_max) for vehicles at a station. This embarking period (Tn_max) ends with a prohibition of access to the vehicle. The controller comprises an input for receiving at least one variable representative of a vehicle occupancy. The controller is arranged to react upon receipt of such a variable during the embarking period (Tn_max) by comparing it to a threshold value and by triggering the prohibition of access to the vehicle in an anticipated manner and before the end of the embarking period (Tn_max) as a function of the result of the comparison between said variable and the threshold value.

This disclosure relates generally to methods and systems for determining whether at least a first component and a second component of a personal protection (PP) configuration are compatible so as to be assembled into an approved PP configuration, responsive to being compared against at least one predetermined criterion. Included is a smart tag for each of the first and second components. The first and second smart tags include respective first and second data. A data acquiring system is provided for acquiring the first and second data and a data processing system processes the acquired first and second data for determining if the first and second components are compatible to be assembled as an approved PP configuration.

This disclosure relates generally to methods and systems for determining whether at least a first component and a second component of a personal protection (PP) configuration are compatible so as to be assembled into an approved PP configuration, responsive to being compared against at least one predetermined criterion. Included is a smart tag for each of the first and second components. The first and second smart tags include respective first and second data. A data acquiring system is provided for acquiring the first and second data and a data processing system processes the acquired first and second data for determining if the first and second components are compatible to be assembled as an approved PP configuration.

A method for utilizing IoT information to generate queue related augmented reality (AR) information associated with a transportation system. In an embodiment, the method includes at least one computer processor identifying a plurality of users within a staging area of a transportation system. The method further includes determining groups of users from among the plurality of users, based on a transportation route respectively associated with transportation information corresponding to respective users. The method further includes identifying a first vehicle that a first user is scheduled to board, based on an indication of the first vehicle in the transportation information associated with the first user. The method further includes determining AR information related to queues associated with boarding the first vehicle and transportation information corresponding to groups of users within a proximity of the queues associated with boarding the first vehicle, and presenting to the first user, the AR information.

A method for utilizing IoT information to generate queue related augmented reality (AR) information associated with a transportation system. In an embodiment, the method includes at least one computer processor identifying a plurality of users within a staging area of a transportation system. The method further includes determining groups of users from among the plurality of users, based on a transportation route respectively associated with transportation information corresponding to respective users. The method further includes identifying a first vehicle that a first user is scheduled to board, based on an indication of the first vehicle in the transportation information associated with the first user. The method further includes determining AR information related to queues associated with boarding the first vehicle and transportation information corresponding to groups of users within a proximity of the queues associated with boarding the first vehicle, and presenting to the first user, the AR information.

A system is provided and generally includes a server, an associate computing device, and a customer computing device. The server may receive data from the customer computing device indicating that a customer is picking up items from a predetermined location. The server may compute an estimated wait time for the customer based on one or more machine learning processes. In some examples, a number of unexpected customers that may arrive is determined. The machine learning process may compute the estimated wait time based on the number of unexpected customers. The machine learning process may be trained with historical data. The estimated wait time is transmitted to the customer computing device, and is displayed to the customer. In some examples, the server sends a list of customers waiting to be serviced to the associate computing device. The list may be prioritized based on estimated wait times for those customers.

A system is provided and generally includes a server, an associate computing device, and a customer computing device. The server may receive data from the customer computing device indicating that a customer is picking up items from a predetermined location. The server may compute an estimated wait time for the customer based on one or more machine learning processes. In some examples, a number of unexpected customers that may arrive is determined. The machine learning process may compute the estimated wait time based on the number of unexpected customers. The machine learning process may be trained with historical data. The estimated wait time is transmitted to the customer computing device, and is displayed to the customer. In some examples, the server sends a list of customers waiting to be serviced to the associate computing device. The list may be prioritized based on estimated wait times for those customers.

There is a need to effectively determine which users should be given priority for using a public service. A way of improving the public service can be to acquire information on congestion degree in use of the public service, to determine whether the congestion degree is higher than a specified level or not, and to transmit information that urges cease of the use of the public service being used to users who have higher utilization of the public service than other users among a plurality of users if the congestion degree is higher than the specified level.