CT-Planner4: Toward a More User-Friendly Interactive Day-Tour Planner

Kurata, Yohei; Hara, Tatsunori

doi:10.1007/978-3-319-03973-2_6

Cited by 32 publications

(12 citation statements)

References 14 publications

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“…Then, the selected POIs are sequenced along a sightseeing itinerary by a heuristic algorithm addressing an instance of the Traveling Salesman Problem. Kurata and Hara (2014) presented CT-Planner4 2 , a web-based tourist tour relying on a genetic algorithm which solves the Selective Traveling Salesman Problem (STSP) (Laporte & Martello, 1990). The solver starts with n random initial tours and ends up to a tour plan with the highest utility score (i.e.…”

Section: Related Workmentioning

confidence: 99%

The eCOMPASS multimodal tourist tour planner

Gavalas

Kasapakis

Konstantopoulos

et al. 2015

Expert Systems with Applications

106

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Section: Related Workmentioning

confidence: 99%

The eCOMPASS multimodal tourist tour planner

Gavalas

Kasapakis

Konstantopoulos

et al. 2015

Expert Systems with Applications

106

View full text Add to dashboard Cite

“…Half of the works presented in [28] implement hybrid systems. Several recommendation systems integrate artificial intelligence techniques such as multi-agent systems [30][31][32], optimisation techniques (ant colony optimisation [33], genetic algorithms [34], iterated local search [2,35], greedy randomised adaptive search methods [36]), automatic clustering (k-nearest neighbours approach [37,38], k-means algorithms [39,40], fuzzy c-means [41]), management of uncertainty (Bayesian networks, fuzzy logic [42,43], rule-based approaches [44], and knowledge representation (ontologies [45][46][47]). For the evaluation of these systems, four methods are proposed in [29]: (1) real-life evaluations (based on precision, recall, and the harmonic mean of both precision and recall), (2) heuristic-based evaluations (based on total POI recommended, POI popularity or tourist interest), (3) crowd-based evaluations (using qualitative measures that focus on user experiences), and (4) online controlled experiments (design-based variants and algorithm-based variants).…”

Section: Related Workmentioning

confidence: 99%

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

Rajaonarivo

Fonteles

Sallaberry

et al. 2019

IJGI

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The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.

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“…CT-Planner4 [6] is a web-based tourist tour planner for seven (7) Japanese cities. Recommended tours are personalized with respect to: (a) user focus and taste, which adjust POI profits, and (b) preferred moving speed and reluctance to walk, which adjust walking travel times.…”

Section: Web and Mobile Ttdp Solversmentioning

confidence: 99%

A personalized multimodal tourist tour planner

Gavalas

Kasapakis

Konstantopoulos

et al. 2014

Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia

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Tourists become increasingly dependent on mobile city guides to locate tourist services and retrieve information about nearby points of interest (POIs) when visiting unknown destinations. Although several city guides support the provision of personalized tour recommendations to assist tourists visiting the most interesting attractions, existing tour planners only consider walking tours. Herein, we introduce eCOMPASS, a context-aware mobile application which also considers the option of using public transit for moving around. Far beyond than just providing navigational aid, eCOMPASS incorporates multimodality (i.e. time dependency) within its routing logic aiming at deriving nearoptimal sequencing of POIs along recommended tours so as to best utilize time available for sightseeing and minimize waiting time at transit stops. Further advancing the state of the art, eCOMPASS allows users to define arbitrary start/end locations (e.g. the current location of a mobile user) rather than choosing among a fixed set of locations. This paper describes the routing algorithm which comprises the core functionality of eCOMPASS and discusses the implementation details of the mobile application using the metropolitan area of Berlin (Germany) as case study.

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CT-Planner4: Toward a More User-Friendly Interactive Day-Tour Planner

Cited by 32 publications

References 14 publications

The eCOMPASS multimodal tourist tour planner

The eCOMPASS multimodal tourist tour planner

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

A personalized multimodal tourist tour planner

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