The development status of rural resident tourism market is introduced in this study,and its potential influencing factors are analyzed.With coastal areas as the study case,the future development trend of rural residen...The development status of rural resident tourism market is introduced in this study,and its potential influencing factors are analyzed.With coastal areas as the study case,the future development trend of rural resident tourism market in China is predicted from the aspects of market scale,consumption pattern,tourism level and tourism season,moreover,countermeasures are put forward for the healthy and sustainable development of rural resident tourism market.展开更多
SUN Gen nian (Shaanxi Normal University, Xi’an 710062,P.R.China) ABSTRACE:Modern inbound tourism in China has been developing for 20 years, a trend line of inbound tourists in statistical data began to show. This pap...SUN Gen nian (Shaanxi Normal University, Xi’an 710062,P.R.China) ABSTRACE:Modern inbound tourism in China has been developing for 20 years, a trend line of inbound tourists in statistical data began to show. This paper introduces the concept of tourism background trend line, and explores its two functions as a barometer in demonstrating fluctuation in the tourism economy and as a forecaster in forecasting tourism development. The tourism background trend line is a new concept, the word "background" derived from environment science, refers to the trend line" which reflects the dynamic curve or dynamic equation of tourism development without considering the impact of unexpected incidents. The introduction of this concept was inspired by Karl Marx’s comments on the relation between value and price. Tourism background trend line reflects the summary of multiple factors involving tourism resources, tourism demand, population growth, the scale and speed of economic development, and the spatial interaction between tourism origins and destimations. It demonstrates the natural and stable trend and the temporal law of tourism development in a country or region. The tourism statistical curve is at random, susceptible to disruptions and disturbances from serious political, economic and environmental happenings, but it always fluctuates around the background line. Tourism background line can reveal the potential of a country’s tourism development. Compared with the statistical line, it can be used as a barometer" indicating ups and downs of tourism industry in the past. When naturally extended, the background trend line also can be used for forecasting the trend of tourism development in future. In this paper, 4 tourism background trend lines of China’s inbound tourists, i.e. foreign tourists, Hong Kong/Macao/Taiwan tourists, overseas Chinese tourists and total tourists from abroad, were established with statistical data from 1978 to 1996. And the impacts of the Political Event in 1989(or Tiananmen Square Incident) on China’s inbound tourism were evaluated. The result shows that the impact of the Event was not limited within one year, but it stretched over 3 years. The total loss was 20 million in tourist arrivals and ＄ 1620 million in foreign currency income. The paper also studied the trend of China’s inbound tourism in the next 4 years.展开更多
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
文摘The development status of rural resident tourism market is introduced in this study,and its potential influencing factors are analyzed.With coastal areas as the study case,the future development trend of rural resident tourism market in China is predicted from the aspects of market scale,consumption pattern,tourism level and tourism season,moreover,countermeasures are put forward for the healthy and sustainable development of rural resident tourism market.
基金Under the auspices of National Natural Science Foundation of China.
文摘SUN Gen nian (Shaanxi Normal University, Xi’an 710062,P.R.China) ABSTRACE:Modern inbound tourism in China has been developing for 20 years, a trend line of inbound tourists in statistical data began to show. This paper introduces the concept of tourism background trend line, and explores its two functions as a barometer in demonstrating fluctuation in the tourism economy and as a forecaster in forecasting tourism development. The tourism background trend line is a new concept, the word "background" derived from environment science, refers to the trend line" which reflects the dynamic curve or dynamic equation of tourism development without considering the impact of unexpected incidents. The introduction of this concept was inspired by Karl Marx’s comments on the relation between value and price. Tourism background trend line reflects the summary of multiple factors involving tourism resources, tourism demand, population growth, the scale and speed of economic development, and the spatial interaction between tourism origins and destimations. It demonstrates the natural and stable trend and the temporal law of tourism development in a country or region. The tourism statistical curve is at random, susceptible to disruptions and disturbances from serious political, economic and environmental happenings, but it always fluctuates around the background line. Tourism background line can reveal the potential of a country’s tourism development. Compared with the statistical line, it can be used as a barometer" indicating ups and downs of tourism industry in the past. When naturally extended, the background trend line also can be used for forecasting the trend of tourism development in future. In this paper, 4 tourism background trend lines of China’s inbound tourists, i.e. foreign tourists, Hong Kong/Macao/Taiwan tourists, overseas Chinese tourists and total tourists from abroad, were established with statistical data from 1978 to 1996. And the impacts of the Political Event in 1989(or Tiananmen Square Incident) on China’s inbound tourism were evaluated. The result shows that the impact of the Event was not limited within one year, but it stretched over 3 years. The total loss was 20 million in tourist arrivals and ＄ 1620 million in foreign currency income. The paper also studied the trend of China’s inbound tourism in the next 4 years.
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.
