Forecasting tourism demand by extracting fuzzy Takagi-Sugeno rules from trained SVMs

Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been widely studied, highly accurate and understandable forecasting models have not been developed. The present paper proposes a novel tourism demand forecasting method that extracts fuzzy Takagi-Sugeno （T-S） rules from trained SVMs. Unlike previous approaches, this study uses fuzzy T-S models extracted from the outputs of trained SVMs on tourism data. Owing to the symbolic fuzzy rules and the generalization ability of SVMs, the extracted fuzzy T-S rules exhibit high forecasting accuracy and include understandable pre-condition parts for practitioners. Based on the tourism demand forecasting problem in Hong Kong SAR, China as a case study, empirical findings on tourist arrivals from nine overseas origins reveal that the proposed approach performs comparably with SVMs and can achieve better prediction accuracy than other forecasting techniques for most origins. The findings demonstrated that decision makers can easily interpret fuzzy T-S rules extracted from SVMs. Thus, the approach is highly beneficial to tourism market management. This finding demonstrates the excellent scientific and practical values of the proposed approach in tourism demand forecasting.

Tourism demand forecasting and tourists’search behavior:evidence from segmented Baidu search volume

Given the importance of web search volume for reflecting tourists'preferences for certain tourism services and destinations,incorporating these data into forecasting models can significantly improve forecasting performance.This study enriches the literature on tourism demand forecasting and tourists'search behavior through segmented Baidu search volume data.First,this study divides Baidu search volume data based on volume sources and periods.Then,by analyzing the most relevant keywords in tourism demand in different segments,this study captures the dynamic characteristics of tourist search behavior.Finally,this study adopts a series of econometric and machine learning models to further improve the performance of tourism demand and forecasting.The findings indicate that tourists’search behavior has changed significantly with the prevalence and popularization of 4G technology and suggest that search volume improves forecasting performance,especially search volume on mobile terminals,from 2014M1–2019M12.

Study upon the Spatial Structure of Regional Eco-tourism-Based upon the Perspective of Supply and Demand and Competition Field

Along with the coming of the low-carbon era, people have paid more and more attention to the natural environment and eco-tourism will embrace a huge development. From the perspectives of the market relationship of supply-demand in economics and of field competition in physics, this paper has discussed upon the present status of the spatial structure of eco-tourism, and analyzed the relationship between supply-demand and field, in order to clarify the direction for the balance between supply and demand in the field and to guide eco-tourism to the way of sustainable development.

From IH&RA's Cairo Congress Sees Revival in Global Travel and Tourism Demand

“The past two years have been extremelydifficull for the travel and tourism industry-notleast for the hospitality sector,”Alain-PhilippeFeutre.IH&RA CEO told delegates from some40 countries meeting in Cairo for the 40thAnnual Congress of the Internationa J Hotel ＆Restaurant Association(5-9 December 2003).Yet three days of presentations and discussionsin the Egyptian capital reflected a newly foundconfidence among hotel operators,suppliers,destinations and other tourism experts as to theimminent revivaI of travel and tourism demand.

Tourism Traffic Demand Prediction Using Google Trends Based on EEMD-DBN

Predicting tourism traffic demand accurately plays an important role in making effective policies for tourist administration. It helps to distribute the resources reasonably and avoid the tourism congestions. This paper considered the noise interference and proposed a hybrid model, combining ensemble empirical mode decomposition (EEMD), deep belief network (DBN) and Google trends, for tourism traffic demand prediction. This model firstly applied dislocation weighted synthesis method to combine Google trends into a search composite index, and then it denoised the series with EEMD. EEMD extracted the high frequency noise from the original series. The low frequency series of search composite index would be used to forecast the low frequency tourism traffic series. Taking the inbound tourism in Shanghai as an example, this paper trained the model and predicted the next 12 months tourism arrivals. The conclusion demonstrated that the forecast error of EEMD-DBN model is lower remarkably than the baselines of ARIMA, GM(1,1), FTS, SVM, CES and DBN model. This revealed that nosing processing is necessary and EEMD-DBN forecast model can improve the prediction accuracy.

