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 pap...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.展开更多
为了研究出行者对旅游出行服务的使用意向,基于出行即服务(mobility as a service, MaaS)环境考虑心理需求构建了服务接受模型(service acceptance model, SAM)。通过网络问卷调查方法获取MaaS影响因素的主观评价数据,基于结构方程模型...为了研究出行者对旅游出行服务的使用意向,基于出行即服务(mobility as a service, MaaS)环境考虑心理需求构建了服务接受模型(service acceptance model, SAM)。通过网络问卷调查方法获取MaaS影响因素的主观评价数据,基于结构方程模型检验了SAM的假设及其内部一致性、可靠性、收敛效度和判别效度。结果表明:感知有用性对出行者使用意向的正向影响最大,感知易用性次之,而感知风险呈负向作用;感知有用性和感知易用性影响出行者的习惯模式一致性,进而间接影响出行者使用意向;心理需求满足对使用意向具有重要作用,出行者的自主性、能力、关联性及利他性等心理需求因素影响感知有用性、感知易用性和感知风险,进而间接影响出行者使用意向;SAM可有效地描述旅游出行服务使用意向,并为异质性出行者在制定精细化、差异化策略时提供支持。展开更多
文摘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.
文摘为了研究出行者对旅游出行服务的使用意向,基于出行即服务(mobility as a service, MaaS)环境考虑心理需求构建了服务接受模型(service acceptance model, SAM)。通过网络问卷调查方法获取MaaS影响因素的主观评价数据,基于结构方程模型检验了SAM的假设及其内部一致性、可靠性、收敛效度和判别效度。结果表明:感知有用性对出行者使用意向的正向影响最大,感知易用性次之,而感知风险呈负向作用;感知有用性和感知易用性影响出行者的习惯模式一致性,进而间接影响出行者使用意向;心理需求满足对使用意向具有重要作用,出行者的自主性、能力、关联性及利他性等心理需求因素影响感知有用性、感知易用性和感知风险,进而间接影响出行者使用意向;SAM可有效地描述旅游出行服务使用意向,并为异质性出行者在制定精细化、差异化策略时提供支持。
