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This paper analyzes the subsequent decisions of tourists with respect to sequential destinations: why they visit a given destination after visiting a previous one and where they are most likely to go. Using a dataset from an on-site tourist survey in Nanjing, China, we study this decision process by partitioning it into three stages, and a nested logit model is used to estimate the determinants in each stage. Apart from the individual characteristics of tourists and destination attributes, we find that the spatial configuration of destinations (spatial structure) also tends to influence tourists’ destination choice. Finally, a series of simulations are carried out to understand the competition/substitution patterns between subsequent destinations.
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... Firstly, most studies stress travel preferences of the majority, ignoring the specific needs of individual tourists (Kim et al. 2013;Zheng, Huang, and Li 2017;Xiaoyu Sun, Huang, and Peng 2018). Secondly, the existing studies rarely consider the order of visitation of tourist attractions (Xia, Zeephongsekul, and Packer 2011;Yang, Fik, and Zhang 2013), which is usually thoughtless, for the next visit of the tourist is closely linked to historical choice and its order (Song et al. 2010;Zheng, Huang, and Li 2017). Thirdly, current research rarely discusses the dynamic change of tourists' interest, since uncertain information such as traffic, weather, or emergencies is not only random but also changing over time (McKercher et al. 2014;Verbeeck, Vansteenwegen, and Aghezzaf 2016;Kolahkaj et al. 2020), resulting in changes in interest over time. ...
... The proposed neural network model can provide personalized service in an environment that affects tourists' travel preferences, which caters to the demand-oriented concept of smart tourism (Zheng and Liao 2019;Yang, Fik, and Zhang 2013). From personalized travel recommendation service to destination management, and then to crowd control, the proposed model offers broad application perspectives (Kang and Gretzel 2012;Mckercher and Lau 2008). ...
... Understanding tourists' travel behavior and recommending attractions or itineraries have become a hotspot for research in recent years (Yang, Fik, and Zhang 2013;Wong and McKercher 2012;Yeh and Cheng 2015). In this article, we present a neural network model to understand the potential movement patterns from tourists' travel trajectories and recommend personalized travel routes. ...
Travel recommendations form a major part of tourism service. Traditional collaborative filtering and Markov model are not appropriate for expressing the trajectory features, for travel preferences of tourists are dynamic and affected by previous behaviors. Inspired by the success of deep learning in sequence learning, a personalized recurrent neural network (P-RecN) is proposed for tourist route recommendation. It is data-driven and adaptively learns the unknown mapping of historical trajectory input to recommended route output. Specifically, a trajectory encoding module is designed to mine the semantic information of trajectory data, and LSTM neural networks are used to capture the sequence travel patterns of tourists. In particular, a temporal attention mechanism is integrated to emphasize the main behavioral intention of tourists. We retrieve a geotagged photo dataset in Shanghai, and evaluate our model in terms of accuracy and ranking ability. Experimental results illustrated that P-RecN outperforms other baseline approaches and can effectively understand the travel patterns of tourists.
... Classical discrete choice modelling frameworks (i.e., MNL model) were selected to estimate the destination and mode choices. The results showed that destination choices were associated with tourism motivations (Wu et al. 2012), demographic characteristics (Tang et al. 2020), and spatial configuration of destinations (Yang et al. 2013). García et al. (2015) investigated that the importance of previous visits to Majorca increased the probability of revisiting. ...
Insights into tourist travel behaviours are crucial for easing traffic congestions and creating a sustainable tourism industry. However, a significant portion of the literature analysed tourist travel behaviour by predefined tourist trip chains which result in the loss of more representative classification. Using tourist travel survey data from Nanjing, China, this paper presents an innovative methodology that combines the tourist trip chain identification and the trip chain discrete choice model to comprehensively analyse the travel behaviour of tourists. The discretized trip chains of tourists are clustered using the ordering points to identify the clustering structure (OPTICS) clustering algorithm to identify typical tourist trip chains, which will then be considered as the dependent variable in the nested logit model to estimate the significant explanatory variables. The clustering results show that there are two main categories, namely single and multiple attraction trip chains, and seven subcategories, which were named according to the characteristics of trip chains. The clustering result is analysed and three main trip chain patterns are derived. Departure city, travel cost, travel time, and travel mode show significant influence on the choice between single and multiple attraction trip chains. The urban attraction trip chain is more favoured by tourists with children, and the typical trip chain shows stronger dependence on travel intention. Visiting Lishui for the first time only affects the choice of the multiple suburban attraction trip chain. These findings are valuable for optimising tourist public transport infrastructure, promoting travel by public transport and better tourism management.
... The process by which tourists organize their trips is currently an intriguing subject for research. As a result, many research has focused on multi-destination travel rather than a single destination and has used the graph theory-based network analysis to study tourists' behavior and understand their decision-making processes (Shih, 2006;Lee et al., 2013;Yang et al., 2013;Wu & Carson, 2008). In this regard, network analysis techniques offer quantitative methods for assessing various centrality measures such as degree, betweenness, closeness and eigenvector of a node in a network (Hwang et al., 2006;Pavlovich, 2003;Shih, 2006). ...
