旅游网站访问者行为的时间分布及导引分析

The Time Distribution and Guide Analysis of Visiting Behavior of Tourism Website Users

运用多种网上查询系统和网站访问量统计工具,获取了旅游网站访问者时间分布的较详细资料,基此总结了旅游网站访问者日内、周内、年内行为的时间分布特征,进而讨论了旅游网站访问者人数与景区旅游者人数之间的相关性及其信息流对人流的导引作用。研究发现:旅游网站访问者日内行为的基本规律是自身表现为一种双峰状态且国内北方显著于南方,其在使用时段上与整体互联网及其主要网站类型的“尖峰时段”特征有较大差异,日内旅游网站访问者人数与景区旅游者人数时间分布的基本一致性说明信息流对人流不具导引作用;旅游网站访问者周内行为的基本规律是自身呈现出周末较少平日较多的特征,与整体互联网及与互联网其他类型的网站比较既有相同性也有差异性,旅游网站平日访问量较高周末访问量较低的特征与周内旅游人数平日较少周末较多的“Z”型分布呈互补状;旅游网站访问者年内行为的基本规律是依据旅游网站的地域性明显与否而形成国内南北方的多种类型,其访问者的时间行为与旅游者人数波动的紧密关系是由自然季节因素的,旅游地旅游网站年内访问者人数走势与该旅游地旅游者人数走势表现出的波涟状特征可解释为信息流对人流发生导引作用。

This paper obtained detailed data from many kinds of online survey systems and website traffic statistic tools. The data are summarized using time-distributing characteristics of the browsing behavior of tourism website visitors on daily, weekly and yearly bases. Under this condition, this paper analyzed the relationship of the traffic of tourism websites and the tourists. Four questions are researched： （1） the self characteristics of website, the comparison of China and foreign countries, and the comparison of southern and northern regions in China; （2） the comparison between the whole internet browsing with different kinds of websites browsing; （3） the comparison of the internet and tradition media; and （4） the guide of tourism website information flow to realistic tourists flow. The time-distributing characteristics of the browsing behavior of tourism website visitors on daily, weekly and yearly bases： Daily： （1） the characteristic is bimodal distribution, 10：00 am and 14：00 pm are the summit, and 20：00-22：00 pm is high frequency stage. The browsing time variation is different in China and other countries, and in northern and southern China. There is close connection in browsing behavior of users and their habits. （2） There are great differences in the browsing time-stage of tourism websites, the whole lnternet and the main websites. （3） The time characteristic and reason of browsing behavior of users can be explained from the deep level of users＇ identity variable. （4） The browsing behavior of tourism websites is different from the looking and hearing behavior of TV media： the time-distributing of net media is dispersive, and the net-usage rate is higher beyond the golden stage. （5） Overall, the tourist traffic daily is the same with the usage trend of tourism websites. From time to time, the information flow does not guide to people flow. Weekly： （1） the self characteristic has no obvious difference in China and foreign countries, and in southern and northern regions. The time distribution of browsing behavior of users is low at weekends and high on weekdays. （2） There are similar characteristics and different characteristics when comparing the whole internet with other types of websites. The usage rate is low at weekends and high on weekdays, which is the same compared with other type websites, mainly because the users get some outdoor activities such as sport. （3） The usage type of tourism website appears ＂Z-shaped＂ distribution pattern, and the using trend of websites is complementary each other with tourist distribution. The browsing traffic of tourism website is high on weekdays and low at weekends, and people flow is low on weekdays and high at weekends. Yearly： （1） the self characteristic is the complicated multiple characteristic type. There are obvious differences between China and other countries, and between southern and northern regions. The traffic of websites that has no obvious regionality is the highest before golden week yearly, in other time stage, the cycle is week and the changing of traffic is little. The traffic of websites that has obvious regionality is one-peak distribution type yearly. （2） The browsing time behavior and the fluctuation of tourist flow with time have close connections. The season influences the seasonal distribution of tourist flow, and influences the seasonal distribution of the usage of tourism websites. The society season produces the splice effect. It causes the yearly traffic trend of tourism websites and tourist flow trend wavy, which is the guidance of information flow to people flow of tourism websites.