312 research outputs found

    Crowd-sourced Photographic Content for Urban Recreational Route Planning

    Get PDF
    Routing services are able to provide travel directions for users of all modes of transport. Most of them are focusing on functional journeys (i.e. journeys linking given origin and destination with minimum cost) while paying less attention to recreational trips, in particular leisure walks in an urban context. These walks are additionally predefined by time or distance and as their purpose is the process of walking itself, the attractiveness of areas that are passed by can be an important factor in route selection. This factor is hard to be formalised and requires a reliable source of information, covering the entire street network. Previous research shows that crowd-sourced data available from photo-sharing services has a potential for being a measure of space attractiveness, thus becoming a base for a routing system that suggests leisure walks, and ongoing PhD research aims to build such system. This paper demonstrates findings on four investigated data sources (Flickr, Panoramio, Picasa and Geograph) in Central London and discusses the requirements to the algorithm that is going to be implemented in the second half of this PhD research. Visual analytics was chosen as a method for understanding and comparing obtained datasets that contain hundreds of thousands records. Interactive software was developed to find a number of problems, as well as to estimate the suitability of the sources in general. It was concluded that Picasa and Geograph have problems making them less suitable for further research while Panoramio and Flickr require filtering to remove photographs that do not contribute to understanding of local attractiveness. Based on this analysis a number of filtering methods were proposed in order to improve the quality of datasets and thus provide a more reliable measure to support urban recreational routing

    Theme Park Routing: A Decision Support System for Walt Disney World Trips

    Get PDF
    With over 52 million visitors annually, the Walt Disney World theme park is one of the busiest places on earth (US City Traveler, 2015). While some of these visitors are regular attendees, most are creating new memories in unfamiliar territory. To assist these novice theme park visitors, a plethora of reference books, blogs, tour guides and other resources exist. These recommend which attractions to visit and their popularity. What these resources do not provide, however, is the optimal order that these attractions should be visited

    A Big Data Analytics Method for Tourist Behaviour Analysis

    Get PDF
    © 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

    A Big Data Analytics Method for Tourist Behaviour Analysis

    Get PDF
    © 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

    A big-data analytics method for capturing visitor activities and flows: the case of an island country

    Get PDF
    © 2019, Springer Science+Business Media, LLC, part of Springer Nature. Understanding how people move from one location to another is important both for smart city planners and destination managers. Big-data generated on social media sites have created opportunities for developing evidence-based insights that can be useful for decision-makers. While previous studies have introduced observational data analysis methods for social media data, there remains a need for method development—specifically for capturing people’s movement flows and behavioural details. This paper reports a study outlining a new analytical method, to explore people’s activities, behavioural, and movement details for people monitoring and planning purposes. Our method utilises online geotagged content uploaded by users from various locations. The effectiveness of the proposed method, which combines content capturing, processing and predicting algorithms, is demonstrated through a case study of the Fiji Islands. The results show good performance compared to other relevant methods and show applicability to national decisions and policies

    Learning Points and Routes to Recommend Trajectories

    Full text link
    The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours. Data about previous trajectories are used for learning transition patterns between POIs that enable us to recommend probable routes. In addition, a probabilistic model is proposed to combine the results of POI ranking and the POI to POI transitions. We propose a new F1_1 score on pairs of POIs that capture the order of visits. Empirical results show that our approach improves on recent methods, and demonstrate that combining points and routes enables better trajectory recommendations

    Development of Context-Aware Recommenders of Sequences of Touristic Activities

    Get PDF
    En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lícules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turístiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turístics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turístics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turístiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i períodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implícites entre els punts d'interès de cada perfil. Finalment, es fa un rànquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turística. També realitzem una segona fase d'anàlisi dels fluxos turístics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turística. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turística.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de películas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turísticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turísticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turísticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turísticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minería de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turística. También llevamos a cabo un análisis de los flujos turísticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turística. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turística.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems
    • …
    corecore