a web application for recommending personalized mobile tourist routes

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  • 1. www.ietdl.orgPublished in IET SoftwareReceived on 3rd September 2011Revised on 23rd December 2011doi: 10.1049/iet-sen.2011.0156 ISSN 1751-8806Web application for recommending personalisedmobile tourist routesD. Gavalas1 M. Kenteris1 C. Konstantopoulos2 G. Pantziou31Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece2Department of Informatics, University of Piraeus, Piraeus, Greece3Department of Informatics, Technological Educational Institution of Athens, Athens, GreeceE-mail: dgavalas@aegean.grAbstract: This study deals with the problem of deriving personalised recommendations for daily sightseeing itineraries fortourists visiting any destination. The authors approach considers selected places of interest that a traveller would potentiallywish to visit and derives a near-optimal itinerary for each day of visit; the places of potential interest are selected based onstated or implied user preferences. The authors method enables the planning of customised daily personalised touristitineraries considering user preferences, time available for visiting sights on a daily basis, opening days of sights and averagevisiting times for these sights. Herein, the authors propose a heuristic solution to this problem addressed to both web andmobile web users. Evaluation and simulation results verify the competence of the authors approach against an alternative method.1Introduction interests, up-to-date information for the sight and informationabout the visit (e.g. date of arrival and departure,Tourists who visit a destination for one or multiple days are accommodation address etc.), a mobile guide can suggestunlikely to visit every tourist sight; rather, tourists are faced near-optimal and feasible routes that include visits to awith the dilemma of which points of interest (POIs) would series of sights, as well as recommending the order of eachbe more interesting for them to visit. These choices aresights visit along the route [7]. Generalised tourist routesnormally based on information gathered by tourists viado not take into consideration the context of the user forinternet, magazines, printed tourist guides etc. Afterexample. the starting or ending point of the user, thedeciding on which sights to visit, tourists have to decide on available time the user affords, the current time, predictedwhich route to take, that is, the order in which to visit eachweather conditions while on journey etc. Taking intoPOI, with respect to the visiting time required for each POI, account the parameters of context and location awarenessthe POIs visiting days/hours and the time available forbrings forward a challenge for the design of appropriatesightseeing on a daily basis. tourist routes [8]. Kramer et al. [9] analysed the interests in Tourists encounter many problems following thisthe proles of each tourist and concluded that theyprocedure. The information contained in printed guide particularly varied from each other. This conclusionbooks is often outdated (e.g. the opening times of some supports the argumentation for deriving personalised,museums might have changed or some other memorial sites instead of generalised, tourist routes.might be closed because of maintenance works etc.), theGiven a list of sights of some tourist destination in which aweather conditions might be prohibitive during one of the user-tourist would potentially be interested in visiting, thevisiting days to visit an important POI etc. [1]. The problem involves deriving the order in which the touristselection of the most important and interesting POIs forshould visit the selected POIs, for each day the tourist staysvisiting also requires fusion of information typicallyat that destination. We term this problem as the touristprovided from separate often non-credible sources. Usually, itinerary design problem (TIDP). Interestingly, the TIDPtourists are satised if a fairly attractive or feasible route is presents similarities to problems that have arisen in the pastderived, yet, they cannot know of any alternative routes that in the eld of operational research; such problems are basedwould potentially be better to follow. Some tourist guideson the mathematical theory of graphs (graph theory) anddo acknowledge such problems and try to propose morecomprise variations of the well-known travelling salesmangeneralised tourist routes to a city or an area. Of course, problem (TSP).these routes are designed to satisfy the likes of the majority For instance, the team orienteering problem (TOP)of its readers, but not those with specialised interests, needs appoints an initial and nal point as well as N points foror constraints [2]. visiting, where each point is associated with a score or Mobile tourist guides may be used as tools to offer solution prot. Given a particular time margin for each of the Mto these types of problems [3 6]. Based on a list of personal team members, the TOP determines M routes (from theIET Softw., pp. 1101doi: 10.1049/iet-sen.2011.0156 & The Institution of Engineering and Technology 2012

