Forecasting hourly attraction tourist volume with search engine and social media data for decision support

Developing a tourism forecasting function in decision support systems has become critical for businesses and governments. The existing forecasting models considering spatial relations contain insufficient information, and the spatial aggregation of simple tourist volume series limits the forecasting accuracy. Using human-generated search engines and social media data has the potential to address this issue. In this paper, a spatial aggregation-based multimodal deep learning method for hourly attraction tourist volume forecasting is developed. The model first extracts the daily features of attractions from search engine data; then mines the spatial aggregation relationships in social media data and multi-attraction tourist volume data. Finally, the model fuses hourly features with daily features to make forecasting. The model is tested using a dataset containing several attractions with real-time tourist volume at 15-minute intervals from November 27, 2018, to March 18, 2019, in Beijing. And the empirical and Diebold-Mariano test results demonstrate that the proposed framework can outperform state-of-the-art baseline models with statistically significant improvements at the 1% level. Compared with the best baseline model, the MAPE values are reduced by 50.0% and 27.3% in 4A attractions and 5A attractions, respectively; and the RMSE values are reduced by 48.3% and 26.1%, respectively. The method in this paper can be used as a function embedded in the decision support system to help multi-department collaboration.     

基于IEME的低碳化景区综合集成评价

低碳经济时代的演进,全球环境问题的凸显对全球旅游业的发展提出了新要求,旅游产业及景区的可持续发展寻求从传统的粗放型向新兴的集约型发展方式转变,如何促进景区低碳化建设,兼顾社会效益、经济效益与生态效益协调发展,实现景区可持续发展已成为社会各界关注的热点。针对国内外现有的旅游景区传统线性评价成果的不足,未能从复杂系统的角度体现景区系统低碳环保举措的成效及其效益的发展趋势。本文在复杂性科学管理熵与管理耗散结构理论的指导下,从经济运行、社会发展、生态环保、低碳控制、建设保障等维度构建低碳化景区多维综合集成评价体系,将管理熵增与管理耗散结构模型及改进后的熵值法应用于低碳化景区建设的评价过程中,从宏微观角度系统地揭示景区低碳化建设的系统序度、发展趋势。     

Who captures whom – Pokémon or tourists? A perspective of the Stimulus-Organism-Response model

Since its launch in 2016, Pokémon Go has attracted huge numbers of players, causing a boom in this game market. Although it is not as popular as before, from time to time we still find crowds of players gathered in some spots where Pokémon appear. Numerous reports have explored this Pokémon phenomenon; however, the exact reasons for its popularity remain unknown. The purpose of this study is to explore the post-adoption behavior of Pokémon Go players and its influential factors in the gaming and tourism industries. The theoretical model of stimulus-organism-response was drawn on to examine the impact of the environmental stimuli (social influence and media influence) on players’ internal organisms, which in turn affect their post-experience responses. Moreover, gender differences were also examined in the hypothetical relationships. A total of 342 valid questionnaires from actual gamers were collected in this study, and data analysis was performed using a structural equation model. The results show that stimulus effects, such as social stimuli (critical mass and social interaction) and media stimuli (content timeliness and media richness), have significant impacts on the players’ internal gamified experience (attachment and conformity), which in turn affect their visit intention to catch creatures at certain attractions and to continue playing Pokémon Go. Further, we have also found that players’ intention to visit Pokémon spots is significantly correlated with their intention to continue playing the game. Findings provide links between gamification and tourism literature. Further theoretical and managerial implications are provided.     

海峡西岸经济区旅游景区(点)空间结构分析

本文以4A级景区（点）为例,基于GIS技术,通过地理集中指数、分布椭圆、最近邻指数等方法分析海峡西岸经济区旅游景区的空间结构特征。结果表明：厦门是旅游景区资源最丰富的城市;旅游景区总体上在市际尺度呈集中分布趋势,在空间上呈聚集分布;自然景点、红色景点和客家景点是主要景点类型和特色景点类型,其分布范围与区域自然地貌特征、历史文化渊源相符;自然景点、红色景点、客家景点在市际尺度上都呈集中分布趋势,在空间上自然景点、红色景点呈均匀分布,客家景点呈聚集分布。本文认为自然地貌特征与历史文化渊源、城市经济发展水平、政府参与是影响海峡西岸旅游景区空间结构的主要因素,并基于此提出一些优化旅游景区空间结构的措施。     

