A Multi-graph Convolutional Network Framework for Tourist Flow Prediction

2021 ◽  
Vol 21 (4) ◽  
pp. 1-13
Author(s):  
Wei Wang ◽  
Junyang Chen ◽  
Yushu Zhang ◽  
Zhiguo Gong ◽  
Neeraj Kumar ◽  
...  
Keyword(s):  
Data Science ◽  
Service Providers ◽  
Tourism Industry ◽  
Convolutional Network ◽  
Flow Prediction ◽  
Challenges And Opportunities ◽  
Real World Datasets ◽  
Relation Graph ◽  
Smart Tourism ◽  
Tourist Flow

With the advancement of Cyber Physic Systems and Social Internet of Things, the tourism industry is facing challenges and opportunities. We can now able to collect, store, and analyze large amounts of travel data. With the help of data science and artificial intelligence, smart tourism enables tourists with great autonomy and convenience for an intelligent trip. It is of great significance to make full use of these massive data to provide better services for smart tourism. However, due to the skewed and imbalanced visiting for point of interest located at different places, it is of great significance to predict the tourist flow of each place, which can help the service providers for designing a better schedule visiting strategy in advance. Against this background, this article proposes a multi-graph convolutional network framework, named AMOUNT, for tourist flow prediction. To capture the diverse relationships among POIs, AMOUNT first constructs three subgraphs, including the geographical graph, interaction graph, and the co-relation graph. Then, a multi-graph convolution network is utilized to predict the future tourist flow. Experimental results on two real-world datasets indicate that the proposed AMOUNT model outperforms all other baseline tourist flow prediction approaches.

scholarly journals FEATURES OF FORMING A TOURIST DESTINATION AS AN ELEMENT OF A SMART CITY

2020 ◽  
pp. 3-10
Author(s):  
Olha Lyubitseva ◽  
Natalyа Bielousova ◽  
Olha Skorostetska
Keyword(s):  
Smart City ◽  
Service Providers ◽  
Tourism Industry ◽  
Educational Process ◽  
Capital City ◽  
Practical Significance ◽  
Tourist Destination ◽  
Tourist Destinations ◽  
Tourism Sector ◽  
Smart Tourism

Purpose. Consider the problems of a tourist destination on the example of a capital city with an emphasis on the structure, mechanism, basic elements and stages of high-quality tourist services, in the context of the formation of the destination "Kiev" as an element of a modern smart city. Methodology. Analytical, statistical, graphic, systemic and comparative geographical methods were used. Approbation. The main conceptual issues of the formation of tourist destinations and the problems of their accompanying components were studied by domestic scientists(M. Boyko, Y. Zabaldina, A. Mazaraki, S. Melnichenko, I. Smirnov, T. Tkachenko and others) and were tested in the previous works of the authors of this article. Scientific novelty. The communication relationship of tourism service providers with their consumers is dictated by the availability of modern technologies, approaches and methods of forming tourist destinations as components of the smart tourism model. Today, the available theoretical, methodological and practical studies of smart tourism and smart destinations are controversial, given the fact that the scientific literature has not yet formed clearly theoretical constructions that would make it possible to formulate the main components of relationships in a smart destination. In this article, the authors have proposed an algorithm for the formation and development of the capital's smart city, using the example of the Kiev destination. Practical significance. The article raises controversial issues related to the realities of modern life of people in the framework of innovative approaches to the activities of the tourism sector in Ukraine, with its problems, challenges and practical solutions. The publication materials can be used in the educational process in higher educational institutions that train specialists for the Ukrainian tourism industry, the practical activities of travel operators and agencies, in the process of work of departments and professional institutions, relate to the tourism system of providing services or the processes of studying the issues of the introduction of innovative technologies into the tourism sector of Ukraine.

scholarly journals Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Dazhou Li ◽  
Chuan Lin ◽  
Wei Gao ◽  
Zeying Chen ◽  
Zeshen Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Geographical Location ◽  
Urban Traffic ◽  
Basic Unit ◽  
Urban Computing ◽  
Convolutional Network ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Real World Datasets

Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.

