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his/herpreference,but it requiresauser to inputa lotof informationsuchas selectionof
PoIs (Pointof Interest), hence takesa lotof time toobtainaplan.Tosolve thisproblem,
therehavebeenproposedautomatic tourplanning systems (ATPS)whichautomatically
select PoIs in the specified area and compose/show users a sightseeing tour including
movingwaysbetweenPoIs.
InexistingATPS,variousobjective functionsareconsidered.Shiraishi et al.[1]pro-
posed an ATPS which solves amulti-objective optimization problem taking into ac-
count a trade-off betweenmonetary cost and satisfactiondegree.Wuet al. [2] proposed
anATPSwhichmaximizes satisfaction degree taking into account tourist’s remaining
staminaasaconstraint.
Ingeneral,usersdonotalwaysselect theshortestor thecheapest routeduringsight-
seeingtourbecause theirmainpurpose is togetsatisfaction/experience throughsightsee-
ingactivities.Whereas, theydonotalwaysmaximize thesatisfactionsince their resource
(money, time and stamina) is limited.Hence, for themovebetweenPoIs,wemust con-
sider the trade-off between the satisfactionof theuser and the time,moneyand stamina
asuser’s resource.For theselectionofPoI,wemustalsocomparemultiplePoIs in terms
of the trade-off between the satisfaction and the resource consumption.As such,ATPS
can be formulated as amulti-objective optimization problemwith several independent
factors.
Inthispaper,weformulate thesightseeingtourrecommendationasamulti-objective
optimizationproblemwithmoney, timeandstaminaconsumptionduringa tour andsat-
isfactiondegreeobtainedby the touras independentvariables.Since thisproblem isNP-
hard,we propose a heuristic algorithm to quickly obtain semi-pareto optimal solutions
based on genetic algorithmNSGA-II [3].We applied the proposedmethod to planning
tours targeting 30 tourist spots in Higashiyama-area of Kyoto, Japan. As a result, we
confirmed that our algorithm could output semi-pareto optimal solutions in reasonable
time that couldbeused fordecisionmakingunder trade-off.
Theremainderof thepaper isstructuredasfollows:Section2overviewstheexisting
studies related to our proposal. Section 3 provides the formulation of our target prob-
lem. Section 4 describes the proposed algorithm based onGA. Section 5 provides the
experimental results toevaluateourmethodandfinallySection6concludes thepaper.
2. RelatedWork
Mostof the existing tournavigation systems recommendPoIswithhighaverage review
scores and/or according to the user’s preference [4,5,6]. These studies focus on the im-
provementofuser satisfactionand recommendasinglePoIusingakindoffilteringsys-
tems,but theydonotconsider thewhole tourplanning includingroutesandother factors
related to tourism.
Some studies support thewhole tour planningby connectingmultiplePoIs.Kurata
et al. [7,8]createdaweb-based interactive tourplanningservicecalledCT-Planner.This
serviceplansand recommendsa tour routewhileanalyzing theuser’spreference.
There are some studies that add other factors related to tourism such asmoney,
time and stamina as constraints [9,10,11,12]. For example,Wuet al. [2] considered the
staminaasaconstraint.However, theseexistingstudies focusonlyon the satisfactionof
users as themain factor and do not consider the trade-off between the satisfaction and
other factors suchasmoney, time, andstamina.
Y.Hiranoetal. /AMethod forGeneratingMultipleTourRoutes 181
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Titel
- Intelligent Environments 2019
- Untertitel
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Autoren
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-Cía
- Verlag
- IOS Press BV
- Datum
- 2019
- Sprache
- deutsch
- Lizenz
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Abmessungen
- 16.0 x 24.0 cm
- Seiten
- 416
- Kategorie
- Tagungsbände