Pei-yu Ren’s scientific contributions

In this study, a hybrid forecasting model based on Empirical Mode Decomposition (EMD) and Least Squares Support Vector Machines (LSSVMs) is proposed to predict tourism demand (i.e. the maximal number of arrivals in a short time interval). The proposed approach first uses EMD decompose the complicated data into a finite set of Intrinsic Mode Functions (IMFs) and a residue, then the IMF components and residue are modeled and forecasted using Least Squares Support Vector Machines, next, the forecasting values are obtained by the sum of these prediction results. In order to evaluate the performance of the proposed approach, the maximal values of tourist arrive in 1 min time interval is used as an illustrative example. Experimental results show that the proposed model outperforms the single LSSVM model without EMD preprocessing.

Based on the existed literature and in-depth interviews, this paper puts forward a theoretical model of relationship between the users’ personal motivation and UGC (User Generated Content) performance in Q&A Community (Question and Answering Community) incorporating Achievement Need Theory and Social Exchange Theory. Furthermore, 294 questionnaires are collected from a famous domestic Q&A Community for hypothesis testing and data analysis by SPSS 19.0. The results show that, achievement need, power need, affiliation need and external reward are in positive correlation with Q&A quality and Q&A quantity; while input cost is in an obvious negative correlation with Q&A quality and Q&A quantity. The affiliation need, power need and achievement can be predictors of Q&A quantity; while the affiliation need, the power need and external reward can be predictors of Q&A quality. Cost has no predictive function on the performance of the generated contents (Q&A quality or Q&A quantity).

The number of tourists to recreation areas has increased dramatically, leading to a growing concern about the congestion phenomena in these areas. Since the information provision has been introduced to reduce the congestion, its potential benefits as well as its drawbacks have been discussed controversially. In this work, we address a basic recreation area scenario with different patterns of information sharing and study the impact of them using agent-based simulation, where the tourists are modelled as agents. Three evaluation indicators are proposed to evaluate the performance of the strategies and both of the positive and negative of information sharing patterns are described.

The development of low-carbon economy put forward a new demand for the way of production and management within the field of various industries, and decarbonated development will be an inevitable mode for tourist destinations to maintain a long-term sustainable development approach. How to evaluate tourist attractions, promote their economic, ecological and social development and harmony, improve the satisfaction of scenic spot stakeholders is necessary to be discussed. So, take Jiuzhai Valley as an example, building its evaluation index system for low-carbon economy, and integrating fuzzy integral with G1 method are applied to the comprehensive evaluation of scenic spot. According to the evaluation results, some proper suggestions with validity can be proposed for the scenic area development, and these evaluation thoughts can provide references for the studying of similar tourist attractions.