Tourists’ spatial-temporal behavior patterns in theme parks: A case study of Ocean Park Hong Kong

Huang, Xiaoting; Li, Minxuan; Zhang, Jingru; Zhang, Linlin; Zhang, Haiping; Shen, Yupeng

doi:10.1016/j.jdmm.2020.100411

Cited by 55 publications

(44 citation statements)

References 51 publications

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“…Iterative framework based on the learn heuristic solution, local search and artificial neural network (ANN) [14] Tourist sequential pattern analysis for pedestrian-and car-based trips POI route construction Convolutional neural network (CNN), LSTM neural network [15] Locally optimal tourist route construction POI route construction Shortest tree path construction [16] Temporal-spatial tourist behaviour analysis on micro-scale distances based on Global Positioning System (GPS) data…”

Section: Papermentioning

confidence: 99%

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Car Tourist Trajectory Prediction Based on Bidirectional LSTM Neural Network

Mikhailov

Kashevnik

2021

Electronics

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COVID-19 has greatly affected the tourist industry and ways of travel. According to the UNTWO predictions, the number of international tourist arrivals will be slowly growing by the end of 2021. One of the ways to keep tourists safe during travel is to use a personal car or car-sharing service. The sensor-based information collected from the tourist’s smartphone during the trip allows his/her behaviour analysis. For this purpose, we propose to use the Internet of Things with ambient intelligence technologies, which allows information processing using the surrounding devices. The paper describes a solution to the car tourist trajectory prediction, which has been the demanding subject of different research studies in recent years. We present an approach based on the usage of the bidirectional LSTM neural network model. We show the reference model of the tourist support system for car-based attraction-visiting trips. The sensor data acquisition process and the bidirectional LSTM model construction, training and evaluation are demonstrated. We propose a system architecture that uses the tourist’s smartphone for data acquisition as well as more powerful surrounding devices for information processing. The obtained results can be used for tourist trip behaviour analysis.

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Section: Papermentioning

confidence: 99%

“…The reviewed papers solve different machine learning tasks such as classification [19,20], clustering [16], event prediction [11][12][13]17], POI route contraction/trajectory prediction [14,15] and trajectory prediction [18,21,22]. To solve the assigned tasks, the presented works use various neural networks such as LSTM or CNNs.…”

Section: Papermentioning

confidence: 99%

Car Tourist Trajectory Prediction Based on Bidirectional LSTM Neural Network

Mikhailov

Kashevnik

2021

Electronics

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“…According to previous studies (Geng et al, 2020;Lades et al, 2020), the importance of outdoor activities in gardens, parks and other natural areas increased significantly during the early stages of Covid-19 pandemic and was associated with a positive effect to the physical and mental health and well-being of individuals. Therefore, it is not surprising that residents of smaller towns and rural areas with more open access to the leisure opportunities possibly are psychologically coping with the pandemic to a better degree than residents of more densely populated regions and big cities (Sturman, 2020;Zhang et al, 2020). In Riga and its suburban area, where approximately half of the country's population is concentrated (Apsite-Berina et al 2020), access to natural objects and outdoor activities are more limited compared to less densely populated areas.…”

Section: Rq2: To What Extent Covid-19 Has Affected Aspects Of Individuals' Daily Life Patterns and Remote Work?mentioning

confidence: 99%

International scientific conference "New Challenges in Economic and Business Development – 2021: Post-Crisis Economy" : Proceedings

2021

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Global pandemic COVID-19 has increased the level of digitalization which allows public and private sector organizations in the world to employ people remotely outside office premises and crossing borders of the world. Remote work is one of the new employment forms caused by the impact of digitalization, which keeps conquering and strengthening the positions on our daily professional lives.It means extended use of different new employment forms, including the digital transition of administration processes and business management, improvement of digital skills and competences, contributing to development of areas of services and products with higher benefit (Breaugh, Farabee, 2012). Research aims to study basic principles and tendencies of remote work organization based on theoretical aspects, draw conclusions and elaborate proposals for improvement of remote work. In order to achieve the goal, the tasks are as follows: 1) provide the explanation of remote work organization; 2) describe secondary data from a conducted survey by Milasi, S., Fernandez -Macias, E., Gonzalez-Vazquez, I. 2020, European Commission; 3) conduct survey about remote work.

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“…The authors of Reference [32] aim to integrate multiple data sources to analyze tourists' spatial-temporal behaviour patterns on micro scale distances.…”

Section: Related Workmentioning

confidence: 99%

“…Using behaviour analysis methods such as classification, clustering and prediction of temporal events. The authors of References [15,18,19,27,28] use the classification, References [18,[23][24][25]32] work with clustering and the authors of References [16,17,21,26,29] use the prediction of temporal events to predict the behaviour tourists.…”

mentioning

confidence: 99%

Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study

Mikhailov

Kashevnik

2020

Future Internet

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The tourism industry has been rapidly growing over the last years and IT technologies have had a great affect on tourists as well. Tourist behaviour analysis has been the subject of different research studies in recent years. This paper presents the digital pattern of life concept which simplifies the tourist behaviour models’ construction and usage. The digital pattern of life defines the general concepts of tourist behaviour, connects the tourist and the digital world and allows us to track behaviour changes over time. A literature review of the current state of the research in selected fields is performed for identifying the existing problems. The case studies of behaviour analysis based on classification, clustering and time series events behaviour models are shown. An ontological approach and artificial neural networks are used during behaviour model construction, training and evaluation. The gathered results can be used by smart tourism service developers and business stakeholders.

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Tourists’ spatial-temporal behavior patterns in theme parks: A case study of Ocean Park Hong Kong

Cited by 55 publications

References 51 publications

Car Tourist Trajectory Prediction Based on Bidirectional LSTM Neural Network

Car Tourist Trajectory Prediction Based on Bidirectional LSTM Neural Network

International scientific conference "New Challenges in Economic and Business Development – 2021: Post-Crisis Economy" : Proceedings

Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study

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