国外游憩需求研究20年——《Annals of Tourism Research》所反映的学术态势

对1974～2002年《旅游研究纪事》(AnnalsofTourismResearch)上标题、关键词出现“需求”(demand)的文章进行了分析,包括从发表文献的时间,作者及其地域背景,研究内容和方法等方面。通过定量、定性分析等手段,探讨20年来国际游憩需求的研究状况,总结出目前游憩需求研究的总体特点,并展望未来游憩需求研究的趋势。

Pulling Vs. Pushing:Effect of Climatic Factors on Periodical Fluctuation of Russian and South Korean Tourist Demand in Hainan Island,China

While climate is an important factor attracting tourists to certain destinations,it can also motivate people residing in a country with a harsh climate to move to another location.By applying X-12 decompositions and a panel data regression analysis,this study analyzes the pull and push effects of climatic seasonal factors between destination(Hainan Island,China) and source countries(Russia and South Korea).The findings show that climatic seasonal factors have significant pulling and pushing effects on seasonal patterns of tourism demand,with temperature being the main factor.Furthermore,the number of paid vacation days in the source country affects that country's sensitivity to climatic seasonal factors;countries with a higher numbers of paid vacation days are more sensitive to climatic conditions.Lastly,future global warming may causes the aforementioned pull and push effects to abate,which will have an unavoidable influence on tourism industries.

Development Strategies of Tea Culture Tourism in the Age of Experience Economy

Experience economy is the fourth development phase following the agrarian economy,the industrial economy and the service economy.In terms of tourist economy,travelling itself is a kind of experience activity,thus such an attribute determines that tourist products should satisfy the individualized needs of tourists.This study proposes that the theory of experience economy can be applied in the development of tea culture tourism to explore the functions of tea culture which could be experienced by tourists,such as leisure experience,health experience,culture experience and so on,and meanwhile,gives strategies of developing tea culture tourism in the age of experience economy.

Interpretable Tourism Demand Forecasting with Two-Stage Decomposition and Temporal Fusion Transformers

This paper proposes a novel interpretable tourism demand forecasting framework that considers the impact of the COVID-19 pandemic by using multi-source heterogeneous data,namely,historical tourism volume,newly confirmed cases in tourist origins and destinations,and search engine data.This paper introduces newly confirmed cases in tourist origins and tourist destinations to forecast tourism demand and proposes a new two-stage decomposition method called ensemble empirical mode decomposition-variational mode decomposition to deal with the tourist arrival sequence.To solve the problem of insufficient interpretability of existing tourism demand forecasting,this paper also proposes a novel interpretable tourism demand forecasting model called JADE-TFT,which utilizes an adaptive differential evolution algorithm with external archiving(JADE)to intelligently and efficiently optimize the hyperparameters of temporal fusion transformers(TFT).The validity of the proposed prediction framework is verified by actual cases based on Hainan and Macao tourism data sets.The interpretable experimental results show that newly confirmed cases in tourist origins and tourist destinations can better reflect tourists'concerns about travel in the post-pandemic era,and the two-stage decomposition method can effectively identify the inflection point of tourism prediction,thereby increasing the prediction accuracy of tourism demand.

A Research on Approach to the Development Strategy of Tourism Economic Growth

As the tourism is improving, the economy growth can be obtained. Therefore, to improve tourism is to improve the economy. In fact, some supplies of tour could not meet the demands, which brings out conflict. It is obvious that solving the problem of supply and demand of tour is the approach to the development strategy of tourism economic growth.

