An Ontology for Service Semantic Interoperability in the Smartphone-Based Tourist Trip Planning System

This paper presents an ontology-based approach for semantic interoperability tourist trip planning services. The proposed ontology describes a tourist, an attraction route and context information about tourist and his/her environment. This ontology is developed within the Tourist Trip Planning System, which consists of a set of interacting services. All services work accordingly to the proposed ontology which leads to service semantic interoperability and allows to increase interaction speed between them

Visiting Time Prediction Using Machine Learning Regression Algorithm

Smart tourists cannot be separated with mobile technology. With the gadget, tourist can find information about the destination, or supporting information like transportation, hotel, weather and exchange rate. They need prediction of traveling and visiting time, to arrange their journey. If traveling time has predicted accurately by Google Map using the location feature, visiting time has another issue. Until today, Google detects the user’s position based on crowdsourcing data from customer visits to a specific location over the last several weeks. It cannot be denied that this method will give a valid information for the tourists. However, because it needs a lot of data, there are many destinations that have no information about visiting time. From the case study that we used, there are 626 destinations in East Java, Indonesia, and from that amount only 224 destinations or 35.78% has the visiting time. To complete the information and help tourists, this research developed the prediction model for visiting time. For the first data is tested statistically to make sure the model development was using the right method. Multiple linear regression become the common model, because there are six factors that influenced the visiting time, i.e. access, government, rating, number of reviews, number of pictures, and other information. Those factors become the independent variables to predict dependent variable or visiting time. From normality test as the linear regression requirement, the significant value was less than p that means the data cannot pass the statistic test, even though we transformed the data based on the skewness. Because of three of them are ordinal data and the others are interval data, we tried to exclude and include the ordinal by transform it to interval. We also used the Ordinal Logistic Regression by transform the interval data in dependent variable into ordinal data using Expectation Maximization, one of clustering algorithm in machine learning, but the model still did not fit even though we used 5 functions. Then we used the classification algorithm in machine learning by using 5 top algorithm which are Linear Regression, k-Nearest Neighbors, Decision Tree, Support Vector Machines, and Multi-Layer Perceptron. Based on maximum correlation coefficient and minimum root mean square error, Linear Regression with 6 independent variables has the best result with the correlation coefficient 20.41% and root mean square error 48.46%. We also compared with model using 3 independent variable, the best algorithm was still the same but with less performance. Then, the model was loaded to predict the visiting time for other 402 destinations

USABILITY STUDY FOR TOURIST MOBILE SYSTEMS RECOMMENDATION

Tourist recommendation system is a system that provides information and recommendations for tourists. This system will help users to reduce the search process and help to make choices. Currently, the recommendation system uses the internet because it is cheap, requires a short time, can be accessed from anywhere and always update. The tourism recommendation system works by using the restrictions and preferences of tourists, then it will recommend tourism travel routes. Recommendation systems can be grouped into four types which are content-based, collaborative-filtering, knowledge-based, and hybrid systems that combine two or more methods. Usability is a product condition that can be used specifically by users to achieve effectiveness, efficiency, and satisfaction in the context of use. Usability test is conducted to see the level of comfort and ease of use of a system recommendation. In general, there are two types of measurements, which are quantitative and qualitative measurements. Quantitatively is the level of completion, level of success, processing time, level of satisfaction and level of error. The SUS (System Usability Scale) will measure user satisfaction by software, hardware and mobile equipment and consisting of a scale that is easy and simple to respondents and makes it ideal for use with a small sample size. The metrics are completion rates, usability problems, task time, task level satisfaction, test level satisfaction, errors, expectations, page views/clicks, and conversions. This research will compare two existing tourist recommendation systems using SUS. The information recommendation that must be fulfilled are level of fulfillment (100%), usability (64%), and level of understanding (64%). Respondents said they need system recommendation that does not need special expertise to operate it (75%) and the function is well integrated (71%)

Solving Multi Objectives Team Orienteering Problem with Time Windows using Multi Integer Linear Programming

This study solves tourist trip planning using team orienteering problem with time windows with more than one objective. In MO-TOPTW, besides maximum score, there is minimum time that must be achieved to make sure tourist get effective and efficient routing. Score represent priority to visit the destinations, while time consist of visiting time and traveling time between destinations. Number of routing is determined and the goal is giving the tourist the best routing that fulfill all the constraints. The constraints are time windows and tourist’s budget time. Modification of mathematical programming will be done. We used small case to compare between heuristic procedure to develop the route with optimization. Optimization is implemented using Multi Integer Linear Programming using Lingo. The global optimum of optimization method gives better result than heuristic, with total score higher as 12% and total time lower 7.3%. Because this is NP-hard problem, the running time is 45 minutes 24 seconds, very long time for tourist to wait the result. Further research must be done to faster the process with preserving the best result

Improving Itinerary Recommendations for Tourists Through Metaheuristic Algorithms: An Optimization Proposal

