3,762 research outputs found
"Applications of Intelligent Systems in Tourism: Relevant Methods"
"This article presents a literature review of Intelligent Systems applications in Tourism in different
parts of the world. The review focuses on the most relevant methods in free and paid databases, in
English and Spanish. Most of the works deal with methodologies based on artificial intelligence,
such as expert systems, fuzzy logic, machine learning, data mining, neural networks, genetic
algorithms. In the review, several characteristics of the systems have been taken into account, such
as, knowledge base, actors in the operation of the system, types of tourists, usefulness or not in space
and time. According to the review it was found that most of the researches are deterministic models,
the most used methodology in tourism are the expert systems based on rules, observing an emerging
innovation in metaheuristics, mainly genetic algorithms and intelligent systems with knowledge
base based on optimization methods for route choice or optimal visit plan. It is expected that this
work serves to give a general perspective on the application of intelligent systems in the area of
tourism, as well as a work that consolidates background for work in this area of research.
All roads lead to the places of your interest: An on-demand, ride-sharing visitor transport service
Successful visitor transport within large tourist sites should balance visitor experience and operating costs. Inspired by the model of sharing economy, we design a “user-centered” intelligent visitor transport system to improve the efficiency and quality of experience of transport service in large tourist sites. The system’s core approach is a three-stage heuristic model based on Pareto optimality. Results of the proposed service indicate a drastic reduction of visitor delay time and an improvement in energy efficiency. The proposed scheduling schemes for organizers are more diversified and adaptable than the existing service
Top-k Route Search through Submodularity Modeling of Recurrent POI Features
We consider a practical top-k route search problem: given a collection of
points of interest (POIs) with rated features and traveling costs between POIs,
a user wants to find k routes from a source to a destination and limited in a
cost budget, that maximally match her needs on feature preferences. One
challenge is dealing with the personalized diversity requirement where users
have various trade-off between quantity (the number of POIs with a specified
feature) and variety (the coverage of specified features). Another challenge is
the large scale of the POI map and the great many alternative routes to search.
We model the personalized diversity requirement by the whole class of
submodular functions, and present an optimal solution to the top-k route search
problem through indices for retrieving relevant POIs in both feature and route
spaces and various strategies for pruning the search space using user
preferences and constraints. We also present promising heuristic solutions and
evaluate all the solutions on real life data.Comment: 11 pages, 7 figures, 2 table
Smart Cities: An In-Depth Study of AI Algorithms and Advanced Connectivity
The goal of smart city development is to improve the quality of life by incorporating technology into daily activities. Artificial intelligence (AI) is critical to the ongoing development of future smart cities. The Internet of Things (IoT) idea connects every internet-enabled device for improved access and control. AI in various domains has changed ordinary towns into highly equipped smart cities. Machine learning and deep learning algorithms have proven indispensable in a variety of industries, and they are now being implemented into smart city concepts to automate and improve urban activities and operations on a large scale. IoT and machine learning technology are frequently used in smart cities to collect data from various sources. This article delves deeply into the significance, scope, and developments of AI-based smart cities. It also addresses some of the difficulties and restrictions associated with smart cities powered by AI. The goal of the study is to inspire and encourage academics to create original smart city solutions based on AI technologies
Study of Optimization of Tourists' Travel Paths by Several Algorithms
The purpose of this paper is to optimize the tourism path to make the distance shorter. The article first constructed a model for tourism route planning and then used particle swarm optimization (PSO), genetic algorithm (GA), and ant colony algorithms to solve the model separately. Finally, a simulation experiment was conducted on tourist attractions in the suburbs of Taiyuan City to compare the path optimization performance of the three algorithms. The three path optimization algorithms all converged during the process of finding the optimal path. Among them, the ant colony algorithm exhibited the fastest and most stable convergence, resulting in the smallest model fitness value. The travel route obtained through the ant colony algorithm had the shortest distance, and this algorithm required minimal time for optimization. The novelty of this article lies in the enumeration and description of various algorithms used for optimizing travel paths, as well as the comparison of three different travel route optimization algorithms through simulation experiments. Doi: 10.28991/HIJ-2023-04-02-012 Full Text: PD