This current study aims to establish a new centrality formula for tourism destinations in effective travel planning. Based on network analysis, the results provide several formulas for measuring centrality derived from our basic algorithm, which we call the attractiveness centrality for effective travel planning. Since the attractions at some tourist hup-points have an impact on the centrality scores of each destination, they can be utilised for more effective trip planning based on spatial patterns. With this in mind, several implications for future studies and destination authorities were also discussed.
... s the NL model with more flexibility in describing hierarchical and sequential choices.Huybers (2003) investigates the destination choice of tourists during a short-break holiday using a NL model and discovers the relative importance of trip attributes and the underlying destination structure in the perception of tourists from Melbourne, Australia.Yang et al. (2013) uses the on-site survey data from Nanjing, China and describes a three-stage destination choice process with the NL model. The tourists first choose to continue to the next destination or return home; then decide on the type of subsequent destinations if they choose to continue the trip; and subsequently choose the exact destination acc ...
... Some pedestrians will therefore form an itinerary or schedule of activities and this is likely to influence the decisions made as to which destinations to visit, with destinations where more important activities can be performed being more likely to be chosen. Research suggests many ways of capturing this intangible influencing factor, such as from market research [77,78], using decay functions based on individual opinions [67,79], the number of transitions between destinations [80], or using properties such as seating capacity [41], and floor space [71]. The desirability is a predictor with a context-dependent interpretation and one that can be used to capture a group of possible influential choice attributes. ...
The move towards publishing research data openly has led to the formation of reference databases in many fields. The benefits of such resources are numerous, particularly in the development of models. While these exist in research on other aspects of pedestrian behaviour, no reference database is available for modelling pedestrian destination choice, the process by which pedestrians choose where they wish to visit next. This work seeks to construct such a database from the literature. The resulting data obtained are described and potential ways in which they could be used to calibrate a simple pedestrian destination choice model are presented. It contains four datasets that include destination choices for hundreds of pedestrians in settings ranging from university campuses and music festivals to highly structured stated preference surveys. A case study using one of these datasets to calibrate a simple pedestrian destination choice model is provided. These efforts highlight some general issues from creating and using reference data openly. Discussing these issues will hopefully guide the development of reference data and accelerate the development of accurate pedestrian destination choice models that can be applied generally.
... Younger individuals have stronger aims to visit or make a trip to various destinations (Verma and Chandra, 2018;Young and Farber, 2019;Wu and Wang, 2005). The traveller's choice to travel influences the rate of the travel industry (Yang et al., 2013;Kim et al., 2020). The aim to return, isn't as significant as aim to visit as many destinations as possible due to individuals wanting to seek out a variety (Kozak et al. 2000;Souiden et al., 2017;Ekinci and Hosany, 2006). ...
Tourism industry plays an eminent role in the development of a country and is correlated with the economic growth of the economy. This research modelled the factors influencing tourists’ behavioural intentions tourism in Pakistan. Factors affecting the intentions of the tourists to visit a particular destination have been identified in this research. Primary data has been collected through questionnaires from respondents of various nationalities. Smart PLS has been used to apply SEM “Structural Equation Modelling approach” for hypothesis testing. The findings supported that destination satisfaction and destination attachment play an imperative role in the intentions of tourists to visit a location. More attached the visitors were with the destination, better the image they had of the destination, the satisfied they were with the destination and would therefore plan to visit it more often. The derivations from the analysis can play a pivot role in developing the policies and appropriate measures to enhance tourism industry in different countries. Attachment of the tourists and their image of the destination assumed a prominent role in their anticipation to visit Pakistan.
... Along with the growing availability of highquality data and knowledge spillover from mainstream economics and business disciplines, a rising number of econometric methods are being applied in hospitality and tourism management. Promising areas include tourism demand modeling (Song, Dwyer, Li, & Cao, 2012), hotel pricing (Abrate & Viglia, 2016), tourism-led growth (Lin, Yang, & Li, 2019), and destination choice analysis (Yang, Fik, & Zhang, 2013). ...
This book chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.
... Itineraries or schedules capture an order of 'priority' for activities pedestrians want to complete at different destinations [12], and they thus influence their destination choice behaviour. The desirability of destination has been investigated in different ways, notably in market research [13,14]. Sigmoid functions have been used to express the preferences of individuals for destinations [15], and features of buildings [2,16] or the number of transitions between destinations [17] have been used to estimate such preferences. ...
Which locations pedestrians decide to visit and in what order drives circulation patterns in pedestrian infrastructure. Destination choice is understood to arise from individuals trading off different factors, such as the proximity and busyness of destinations. Here, a virtual experiment is used to investigate whether this behaviour depends on the layout of buildings, whether planned or imposed destination schedules influence decisions and whether it is possible to distinguish different choice behaviour strategies in pedestrian populations. Findings suggest that virtual experiments can consistently elicit a range of destination choice behaviours indicating the flexibility of this experimental paradigm. The experimental approach facilitates changing the environment layout while controlling for other factors and illustrates this in itself can be important in determining destination choice. Destination schedules are found to be relevant both when imposed or generated by individuals, but adherence to them varies across individuals and depends on prevailing environmental conditions, such as destination busyness. Different destination choice behaviour strategies can be identified, but their properties are sensitive to the detection methods used, and it is suggested such behaviour classification should be informed by specific use-cases. It is suggested that these contributions present useful starting points for future research into pedestrian destination choice.