技术嵌入视野下的低碳旅游景区建设研究

对技术在旅游业中的应用和低碳旅游景区建设涉及的主要服务内容进行综述,并在此基础上,提出低碳旅游景区建设主要包括核心、辅助和延伸三层面服务内容,分析低碳旅游景区建设其服务内容的技术需求,阐述低碳旅游景区建设服务内容的技术嵌入原则和路径,最后对低碳旅游景区建设服务内容的技术嵌入实现进行论述。     

基于Logit模型的北京世界遗产地游客选择研究

世界遗产是北京建设世界旅游城市重要的资源基础,而世界遗产地的旅游发展很大程度决定于游客决策。尽管我国在游客决策相关领域已有一定研究成果,但是针对世界遗产地游客决策行为的研究仍然十分欠缺。本文以北京世界遗产地游客为研究对象,基于二项Logit模型,分析探讨了游客选择的模式差异以及景点选择的影响因素。研究结果显示：①认知（“是否为世界遗产”和“是否为北京代表性景点”）影响与景点知名度成反比,北京6个世界遗产地总体可以分为三类,其中故宫主要受游客特征和旅游形式的影响,颐和园、长城、天坛同时受到认知和旅游形式的影响,十三陵和周口店仅受到游客认知特征的影响;②近距离负面影响游客对高知名度景点的选择,而短期游客会更倾向于选择高知名度景点。因而,建议加强低知名度景点的认知和景点独特性的宣传,关注重点群体,丰富旅游体验,提升景点综合竞争力。     

ICTs and Internet adoption in China's tourism industry

The fast development of information communication technologies (ICTs) and the expansion of the Internet have changed industry structures around the world. New technologies have been adopted in the tourism industry in Europe and America for more than 30 years, and the trend is likely to continue into the future. China, as a fast-growing developing country in Asia, is gaining importance in the international tourism market for its historical and cultural attractiveness as a destination. It is also becoming a booming tourism source country as its population starts travelling overseas. This study examines how the ICT and Internet gradually change the tourism industry structure in China; how important such changes are, and to where such changes will lead China's tourism industry. This exploratory research is conducted based on information collected from several tourism organisations, such as airlines, hotels, tour operators, visitor attractions and the tourism authorities within China.     

上海工业旅游资源的空间分布与联动特征

以城市工业旅游资源的空间结构为研究对象,运用地统计方法和GIS的最邻近点指数、α指数、β指数、γ指数和通达性指数测度上海市64家有代表性的工业旅游景区总体空间分布类型与联动特征。这将为城市工业旅游景点开发模式选择和旅游线路设计提供空间决策依据,同时又有助于完善后工业社会城市的工业旅游景区与毗邻地区的综合规划策略。研究发现：①上海市工业旅游资源总体呈弱集聚分布,旅游网络趋于环网型;②不同类型的工业旅游景区空间集聚程度与连接程度不同,创意产业集聚区的集聚度与连接度比工业企业景点、工业博物馆高,说明创意产业集聚区工业旅游发展成熟度要高于工业企业景点与工业博物馆,而后两者相较之下存在较高开发潜力;③上海市核心区工业旅游景区通达度较好,具有良好的联动条件与联动潜力。     

基于网络数据文本分析的目的地形象维度分异研究——以南京为例

随着Web2.0技术的发展,网络文本已成为目的地形象研究的重要数据来源。本文选取南京作为案例地,以马蜂窝游客对南京的10 077条在线评论文本为数据,运用网络大数据文本挖掘法、词频统计法和共现网络法,分析得出目的地形象维度重要性存在差异,旅游吸引物是第一层次,公共基础设施是第二层次,休闲和娱乐、旅游环境和地方氛围是第三层次,旅游基础设施是第四层次。其次,南京感知形象中突出的认知成分源于旅游吸引物和公共基础设施维度的感知,而情感形象为正面,并产生了一定的意动形象,形成了网络口碑效应。此外,南京整体形象主要源于旅游吸引物和公共基础设施维度的感知,进一步验证了感知维度的重要性存在差异。本文验证了旅游形象的相关理论,丰富了旅游大数据研究方法,并可为南京旅游形象建设和发展提供科学依据。     