Tourism Decision Making System & Auto Guidance Technique using Data analytics
A unique Tourism Decision Making System TDMS) describes and evaluates the evaluation of research and developments in information technology meant for pronouncement sustain as well as examination during the sector of visiting the attractions. Individuals in the tourism sector are classified according to their decision-making technologies. The current trends and growth directions of choice help technologies were analysed for visitors from various advertising categories. The potential to provide customising, augmentation, and help for visitors at all phases of their trips by integrating modern automated approaches with GIS capabilities demonstrates the need for breakthroughs in digital advanced analytics
An Intelligent Customization Framework for Tourist Trip Design Problems
In the era of the experience economy, “customized tours” and “self-guided tours” have become mainstream. This paper proposes an end-to-end framework for solving the tourist trip design problems (TTDP) using deep reinforcement learning (DRL) and data analysis. The proposed approach considers heterogeneous tourist preferences, customized requirements, and stochastic traffic times in real applications. With various heuristics methods, our approach is scalable without retraining for every new problem instance, which can automatically adapt the solution when the problem constraint changes slightly. We aim to provide websites or users with software tools that make it easier to solve TTDP, promoting the development of smart tourism and customized tourism
A decision support system for the management of smart mobility services
Master Dissertation (Master Degree in Engineering and Management of Information Systems)Nos dias que correm, a mobilidade assume especial importância no quotidiano das áreas
metropolitanas em crescimento no país. . Com o notório crescimento das cidades, torna-se
necessária e urgente uma transformação dos costumes e formas de mobilidade dentro das
áreas urbanas, alterando as realidades aparentes que hoje conhecemos. Inseridos numa
sociedade cada vez mais consciencializada e alerta para as questões ambientais, é essencial
transportar esta mentalidade renovada para a resolução das problemáticas citadinas. Assim,
o conceito de “Cidade Verde” levanta uma série de questões que exigem uma resposta eficaz
para o bem-estar dos seus habitantes.
Por entre as várias soluções apresentadas para estas patologias, uma das mais promissoras é, sem dúvida, o sistema de mobilidade partilhada. Pela sua dimensão, é pertinente
expor o caso prático da cidade de Barcelona, em Espanha, explorando o seu sistema de
partilha de scooters, um meio que adquire especial importância como meio de transporte
urbano. Como qualquer sistema em constante aprimoramento, procura-se uma solução
para a problemática da variação de procura, que apresenta oscilações constantes, tanto a
nível temporal como geográfico, resultando na falta de veículos em algumas áreas e excesso
noutras. Assim sendo, o rebalanceamento do sistema torna-se crucial para uma possível
maximização na utilização de veículos, satisfazendo a procura e potenciando um aumento
da sua utilização.
No correr desta dissertação, foram estudados e utilizados vários métodos de otimização
moderna (metaheurísticas) para a procura de soluções (sub)ótimas para o(s) percurso(s)
a percorrer pelo(s) veículo(s) que executam a redistribuição das scooter/bicicletas pelas
diversas áreas abrangidas pelo sistema de partilha. Deste modo, foi desenvolvido um
sistema de apoio à decisão para satisfazer estas necessidades, garantindo ao utilizador toda
a informação relevante para um trabalho mais eficiente e preciso.Nowadays, mobility is especially important in the daily life of the country growing metropolitan areas. With the increasing influx of people and development of these large cities, the
reality of mobility that we know becomes increasingly unsustainable. Along with mobility, the environmental concerns are one of the main topics of discussion worldwide and
the population is starting to act and change the way they live to find a more “green” and
sustainable way of doing it.
Several proposals have been put forward, trying to mitigate this issue and, one of the
most promising is, undoubtedly, shared mobility systems. In this case study will be addressed the Barcelona scooter sharing system, characterized by its great size and importance
as a mean of urban transport. One of the problems presented by these sharing services is
that demand varies widely, both temporal and geographical. Thus, there are several cases
where there is a lack of vehicles in some areas and an excess in others. The rebalancing of
the system is crucial to maximize vehicle utilization and meet customer demand.
In this thesis, several modern optimization methods (metaheuristics) were used to search
for (sub)optimal solutions for the redistribution route(s). A decision support system was
developed to meet this end, giving the end user relevant information for a more efficient
and precise work
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