Personalized Tourist Route Generation
Identifieur interne : 000D17 ( Main/Exploration ); précédent : 000D16; suivant : 000D18Personalized Tourist Route Generation
Auteurs : Ander Garcia [Espagne] ; Olatz Arbelaitz [Espagne] ; Maria Teresa Linaza [Espagne] ; Pieter Vansteenwegen [Belgique] ; Wouter Souffriau [Belgique]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists’ needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time.
Url:
DOI: 10.1007/978-3-642-16985-4_47
Affiliations:
- Belgique, Espagne
- Province du Brabant flamand, Région flamande
- Louvain
- Katholieke Universiteit Leuven
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Le document en format XML
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<front><div type="abstract" xml:lang="en">Abstract: When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist agenda. Finally, they fine tune up this route to better fit tourists’ needs. We present an intelligent routing system to fulfil the same task. We divide this process in three steps: recommendation, route generation and route customization. We focus on the last two steps and analyze them. We model the tourist planning problem, integrating public transportation, as the Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) and we present an heuristic able to solve it on real-time. Finally, we show the prototype which generates and customizes routes in real-time.</div>
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