旅游网络舆情危机事件中观点演化的仿真与实证研究

【目的/意义】旅游业是国民经济中最容易受到外部事件冲击的行业之一。近年来由于交通意外、设施损 坏、自然灾害、突发公共卫生事件使得涉旅危机事件逐渐增多。随着网络新媒体的发展,以微博为代表的社交媒体 逐渐成为旅游网络舆情危机事件产生与传播的策源地。本论文研究旅游网络舆情危机事件的观点演化机制,对旅 游行业相关部门科学地进行危机舆情的监控与引导具有重要的意义。【方法/过程】本文从虚拟仿真与实证分析入 手,分析旅游网络舆情危机事件中网民观点的演化过程。【结果/结论】研究发现旅游舆情网络具有无标度的结构特 征,旅游网络危机舆情具有纵深扩散、反复发酵的演化规律。最后提出旅游网络舆情危机事件线上线下协同治理 的对策建议。【创新/局限】采用混合方法研究,在模型的基础上构建了无标度加权网络结构特征的旅游网络舆情危 机事件观点演化模型,在仿真研究的基础上结合实证分析,为研究旅游网络舆情危机事件观点演化机制提供了新 的研究视角;但研究中对于观点演化机制的网络平台研究仅考虑了微博,将来研究中将着眼于更多的新媒体社交 平台进行分析。     

旅游环境承载力研究进展

随着旅游业的发展,作为旅游业发展载体的旅游景区的游客接待量也大幅攀升,出现旅游景区“人满为患”的同时也对其带来了不可避免的破坏和污染,同样也使游客的游览体验质量大幅下降。如何科学地界定旅游案区入区人数,以保证景区最佳旅游功能的发挥并使人们获得良好的旅游体验,必须对风景区的旅游承载力进行系统研究,帮助管理者更好地对景区进...     

Demand forecasting model using hotel clustering findings for hospitality industry

Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models.     

旅游景区服务提供效率评价方法——一个低碳经济视角

从低碳经济视角出发,构造了旅游景区碳排放足迹指标,对旅游活动的环境压力进行测度,再利用数据包络分析法与方向性距离函数,结合旅游景区碳排放足迹指标,构建了低碳旅游服务提供效率模型与指数。最后,以四川省19个旅游景区为对象,将传统旅游服务提供效率与低碳旅游服务提供效率的测算结果进行对比分析,研究结果显示,环境因素考虑与否,对旅游景区服务提供效率值产生很大的影响,考虑环境因素的旅游服务提供效率测评更为科学、合理。     

城市旅游生态足迹测评——以福建省福州市为例

我国旅游城市众多,城市旅游的可持续发展正成为旅游研究的重要领域。生态足迹(ecological footprint)理论以其理论、方法的创新性和实践的可操作性,为旅游可持续发展研究提供了新思路和新方法。基于生态足迹的理论,从旅游者消费结构特征出发,将城市旅游生态足迹划分为旅游交通、餐饮、住宿、购物、游览、娱乐等6个方面,并对福建省福州市的旅游生态足迹进行了测评,计算出2005年福州市的人均均衡旅游生态足迹为0.039795hm2,其中旅游交通的生态占用比重最大,达64.4%;化石能源地的土地类型占用最多,为66.9%,旅游活动对总生态赤字的贡献率为7.6%。旅游生态足迹的实证分析表明,城市旅游活动是一种对自然资源高需求和高消耗的综合性生活方式,是影响旅游目的地生态环境的重要因素。     

An overview of online fake news: Characterization,detection, and discussion

Over the recent years, the growth of online social media has greatly facilitated the way people communicate with each other. Users of online social media share information, connect with other people and stay informed about trending events. However, much recent information appearing on social media is dubious and, in some cases, intended to mislead. Such content is often called fake news. Large amounts of online fake news has the potential to cause serious problems in society. Many point to the 2016 U.S. presidential election campaign as having been influenced by fake news. Subsequent to this election, the term has entered the mainstream vernacular. Moreover it has drawn the attention of industry and academia, seeking to understand its origins, distribution and effects.Of critical interest is the ability to detect when online content is untrue and intended to mislead. This is technically challenging for several reasons. Using social media tools, content is easily generated and quickly spread, leading to a large volume of content to analyse. Online information is very diverse, covering a large number of subjects, which contributes complexity to this task. The truth and intent of any statement often cannot be assessed by computers alone, so efforts must depend on collaboration between humans and technology. For instance, some content that is deemed by experts of being false and intended to mislead are available. While these sources are in limited supply, they can form a basis for such a shared effort.In this survey, we present a comprehensive overview of the finding to date relating to fake news. We characterize the negative impact of online fake news, and the state-of-the-art in detection methods. Many of these rely on identifying features of the users, content, and context that indicate misinformation. We also study existing datasets that have been used for classifying fake news. Finally, we propose promising research directions for online fake news analysis.     