In recent years, recommender systems have been used as a solution to support tourists with recommendations oriented to maximize the entertainment value of visiting a tourist destination. However, this is not an easy task because many aspects need to be considered to make realistic recommendations: the context of a tourist destination visited, lack of updated information about points of interest, transport information, weather forecast, etc. The recommendations concerning a tourist destination must be linked to the interests and constraints of the tourist. In this research, we present a mobile recommender system based on Tourist Trip Design Problem (TTDP)/Time Depending (TD) – Orienteering Problem (OP) – Time Windows (TW), which analyzes in real time the user’s constraints and the points of interest’s constraints. For solving TTDP, we clustered preferences depending on the number of days that a tourist will visit a tourist destination using a k-means algorithm. Then, with a genetic algorithm (GA), we optimize the proposed itineraries to tourists for facilitating the organization of their visits. We also used a parametrized fitness function to include any element of the context to generate an optimized recommendation. Our recommender is different from others because it is scalable and adaptable to environmental changes and users’ interests, and it offers real-time recommendations. To test our recommender, we developed an application that uses our algorithm. Finally, 131 tourists used this recommender system and an analysis of users’ perceptions was developed. Metrics were also used to detect the percentage of precision, in order to determine the degree of accuracy of the recommender system. This study has implications for researchers interested in developing software to recommend the best itinerary for tourists with constraint controls with regard to the optimized itineraries

Aplicación web para la creación de itinerarios turísticos personalizados

El turismo es de los sectores más importantes de la sociedad actual, siendo millones el número de personas que se desplazan de un lugar a otro en los distintos medios de transporte, como aviones o barcos. Los turistas quieren llevarse de su viajes experiencias insuperables, visitando los mejores lugares de la mejor manera posible, siempre ajustándose a sus preferencias y limitaciones. Es decir, quieren tener el mejor itinerario turístico posible. La elaboración de estos itinerarios puede causar problemas, debido al gran bombardeo de información, ofertas y promociones que hay en internet. Por lo que hay poca probabilidad de que se realice una valoración completa de esta. Para evadir este problema, los turistas suelen acudir a empresas físicas como agencias de viajes o utilizar aplicaciones informáticas que preparen sus itinerarios. Con este trabajo de fin de grado se quiere resolver el Tourist Trip Design Problem (TTDP) a través de un sistema de recomendación que, valorando distintos criterios sea capaz de generar un itinerario turístico personalizado, con un conjunto de visitas ordenadas en el tiempo a distintos puntos de interés. Este trabajo se basa en una aplicación full stack que dispone de un sistema generador de itinerarios turísticos que trata el problema de diseño de rutas turísticas, con el fin de generar itinerarios turísticos personalizados. En esta aplicación se dispone de una interfaz web donde se pueden registrar puntos de interés, turistas y sus preferencias de viajes. Para cada conjunto de preferencias acerca de un viaje, el sistema a través de un algoritmo de recomendación basado en una técnica heurística, es capaz de crear itinerarios personalizados teniendo en cuenta diversos criterios. El aplicativo cuenta con distintas partes, entre ellas destacan el front-end, que cuenta con las vistas y componentes necesarios para que la visualización sea posible. Y por otro lado, destaca el back-end, el cual se basa en una API REST que cuenta con distintos endpoints con los que se gestionan las peticiones HTTP para que se pueda dar el flujo de datos. Una vez terminada la aplicación, han sido realizadas numerosas experimentaciones en las que se han contemplado diferentes escenarios para, de esta manera, poder comprobar que el aplicativo funciona como se espera.Tourism is one of the most important sectors in today's society, with millions of people travelling from one place to another by various means of transport, such as planes or ships. Tourists want to take away unsurpassed experiences from their trips, visiting the best places in the best possible way, always adjusting to their preferences and limitations. In other words, they want to have the best possible tourist itinerary. The elaboration of these itineraries can cause problems, due to the great bombardment of information, offers and promotions on the internet. As a result, there is little likelihood that a full assessment will be made. To circumvent this problem, tourists often turn to physical companies such as travel agencies or use software applications to prepare their itineraries. The aim of this final degree project is to solve the Tourist Trip Design Problem (TTDP) by means of a recommendation system that, taking into account different criteria, is capable of generating a personalised tourist itinerary, with a set of visits ordered in time to different points of interest. This work is based on a full stack application that has a tourist itinerary generator system that deals with the problem of designing tourist routes, in order to generate personalised tourist itineraries. This application has a web interface where points of interest, tourists and their travel preferences can be registered. For each set of preferences about a trip, the system, through a recommendation algorithm based on a heuristic technique, is able to create personalised itineraries taking into account various criteria. The application has different parts, including the front-end, which has the necessary views and components to make visualisation possible. And on the other hand, the back-end, which is based on a REST API that has different endpoints with which HTTP requests are managed so that the data flow can take place. Once the application has been completed, numerous experiments have been carried out in which different scenarios have been considered. In this way, we were able to check that the application works as expected

Orienteering Problem: A survey of recent variants, solution approaches and applications

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