Science of Machine Learning

Navigate people with comfortable traveling route

- Dynamic migration scheduling for greater visitor satisfaction -

Abstract

We have developed a technology to dynamically compute and recommend comfortable migration schedules for customers in such as amusement parks based on spatio-temporal prediction of near-future congestion levels and resource demands by using real-time observation of the people flow and preferred attraction information. This technology is aimed at equalizing waiting time at attraction queues in a venue and maximizing customer satisfaction by real-time processing of spatio-temporal prediction of people flow and mathematical optimization of visitor’s migration schedules. It is also expected to support stable control of infrastructure and optimal resource management in and around venues such as leisure spots, airports, and commercial facilities.

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Poster


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Presenters

Tomoharu Iwata
Tomoharu Iwata
Ueda Research Laboratory
Hitoshi Shimizu
Hitoshi Shimizu
Innovative Communication Laboratory
Tatsushi Matsubayashi
Tatsushi Matsubayashi
Service Evolution Laboratories
Futoshi Naya
Futoshi Naya
Innovative Communication Laboratory
Naonori Ueda
Naonori Ueda
Ueda Research Laboratory