Understanding the Tourists’ Spatio-Temporal Behavior Using Open GPS Trajectory Data: A Case Study of Yuanmingyuan Park (Beijing, China)

Hemmington

et al. 2021

IJCHM

131

Purpose Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience. Design/methodology/approach The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”. Findings The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns. Practical implications This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries. Originality/value This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

Section: Artificial Intelligence: the Future Of The Hospitality And Tourism Industriesmentioning

confidence: 99%

Artificial intelligence: a systematic review of methods and applications in hospitality and tourism

Hemmington

et al. 2021

IJCHM

131

“…In this paper, we provide an extensive experimental study to find small groups in pedestrian data. This is an important case in analyzing the throughput in public spaces like shopping malls [38], parks [23,50] and train stations [36], and in detecting suspect behavior in such spaces [10,20]. With the Covid-19 pandemic, the application to identifying possible transmission of a disease has become highly relevant as well.…”

Section: Our Contributionmentioning

confidence: 99%

An experimental evaluation of grouping definitions for moving entities

Wiratma

Kreveld

Löffler

et al. 2022

JOSIS

One important pattern analysis task for trajectory data is to find a group: a set of entities that travel together over a period of time. In this paper, we compare four definitions of groups by conducting extensive experiments using various data sets. The grouping definitions are different by one or more of three different characteristics: whether they use the measured sample points or continuous movement, how distance is used to decide if entities are in the same group, and whether the duration of the group is measured cumulatively or as one contiguous time interval. We are interested in the differences between the definitions and comparisons to human-annotated data, if available. We concentrate on pedestrian data and on different crowd densities. Furthermore, we analyze the robustness of the definitions with respect to their dependence on different sampling rates. We use two types of trajectory data sets: synthetic trajectories and real-life trajectories extracted from video surveillance. We present the results of the quantitative evaluations. For experiments with real-life trajectories, we augment them with a qualitative evaluation using videos that show groups in the trajectories with a color coding.

“…The use of travel digital footprints can accurately and quickly analyze the routes and emotions of tourists, which reveals the behavior law of tourists. Travel digital footprints can be collected from different types of data sources, such as GPS [27], mobile network data [44], geo-labeled photos [45] and User Generated Content data [46,47].…”

Section: B Travel Digital Footprintmentioning

confidence: 99%

Chinese Tourists in Malaysia: An Analysis of Spatio–Temporal Behavior Based on Tourism Digital Footprints

Cao

Teo

2022

IEEE Access

Travel digital footprints can be used to analyze tourists' spatio-temporal behavior to customize travel plans and recommendations. This study focuses on Chinese tourists in Malaysia as the research subject and uses the tourism digital footprint of Chinese tourists' travel texts on Qunar.com as the primary data source. It connects traditional quantitative analysis with complex network analysis to study tourists' behavior, time pattern, and complex network effects. The research results were as follows: (1) there was an uneven distribution of core tourism nodes with structural holes in Malaysia, which form a network pattern of imbalanced power and fierce internal competition; (2) the digital footprints of Chinese tourists were mainly concentrated in traditional tourist hot spots in Malaysia; (3) besides the competition between domestic islands, Thailand and Singapore are Malaysia's main competitors for Chinese tourists. Based on the above research results, this paper proposes the following suggestions: (1) support the emerging or potential tourist destinations vigorously based on consolidating the existing traditional tourist cities; (2) allow core cities to play the full role of actively attracting tourists to transfer to next-level destinations; (3) to conduct digital precision marketing by relying on the tourists' consumption behavior big data; (4) integrate regional resources, refine the tourism market, and develop characteristic tourism products differently.