Route Recommendations to Business Travelers Exploiting Crowd-Sourced Data

Thomas Collerton (), Andrea Marrella (), Massimo Mecella () and Tiziana Catarci ()
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Thomas Collerton: Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy
Andrea Marrella: Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy
Massimo Mecella: Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy
Tiziana Catarci: Department of Computer, Control and Management Engineering Antonio Ruberti (DIAG), University of Rome La Sapienza, Rome, Italy

No 2017-07, DIAG Technical Reports from Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza"

Abstract: Business travellers are those people who attend work-related meetings and in their few hours of spare time would like to see the best that the host city can offer in terms of cultural activities and sightseeings. In this work we present a complex architecture, consisting of mobile applications and back-end server components, which supports such a type of users in recommending possible routes within their constraints. The three main contributions are (i) a set of machine learning algorithms that can be used to detect a queuing state of a user with a high degree of accuracy, (ii) how to determine user’s positioning, and (iii)how to practically realize a planner providing a reasonably good enough route plan within a handful of seconds. Preliminary tests demonstrate that the single components of the proposed architecture are feasible and provide good results.

Keywords: planning; crowd-sourced data; cultural heritage; smart tourism (search for similar items in EconPapers)
Date: 2017
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