3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction

2021 ◽  
Vol 15 (6) ◽  
pp. 1-21
Author(s):  
Tong Xia ◽  
Junjie Lin ◽  
Yong Li ◽  
Jie Feng ◽  
Pan Hui ◽  
...  
Keyword(s):  
Time Interval ◽  
Urban Structure ◽  
Dynamic Graph ◽  
Temporal Dependence ◽  
Convolutional Network ◽  
Urban Structures ◽  
Flow Prediction ◽  
Wide Range ◽  
Spatio Temporal ◽  
Real World Datasets

Crowd flow prediction is an essential task benefiting a wide range of applications for the transportation system and public safety. However, it is a challenging problem due to the complex spatio-temporal dependence and the complicated impact of urban structure on the crowd flow patterns. In this article, we propose a novel framework, 3- D imensional G raph C onvolution N etwork (3DGCN), to predict citywide crowd flow. We first model it as a dynamic spatio-temporal graph prediction problem, where each node represents a region with time-varying flows, and each edge represents the origin–destination (OD) flow between its corresponding regions. As such, OD flows among regions are treated as a proxy for the spatial interactions among regions. To tackle the complex spatio-temporal dependence, our proposed 3DGCN can model the correlation among graph spatial and temporal neighbors simultaneously. To learn and incorporate urban structures in crowd flow prediction, we design the GCN aggregator to be learned from both crowd flow prediction and region function inference at the same time. Extensive experiments with real-world datasets in two cities demonstrate that our model outperforms state-of-the-art baselines by 9.6%∼19.5% for the next-time-interval prediction.

scholarly journals Okos turizmus és okos rendezvények vizsgálata a Székesfehérvári Királyi Napok tükrében : Smart tourism and smart events reflected by the Székesfehérvár Royal Days

2021 ◽  
Vol 21 (2) ◽  
pp. 34-42
Author(s):  
Ivett Pinke-Sziva ◽  
Krisztina Keller
Keyword(s):  
Present Article ◽  
Service Providers ◽  
Research Question ◽  
Tourism Industry ◽  
Supply Side ◽  
Expert Interviews ◽  
Demand Side ◽  
Management Organization ◽  
The Impact ◽  
Smart Tourism

A digitalizáció hatása a turisztikai iparágra elvitathatlan mind a keresleti, mind a kínálati oldalt tekintve. Jellemzően a szolgáltatások foglalása, az attrakciók interpretációja kerül az online és digitális megoldások középpontjába, ugyanakkor a rendezvények esetében mindezen megoldások éppúgy szerepet kaphatnak. Az is látható, hogy a nagyvárosok életében népszerűek az okos turisztikai applikációk, de vajon mi a helyzet a kisebb hazai városokkal, a vidéki desztinációkkal? Mindezen témák vizsgálata áll jelen cikkünk középpontjában, az alábbi kutatási kérdésben összegezve: Mit jelenthet az okos turizmus Székesfehérvár esetében, és hogyan jellemezhető az okos rendezvény iránti kereslet a Királyi Napok tekintetében? A kérdések vizsgálatához egyrészt 13 szolgáltatóval készítettünk kvalitatív szakértői mélyinterjút, másrészt keresleti oldali kutatást végeztünk a helyi Turisztikai Desztináció Menedzsment Szervezet (TDMSZ) segítségével, mely során kérdőíves megkérdezésünkre 414 értékelhető választ kaptunk a 2019. évi Királyi Napok látogatóinak köréből. The impact of digitalisation on the tourism industry is indisputable on both the demand and the supply side. Typically, the booking of services and the interpretation of attractions have become the focus of online and digital solutions, but in the case of events, all of these solutions can play a role. It can also be seen that, in the life of larger cities, smart tourism applications are popular, but does this apply to smaller domestic cities and countryside destinations? The examination of all these topics is the focus of our present article, addressing the following research question: What can smart tourism mean in the case of Székesfehérvár and how can the demand for a smart event be characterized in terms of the Royal Days? To examine the questions, we conducted qualitative in-depth expert interviews with 13 key service providers, as well as demand-side research using a questionnaire with the help of the local Destination Management Organization (DMO), reaching 414 meaningful answers among visitors at the 2019 Royal Days.

scholarly journals Forecasting of Short-Term Daily Tourist Flow Based on Seasonal Clustering Method and PSO-LSSVM

2020 ◽  
Vol 9 (11) ◽  
pp. 676
Author(s):  
Keqing Li ◽  
Changyong Liang ◽  
Wenxing Lu ◽  
Chu Li ◽  
Shuping Zhao ◽  
...  
Keyword(s):  
Tourism Industry ◽  
Support Vector ◽  
Tourist Attractions ◽  
Flow Prediction ◽  
Model Combining ◽  
Proposed Model ◽  
Forecasting Method ◽  
Optimized Model ◽  
Daily Changes ◽  
Tourist Flow