旅游新质生产力:供需特质、创新取向与新旧之辨

新质生产力是以科技创新为主导的生产力,广泛地渗透到旅游领域,深刻地变革了传统旅游业的生产方式和旅游者消费行为。然而,旅游新质生产力的内涵以及应用范围尚未得到清晰界定。因此,本文围绕旅游新质生产力,从供给侧和需求侧辨析旅游新质生产力的表现和特征。本文认为:旅游新质生产力在供给侧方面具有创新驱动性,在需求侧更强调人本内涵,且将旅游供给与需求紧密地融合在一起;旅游新质生产力通过助推旅游数字生产创新、强化旅游产业链协调发展、延伸和创造新的旅游产品及体验等方式推动了旅游业高质量发展。最后,本文分析了旅游新质生产力与旧质生产力的关系,以期为探索旅游业发展路径提供有益参考。

国家图书馆文旅融合服务供需分析研究:19个图书馆的实践解析

以19个国家图书馆为研究对象,采用网络调查法、案例分析法和文献研究法,梳理国家图书馆文旅融合服务供需两端相关理论并提炼分析框架,基于框架归纳阐释四大实践特点并提出经验启示。国家图书馆文旅融合服务实践呈现国家典籍珍藏及展示、以人文吸引力为核心、用户导向的文旅服务、开放包容的文旅服务的特点。我国其他图书馆应充分借鉴国家图书馆文旅融合服务实践经验,强化馆藏资源质量基础支撑,以人文吸引力为重点发展方向,推动文旅融合服务全覆盖、个性化并营造开放包容服务环境。

康养旅游背景下高职酒店管理与数字化运营专业人才培养探索

康养旅游产业的快速发展对康养旅游从业人才素养提出了新的需求,加快康养旅游人才培养步伐迫在眉睫。文章通过梳理酒店业态康养方向的发展现状,剖析市场对康养旅游人才的需求,从明确人才培养目标、形成学校内部协同培养机制、重构课程体系、开拓多元化的专业实践教学基地四个方面,探索酒店管理与数字化运营专业(康养方向)人才培养的实现路径,以期为温州康养旅游业的发展储备人才。

旅游偏好是否可以促进国际旅游消费?——基于中国旅游调查的实证研究

从传统经济学、行为经济学的角度出发,利用2016年中华人民共和国文化与旅游部全国性的国民旅游休闲调查数据,分析了旅游偏好与国际旅游消费之间的相互作用,运用最小二乘加权回归模型(WLS)研究了旅游偏好对旅游消费的影响,以及旅游需求在旅游偏好与旅游消费中的中介作用。结果发现:(1)对于国内旅游,旅游偏好通过旅游需求的中介作用对旅游消费产生正向作用,即消费者对特定旅游活动和目的地的偏好驱动了他们对国内旅游的需求,从而增加了国内旅游消费;(2)对于出境国际旅游,在交易效用和损失厌恶效应的影响下,旅游偏好通过国际旅游需求的中介作用对国际旅游消费产生正向影响作用,但具有明显的区域性特征,在中国西部城市无正向促进作用,但在东部城市和中部城市有正向促进作用;(3)男性和女性在旅游偏好和旅游消费上不存在性别差异。

基于JADE-TFT模型的可解释性旅游需求预测研究

本研究提出了一种新颖的考虑疫情冲击的可解释性旅游需求预测框架,通过使用多源异构数据,即历史旅游量、本土新增确诊病例、百度指数、天气数据来预测新冠疫情影响下国内旅游需求变化。本研究引入疫情相关的搜索引擎数据的概念来进行旅游需求预测,并提出合成领先搜索指数和变分模态分解法结合处理搜索引擎数据的方法。同时,本研究提出了JADE-TFT可解释旅游需求预测模型,该模型利用带有外部归档的自适应差分进化算法智能高效地优化时域融合变换器的超参数。TFT是一种基于注意力的深度学习模型,将高性能预测与可解释的时间动态分析相结合,在预测研究中表现出优异的性能。TFT模型产生可解释的旅游需求预测输出,包括不同输入变量的重要性排序和不同时间步的注意力分析。基于黄山旅游数据的实际案例验证了所提出预测框架的有效性。可解释的实验结果表明,疫情相关的搜索引擎数据可以更好地反映后疫情时代游客对出行的担忧。

