Queuing Analytics using Smartphones

We present QueueVadis, a system that addresses the problem of estimating, in real-time, the properties of queues at commonplace urban locations, such as coffee shops, taxi stands and movie theaters. Abjuring the use of any queuing-specific infrastructure sensors, QueueVadis uses participatory mobile sensing to detect both (i) the individual-level queuing episodes for any arbitrarily-shaped queue (by a characteristic locomotive signature of short bursts of “shuffling forward” between periods of “standing”) and (ii) the aggregate-level queue properties (such as expected wait or service times) via appropriate statistical aggregation of multi-person data. Moreover, for venues where multiple queues are too close to be separated via location estimates, QueueVadis also uses a novel disambiguation technique to separate users into multiple distinct queues. User studies, performed with 138 cumulative total users observed at 23 different real-world queues across Singapore and Japan, show that QueueVadis is able to (a) identify all individual queuing episodes, (b) predict service and wait times fairly accurately (with median estimation errors in the 10%-20% range), independent of the queue’s shape, (c) separate users in multiple proximate queues with close to 80% accuracy and (d) provide reasonable estimates when the participation rate (the fraction of QueueVadis-equipped people in the queue) is modest.