Crowdsourcing Queue Estimations in Situ

Abstract

 We present the development and evaluation of a situated crowdsourcing mechanism that estimates queue length in real time. The system relies on public interactive kiosks to collect human estimations about their queue waiting time. The system has been designed as a standalone tool that can be retrospectively embedded in a variety of locations without interfacing with billing or customer systems. An initial study was conducted in order to determine whether people who just joined the queue would differ in their estimates from people who were at the front of the queue. We then present our system’s evaluation in four different restaurants over 19 weekdays. Our analysis shows how our system is perceived by users, and we develop 2 ways to optimise the waiting time estimation: by correcting the estimations based on the position of the input mechanism, and by changing the sliding window considered inputs to provide better prediction. Our analysis shows that approximately 7% of restaurant customers provided estimations, but even so our system can provide predictions with up to 2 minute mean absolute error.