Explore for a day? Generating personalized itineraries that fit spatial heterogeneity of tourist attractions

Ji, Haipeng; Zheng, Weimin; Zhuang, Xinyi; Lin, Zhibin

doi:10.1016/j.im.2021.103557

Cited by 18 publications

(5 citation statements)

References 42 publications

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“…Liao and Zheng propose designing day trip routes in a time-dependent stochastic environment that can design more realistic and personalized routes for tourists [30]. Ji et al not only considered tourists' preferences but also the heterogeneity of the spatial structure of attractions to optimize the tourist experience and help tourists travel more enjoyably [31]. Zheng et al proposed a multi-objective evolutionary algorithm for tourism route recommendation based on the decomposition of two-stage and Pareto hierarchies, which recommends better distribution and diversity of tourist routes, so that the recommended routes can better meet the personalized needs of tourists [32].…”

Section: B Travel Recommendationmentioning

confidence: 99%

“…Equation (15) indicates that the objective function combines three aspects of location hotness, favorability, and the degree of satisfying visitor demand; equation (16) and equation ( 17) specify the start and end points of the route; equation (18) indicates that each location can be selected at most once; equation (19) specifies the connectivity of attractions and restaurants within the same day; equation (20) indicates that the previous day must end with a hotel and the next day begins with a hotel, specifying hotel connectivity; equation (21) indicates that there can be no returning streams; equation (22) indicates that there can be no subloops; equation (23) specifies that one must travel from one point to another; equation (24) and equation ( 25) are used to calculate the arrival time for location j; equation (26) and equation ( 27) are used to calculate the departure time of location j; equation (28) specifies that the departure time from the starting point and the hotel is ''0''; equation (29) and equation (30) are the time window constraints; equation (31) constrains that the daily travel time must not exceed a certain value.…”

Section: ) Modeling Of Travel Itinerary Customizationmentioning

confidence: 99%

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Co-Existing in Differences: Tailoring Travel Itineraries for Tourists With Similar Interests

Yang

2023

IEEE Access

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Customized tourism is gaining more and more attention in the era of the prominent personalization of tourists. Private customization has disadvantages such as high cost and resource consumption. To save resources and costs from the three aspects of travel companies, tourists, and society, it is proposed to bring similar tourists together and customize travel itineraries for tourists. Online resource information related to tourism and transportation help companies customize the itinerary more efficiently and accurately, and the itinerary has a detailed schedule. In this method, we first cluster visitors according to their heterogeneous needs. The relevant online information collected is then combined with the needs of each visitor in the tour group to build and solve customized models of the tour itinerary. Finally, the price of the tour itinerary is determined. At the end of this paper, we discussed what level of clustering makes visitors most satisfied. It proves that this approach can be customized for tour groups. Visitors show a certain level of satisfaction with this customized format. However, the applicability of this method needs to be further verified on a larger range of data sets.INDEX TERMS Customized tourism, heterogeneous demand, tourist clustering, model optimization. I. INTRODUCTIONThe service industry plays an important role in the world economy [1]. Tourism is a kind of service industry, which provides scenic spots, accommodation, catering, and other related services [2]. Tourists' demands for tourism products are increasingly personalized [3], and the personalization of tourism products is getting more and more attention from tourism companies and tourists [4], [5]. With the advent of COVID-19, visitor preferences have changed, and capturing visitor needs has become even more important [6], [7]. Generally speaking, the customization of private tourism must take full account of the individual needs of tourists, but it also increases the cost of tourists and tourism companies. Everyone traveling alone increases the social pressure of traffic and environmental problems. Some scholars have suggested that tourism has strong resilience. With the efforts of the government, the tourism industry, and other parties, it is only a matter of time before the tourism industry recovers [8], [9]. The emergence of COVID-19 has brought about aThe associate editor coordinating the review of this manuscript and approving it for publication was Claudia Raibulet .

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Section: B Travel Recommendationmentioning

confidence: 99%

Section: ) Modeling Of Travel Itinerary Customizationmentioning

confidence: 99%

Co-Existing in Differences: Tailoring Travel Itineraries for Tourists With Similar Interests

Yang

2023

IEEE Access

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“…In addition, the methods, subjects and data of researching tourist attractions are constantly expanding. Regarding the methodology, scholars have employed various methods, such as tourism behavior research methods [ 30 ], spatial statistical analysis methods [ 29 ], spatial simulation modeling methods [ 31 ], and network analysis [ 13 , 32 ], to explore the spatial patterns of tourist attractions. Regarding the subject, classification is based on the level of the tourist attractions, such as A-level, AA-level, and AAA-level tourist attractions [ 33 , 34 ], or the natural, cultural, and historical characteristics of the attraction, including world cultural heritage sites, natural reserves, national parks, characteristic towns, and rural tourism [ 25 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China

Jiang,

Huang,

Xiong

et al. 2024

Heliyon

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“…The spatial structure of tourist attractions not only involves the distribution, taste, and quantity combination of the attractions but also often has a profound impact on tourism activities. Therefore, the spatial structure and distribution patterns of tourist attractions have always been one of the core research topics in the domain of tourism geography [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution and Influencing Factors of High-Level Tourist Attractions in China: A Case Study of 9296 A-Level Tourist Attractions

Zikirya,

Zhou

2023

Sustainability

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The distribution pattern of high-level tourist attractions is crucial for the sustainable development of the tourism industry. However, few studies have explored the spatial distribution and dominant influencing factors of tourist attractions of different levels from a macro perspective in China. This study, which was based on large-scale multi-source data, involved the use of kernel density analysis, local spatial autocorrelation, and geographical detector analysis to explore the spatial distribution, spatial correlation, and dominant influencing factors of high-level tourist attractions in China. The study’s results show that the spatial distribution of tourist attractions of different levels is polarized and regionally clustered, and there exist some spatial correlation effects among attractions of the same level. Additionally, different influencing factors play a different role in determining the spatial distribution of attractions of different levels. Based on market demand and tourism resources, it is necessary to regulate attractions of different levels to promote the sustainable development of high-level tourist attractions and provide a reference for the development of China’s tourism industry.

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Explore for a day? Generating personalized itineraries that fit spatial heterogeneity of tourist attractions

Cited by 18 publications

References 42 publications

Co-Existing in Differences: Tailoring Travel Itineraries for Tourists With Similar Interests

Co-Existing in Differences: Tailoring Travel Itineraries for Tourists With Similar Interests

Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China

Spatial Distribution and Influencing Factors of High-Level Tourist Attractions in China: A Case Study of 9296 A-Level Tourist Attractions

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