供需视角下的传统村落旅居康养路径研究——以南昌为例

乡村旅居康养是以乡村康养服务体系和旅居生活方式为核心,倡导“康养、社交、度假、文娱”新时代旅居理念,并成为一种新的康养生活方式。通过对城市中老年人的问卷调查,了解城市中老年人对乡村旅居康养的认知度和需求度。通过对南昌市13个传统村落的实地勘察,分析其传统村落中的村民意愿及康养资源、公共服务设施现状情况,由此发现南昌市传统村落旅居康养的供需矛盾,分析传统村落在旅居康养供给中面临的问题和困境,并提出相对应的改善措施以优化供需平衡,从而推动传统村落的保护发展,以期为其他传统村落的旅居康养空间规划设计提供借鉴。

医疗需求驱动下自贸区医疗旅游创新模式研究

全球医疗旅游逐年快速增长,我国医疗需求外溢现象凸显.论文聚焦于我国自贸区,通过医疗旅游模式创新,以实现境内医疗资源的优化配置,推动居民医疗旅游消费回流.通过对跨境医疗旅游发展现状、驱动机制以及医疗旅游消费者选择行为等方面的深入剖析,揭示了当前医疗旅游的形态.以海南国际医疗旅游先行区为案例,探讨了在国家政策引领下的区域医疗旅游模式的孵化路径.最后,通过对全球主要医疗旅游目的地的优势比较分析,为我国自贸区进一步拓展医疗旅游模式,构建国际化的医疗旅游目的地品牌提供了路径启示.论文为推动医疗旅游从外循环向内循环的转型,实现医疗旅游以内循环为支撑的产业发展新境界,以期达到跨境医疗不跨境,为新兴目的地医疗旅游模式创新提供了理论基础与战略建议.

我国冰雪体育旅游产业供需动态平衡的关键堵点及纾解路径

运用文献资料法、逻辑分析法,剖析我国冰雪体育旅游产业供需动态平衡的理想样态、关键堵点,并提出纾解路径。理想样态:生产提质增效,高质量供给形成;利益分配合理,彰显公平正义;流通效率提升,要素流通顺畅;消费内需扩大,需求质量升级。关键堵点:供给结构性失衡仍突出,利益分配不均有待破解,要素流通效率亟须提升,消费需求潜力释放不足。纾解路径:突破低端产品定格,培育创新型供给;恪守公平正义原则,完善利益分配机制;加快要素市场化配置,完善流通基础设施;提升大众消费黏性,推动居民消费升级。

数字文旅视角下“云游杭州”严肃游戏设计研究

在数字文旅的视角下,以杭州的文旅景点作为实践对象进行严肃游戏设计,通过游戏机制与数字技术将现实的文旅体验映射于虚拟游戏世界,为目前文旅领域的发展带来了新的设计思路和实践参考。基于八角行为分析法,提出数字文旅严肃游戏的设计方法与思维逻辑,分析三类用户群体及其核心驱动力,设计游戏活动回路,并以云游杭州严肃游戏为例进行设计实践。基于严肃游戏的原理,构建数字文旅体验的游戏设计流程。提取用户特征、文化元素、目标、动机进行游戏内符号系统转化,采取多元融合设计,为同类数字文旅产品的应用带来新的设计思路。

航空运输对旅游需求的影响——基于中国省级面板数据分析

近20年来,中国航空和旅游业快速增长,考虑到其对经济的重要性,需要协调规划与政策。利用2012—2021年中国省级面板数据,采用两阶段最小二乘法,关注航空运输对旅游需求的影响,旨在验证其促进效益,推动中国民航与旅游的协同发展。实证结果表明,航空运输对旅游需求有显著正向影响,民航服务改善和旅游市场潜力扩大有助于旅游业增长。因此,地方政府、机场和航空公司合作,有助于政府、旅游业者和航空业者了解市场需求变化,及时调整政策,促进民航与旅游业深度融合与创新发展。