Social commerce: Factors affecting customer repurchase and word-of-mouth intentions

Social commerce refers to an extension of e-commerce sites, integrated with social media and Web 2.0 technology to encourage online purchases and interactions with customers before, during, and after the purchase. As the country with the largest e-commerce market in Southeast Asia and many active social media users, Indonesia has many opportunities to implement successful social commerce. Since customers are the primary focus in social commerce, repurchase and word-of-mouth (WOM) intentions have been considered as significant behavioural intentions after a customer completes a purchase. Thus, this study aims to identify factors that affect customers’ repurchase and WOM intentions. A total of 421 sets of survey data were gathered from social commerce customers in Indonesia and analysed using the partial least squares approach. The results indicate that repurchase and WOM intentions are positively affected by trust and satisfaction, where both trust and satisfaction are positively affected by reputation and information quality. The results provide theoretical and practical implications for future social commerce research and practical implications for social commerce firms.     

北方户外运动产业现状及价值创新策略

近年来,我国户外运动产业发展迅速。随着生活方式的多元化发展,北方的户外运动越来越受到人们的关注和喜爱,通过对我国户外产业现状及北方户外运动产品消费需求特征的调研,提出了北方户外运动市场需求的价值创新策略。如何抓住机遇,进一步推进户外运动产业的发展,是摆在我们面前的一个重要课题。     

Micro-Doppler-based target detection and feature extraction in indoor and outdoor environments

In many cases, a target or a structure on a target may have micro-motions, such as vibrations or rotations. Micro-motions of structures on a target may introduce frequency modulation on the returned radar signal and generate sidebands on the Doppler frequency shift of the target's body. The modulation due to micro-motion is called the micro-Doppler (m-D) phenomenon. In this paper, we present an effective quadratic time-frequency S-method-based approach in conjunction with the Viterbi algorithm to extract m-D features. For target recognition applications, mainly those in military surveillance and reconnaissance operations, m-D features have to be extracted quickly so that they can be used for real-time target identification. The S-method is computationally simple, requiring only slight modifications to the existing Fourier transform-based algorithm. The effectiveness of the S-method in extracting m-D features is demonstrated through the application to indoor and outdoor experimental data sets such as rotating fan and human gait. The Viterbi algorithm for the instantaneous frequency estimation is used to enhance the weak human m-D features in relatively high noise environments. As such, this paper contributes additional experimental m-D data and analysis, which should help in developing a better picture of the human gait m-D research and its applications to indoor and outdoor imaging and automatic gait recognition systems.     

北京市音乐旅游资源分布规律研究

音乐旅游是文化旅游的重要组成部分,不仅能够拓宽旅游业发展的渠道,同时极大地丰富了居民的文化生活。本文选取北京市作为研究区域,对音乐旅游资源的分布规律进行了研究。在前期研究基础上,结合网络、文献、调研收集数据,将音乐旅游资源按功能分为3类：音乐表演场所旅游资源、音乐观光旅游资源、音乐休闲旅游资源,并将不同功能、定位和性质的音乐旅游资源结合起来,建立了北京市音乐旅游资源采样数据库,采用GIS空间分析技术得出结论。研究发现,北京市音乐旅游资源整体上是以国家大剧院为核心,呈同心圆向外扩散分布。且均以交通为主导,沿市内环路和主要地铁线分布。音乐表演场所旅游资源依托于音乐院校、歌舞团和文化馆分布;政策性的扶持优化了音乐表演场所旅游资源的布局。音乐观光旅游资源发展依托于大型城市公园;其中,音乐文化创意产业呈集聚布局态势。音乐休闲旅游资源依托于大型商业中心和商业中心布局;且与饭店业布局规律基本一致。但整体而言,北京市音乐旅游资源分布不均匀,需要进一步优化和调整。