The accurate prediction of tourist flow is essential to appropriately prepare tourist attractions and inform the decisions of tourism companies. However, tourist flow in scenic spots is a dynamic trend with daily changes, and specialized methods are necessary to measure it accurately. For this purpose, a tourist flow forecasting method is proposed in this research based on seasonal clustering. The experiment employs the K-means algorithm considering seasonal variations and the particle swarm optimization-least squares support vector machine (PSO-LSSVM) algorithm to forecast the tourist flow in scenic spots. The LSSVM is also used to compare the performance of the proposed model with that of the existing ones. Experiments based on a dataset comprising the daily tourist data for Mountain Huangshan during the period between 2014 and 2017 are conducted. Our results show that seasonal clustering is an effective method to improve tourist flow prediction, besides, the accuracy of daily tourist flow prediction is significantly improved by nearly 3 percent based on the hybrid optimized model combining seasonal clustering. Compared with other algorithms which provide predictions at monthly intervals, the method proposed in this research can provide more timely analysis and guide professionals in the tourism industry towards better daily management.

Challenges and Opportunities for Data Science and Machine Learning in IoT Systems - A Timely Debate: Part 2

2020 ◽  
pp. 2-6
Author(s):  
Sumi Helal ◽  
Flavia C. Delicato ◽  
Cintia B. Margi ◽  
Satyajayant Misra ◽  
Markus Endler
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Challenges And Opportunities

scholarly journals Wealth of Wind and Visitors: Tourist Industry Attitudes towards Wind Energy Development in Iceland

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 693
Author(s):  
Anna Dóra Sæþórsdóttir ◽  
Margrét Wendt ◽  
Edita Tverijonaite
Keyword(s):  
Land Use ◽  
Wind Energy ◽  
Wind Turbines ◽  
Wind Farm ◽  
Service Providers ◽  
Wind Farms ◽  
Energy Development ◽  
Tourism Industry ◽  
Land Use Conflicts ◽  
Wind Energy Development

The interest in harnessing wind energy keeps increasing globally. Iceland is considering building its first wind farms, but its landscape and nature are not only a resource for renewable energy production; they are also the main attraction for tourists. As wind turbines affect how the landscape is perceived and experienced, it is foreseeable that the construction of wind farms in Iceland will create land use conflicts between the energy sector and the tourism industry. This study sheds light on the impacts of wind farms on nature-based tourism as perceived by the tourism industry. Based on 47 semi-structured interviews with tourism service providers, it revealed that the impacts were perceived as mostly negative, since wind farms decrease the quality of the natural landscape. Furthermore, the study identified that the tourism industry considered the following as key factors for selecting suitable wind farm sites: the visibility of wind turbines, the number of tourists and tourist attractions in the area, the area’s degree of naturalness and the local need for energy. The research highlights the importance of analysing the various stakeholders’ opinions with the aim of mitigating land use conflicts and socioeconomic issues related to wind energy development.

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

2021 ◽  
Vol 17 (3) ◽  
pp. 1-23
Author(s):  
Shengsheng Qian ◽  
Jun Hu ◽  
Quan Fang ◽  
Changsheng Xu
Keyword(s):  
Social Media ◽  
Visual Information ◽  
Representation Learning ◽  
Fake News ◽  
Unified Framework ◽  
Model Learning ◽  
Convolutional Network ◽  
Textual Information ◽  
Convolutional Networks ◽  
Real World Datasets

In this article, we focus on fake news detection task and aim to automatically identify the fake news from vast amount of social media posts. To date, many approaches have been proposed to detect fake news, which includes traditional learning methods and deep learning-based models. However, there are three existing challenges: (i) How to represent social media posts effectively, since the post content is various and highly complicated; (ii) how to propose a data-driven method to increase the flexibility of the model to deal with the samples in different contexts and news backgrounds; and (iii) how to fully utilize the additional auxiliary information (the background knowledge and multi-modal information) of posts for better representation learning. To tackle the above challenges, we propose a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) to capture the semantic representations by jointly modeling the textual information, knowledge concepts, and visual information into a unified framework for fake news detection. We model posts as graphs and use a knowledge-aware multi-modal adaptive graph learning principal for the effective feature learning. Compared with existing methods, the proposed KMAGCN addresses challenges from three aspects: (1) It models posts as graphs to capture the non-consecutive and long-range semantic relations; (2) it proposes a novel adaptive graph convolutional network to handle the variability of graph data; and (3) it leverages textual information, knowledge concepts and visual information jointly for model learning. We have conducted extensive experiments on three public real-world datasets and superior results demonstrate the effectiveness of KMAGCN compared with other state-of-the-art algorithms.

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