Dispersal of tourist is a growing policy concern in Australia and New Zealand, as well as other tourist-dependent nations. However, the understanding of this field is limited. This paper summarises literature of the last three decades based on 58 shortlisted journal articles published between 1980 and 2019. Based on prior research, this paper contributes to the field by proposed an improved conceptual definition of tourist dispersal. Typical dispersal patterns are also explored, alongside with an outline of key intervening factors. For future researchers, we outlined new and old data collection methods, and also the corresponding analytical approaches. There is a growing trend to use more advanced tracking methods to address the limitation of prevailing approaches, such as tourist survey or travel diaries. Quantitative approaches have been the mainstay of research but qualitative approaches are also able to capture unique aspects of tourist psychology and motives to disperse. Significant gaps remain in existing knowledge and the potential for dispersal to improve destination fortunes and tourist experience are still unknown. Future research ought to combine various approaches by combining datasets and also methodologies.
Because tourists derive utility from the enjoyment of destination characteristics, Lancaster's approach is putatively appropriate to address the particular structure of the tourism industry. Most research efforts regarding tourism destination, including those applying Lancaster's model, specifically address the choice of a single destination. This article is intended to explain multiple destination choice using Lancaster's characteristics model and a discussion of model implications of some marketing strategies for destinations as well as for tour operators. The model developed herein explains that packages of multiple destinations can create preferable combinations of characteristics for certain travelers. Furthermore, the model provides useful strategies for tour operators in combining destinations into a travel menu or package.
This study examines the role of cognitive distance measures in predicting tourists’ destination visiting intentions. In addition, the study examines the stability of those roles across three distance ranges. A total of 1,020 Taichung residents in Taiwan were administered an onsite questionnaire. The results indicated that, when considering all destinations, cognitive distance estimates did not directly predict tourists’ visiting intentions; instead, cognitive distance estimates influenced the inaccuracy of cognitive distance which then predicted visiting intentions. Point pattern analysis revealed that respondents preferred destinations were spatially clustered; therefore, the stability of the proposed model was then tested across psychological, hybrid, and physical distance ranges. The complexity of the findings was then interpreted in the context of Taiwan’s spatially clustered destinations; and theoretical, research, and practical implications are proposed.
The prime purpose is to examine the research methods employed by recreation geographers. The chapters deal with: descriptive; explanatory; predictive; and normative research on location, and travel. A secondary result is to create a work definition of the field of recreation geography. Examples are drawn from all over the world and cover research over the last 100 yrs.-J.Clayton
A central theme in outdoor recreation has been the development of general models that can be used to forecast future participation levels. These models have traditionally included measures of distance-decay, as well as attributes of the household and the recreation facility believed to affect outdoor recreation behaviour. Recent research by a number of scholars suggest, however, that the results found by applying these models lack comparability between regions because they may be seriously effected by the spatial structure of recreation opportunities. The nature of the problems associated with spatial structure are briefly introduced and an accessibility index was employed in order to identify households facing similar distributions of recreation opportunities. An analysis of covariance was then employed to identify the differing effects of spatial structure upon outdoor recreation behaviour. The implications of the research are discussed with particular reference to variety seeking and spatial diversification behaviour.
Outdoor recreation in Illinois encompasses a wide variety of activities as well as land and water resources. As a response to increasing conflict over resource use, households have adopted differing travel strategies to satisfy recreational needs. This paper examines the compatibility of outdoor recreation activities as an influence on travel behavior by focussing upon the number of different recreation areas that are visited in relation to choice of activities. The concept of compatibility takes into account intertemporal travel patterns and the linkage between the resource base and the availability of facilities for specific recreation activities. The results indicate that the availability of site resources relates to the participants' evaluation of activity compatibility as well as the number of alternative parks and recreation areas visited. Fifty-five percent of the variation in travel behavior to Illinois' public facilities can be related to the compatibility of activities and only about three percent is related directly to socio-economic or spatial variables.
Available at JSTOR https://www.jstor.org/stable/pdf/490371.pdf?casa_token=hsoA9LekQvQAAAAA:gKdA4GP29oKPVoOjKIttGMwAvxNiKCgxOTODDskHSeKGRkwhR7QCIfBdBf2acCnYu-ROIQyPKsbWrr1I3vx_lYljjkb3i9QlCllDyxPSXOdrPcoT7WmJRQ
Preface 1. In the beginning 2. Basic notations of statistics 3. Choosing 4. Paradigms of choice data 5. Processes in setting up stated choice experiments 6. Choices in data collection 7. Nlogit for applied choice analysis: a primer 8. Handling choice data 9. Case study: model choice data 10. Getting started modelling: the workhorse - MNL 11. Getting more from your model 12. Practical issues in the application of choice models 13. Allowing for similarity of alternatives 14. Nested logit estimation 15. The mixed logit model 16. Mixed logit estimation Nlogit terms and commands References Index.