2. www.ietdl.orginitial to the end point) via a subset of N points, aiming at considering several user constraints. Google city toursmaximising the overall prot of visited points [10]. Theapplication [21] represents another interesting approachTOP cannot be solved in polynomial time (NP-complete) along the same line suggesting multiple daily itineraries[11], hence heuristics deriving near-optimal solutions arethrough the familiar Google maps interface. Yet, thethe only realistic way to tackle such problems, especiallysuggested itineraries are not personalised. Furthermore, citywhen considering online applications. TOP can be thoughttours implementations are only provided through a webof as a starting point to model TIDP whereby the M team interface and have not been tested on mobiles; hence, theymembers are reduced to the number of days available for lack location-based and context-aware features [16, 17]. Onthe tourist to stay and the prot of a sight signies the the other hand, P-Tour and DTG have been implementedpotential interest (or degree of satisfaction) of a particularon mobiles, but can only deal with a small number of POIstourist visiting the POI within a given time span available (i.e. their scalability is questionable).for sightseeing daily (therefore TOP considers the timespent while visiting each POI as well as the time needed totravel from one POI to another).3 DailyTRIP modelling Nevertheless, TOP does not take into consideration the DailyTRIP modelling involves the denition and thePOIs visiting days and hours. Therein, the resemblance ofdescription of the user model, visit model and the sightTIDP with another operational research problem (travelling(POI) model (see Fig. 1) taking into considerationsalesman problem with time windows, TSPTW) [12] comes parameters/constraints like those listed below:forward. TSPTW concerns the minimum cost path for avehicle which visits a set of nodes. Each node must be1. User model:visited only once and the visit must be carried out inside anallowed time interval (time window). The correlation of device (e.g. screen resolution, available storage space,time windows with the POIs visiting days/hours is obvious.processing power etc.);However, TSPTW involves planning of only one route (i.e. language of content, localisation;not M, as many as days available to the tourist to visit personal demographic data (e.g. age, educationalPOIs), while it requires the vehicle to visit the whole set oflevel);nodes. A generalisation of TOP and TSPTW is referred to interests (explicit declaration or implicitly collected);as team orienting problem with time windows (TOPTW) disability (e.g. blind, deaf, kinetic disability);[13] and considers multiple vehicles (i.e. itineraries) that budget threshold willing to spend on sightseeing.should visit a subset of nodes, each within its allowed timewindow. 2. Visit model: The main contribution of this paper lies in modelling andinvestigating a generalisation of TOPTW through introducing geographical location of accommodation;a novel heuristic that provides near-optimal solutions to period of stay (arrival and departure date);TIDP: the daily tourist itinerary planning (DailyTRIP). It is time constraints (e.g. available time each day to tour,noted that some preliminary ideas of our technique have alsonumber and duration of desirable breaks etc.);been presented in [14]. means of travel (e.g. walking, driving, bus, metro etc.). The remaining of this article is organised as follows: relatedwork is discussed in Section 2. The modelling, design and 3. Sight (POI) model:implementation of DailyTRIP are presented in Sections 3 5, respectively. Section 6 compares the approaches taken by category (e.g. museum, archaeological site, monumentDailyTRIP against a relevant algorithmic solution. Sectionetc.);7 discusses simulation results whereas Section 8 draws available multimedia resources (collection of texts,conclusions and grounds for future work.video, audio etc. localised in different languages; geographical position (coordinates);2Related work weight or objective importance (e.g. the Acropolis ofAthens is thought to be objectively more important ofThe issue of personalised tourist itineraries has not beenthe Coin Museum of Athens, hence the Acropolis islooked at in the electronic and mobile tourism literature,assigned a larger weight);with the exception of the algorithms proposed in [11, 15]. average duration of visit (e.g. the ArchaeologicalIn Souffriau et al. [15], proposed a heuristic solution for the Museum of Athens typically takes longer to visit thanorienteering problem, that is, they only consider a singletourist itinerary. The algorithm presented in [11] deals withTOPTW and also takes into account the time needed tovisit a sight; hence, it is directly comparable with our work. Other relevant research projects with respec

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