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.

Abstract

Today, smart cities are turning to electric transport, carpooling and zero emission zones. The growing number of electric vehicles on the roads makes it increasingly necessary to have a public charging infrastructure. On the other hand, the main limitations of electric vehicles are the limited range of their batteries and their relatively long charging times. To avoid having problems to recharge, electric vehicle drivers must plan their journeys more thoroughly than others. At the goal of optimizing trip time, drivers need to automate their travel plans based on a smart charging solution, which will require the development of new Vehicle-to-Grid applications that will allow at the charging stations to dynamically interact with the vehicles. In this paper, we propose an architecture based on an algorithm allowing the management of charging plans for electric vehicles traveling on the road to their destination, in order to minimize the duration of the drivers&rsquo journey including waiting and charging times. The decision taken by the algorithm based on the exploration of the data of each public supply station according to its location, number of vehicles in the queue, number of charging sockets, and rates of service. Document type: Article