目的地区域内旅游线路模式及客流影响因素研究

Travel Itinerary Patterns and Factors Affecting Tourist Flow within Destination District

基于大样本数据的分析有助于深入揭示游客在目的地区域内迁移模式与扩散的空间特征。文章以江苏省为例,研究游客在省域尺度的区域内聚和扩散,对多节点线路距离和游客移动步长分布特征进行了刻画,使用最优标度回归模型分析了多种因素对各景区接待不同移动模式游客量的影响程度。结果表明:(1)外来游客在江苏省内的多节点模式空间距离呈现指数型分布规律,相应距离模式上的游客量呈现幂指数分布,游客在高等级景区间转移的移动步长呈现指数分布;(2)省域客流内聚于南京、苏州、扬州与无锡的三角地带,“烟花三月下扬州”的季节性使得客流在苏中地区较为活跃;(3)景区等级、创建年限、所在城市和距离市中心距离等因素对游客的不同移动模式选择有普遍的显著影响,在重要性程度上差异明显。多节点模式游客偏好高价门票型景区,受景区区位便利性影响更强,而单节点模式受景区面积和季节性影响较大,高等级景区和高满意度会促使游客继续游览下一节点。文章在大数据支持下总结了游客移动规律特征,对比不同模式客流偏好的多因素影响机制,有助于深入大尺度目的地区域内部以景区为节点的游客空间行为研究,也为在实践中具体评价游线质量,优化景区间交通体系,并促进目的地全域协调发展提供了应用方法参考。

Large-sample data are helpful in determining the spatial characteristics of tourist movement patterns and diffusion at tourism destinations. This paper describes the tour distance distribution and displacement distribution characteristics of a multi-node model for tourists. We applied the regional cohesion and diffusion of tourist flow from outer areas of a Chinese province(Jiangsu) to its cities to analyze various models with respect to optimal scale regression. We also investigated the effect of various factors on the number of tourists who adopted different travel patterns at a number of scenic spots;we undertook a comparative analysis of tourist preferences.This paper found that the distance of the multi-node movement of tourists in Jiangsu province followed an exponential distribution;the number of tourists on the corresponding distance mode showed a power-law distribution. The tourist flow was concentrated in the triangular area of Nanjing,Suzhou, and Yangzhou;it followed the seasonal characteristics of“Heading east for Yangzhou among hazy vernal hues”, which led to a concentrated tourist flow in central Jiangsu in April. The attractiveness of scenic spots, the age of establishments catering to tourists, the city where such establishments were located, and the distance from the city center had an extensive, significant effect on the tourists’ choice of different mobility modes;however, the importance of those factors varied conspicuously. In the multi-node model, we considered travel ticket prices when we selected tourists and nodes for inflow. However, in contrast to received opinion, such ticket prices had a positive effect on the passenger flow coefficient for the scenic spots. The tourist flow to scenic areas decreased with distance from the city center. The effect on attenuation for multi-node tourists(-0.310) exceeded that for single-node tourists(-0.293);the restrictive effect on the attraction for tourists(-0.324) exceeded the outward diffusion of the scenic area(-0.268). Factors such as distance from the city center and attractiveness of the scenic spots and the city generally displayed great importance. In contrast to multinode travel, single-node travel was characterized by greater attention being devoted to the attractiveness of scenic spots(importance, 0.318) and was less affected by the attractiveness of the city.After visiting a very attractive scenic spot or obtaining a high degree of satisfaction, tourists would be motivated to continue their visit for the next node.Our results provide support for policy makers to better allocate and manage the spatial optimization of tourism resources in popular spots and regions with greater tourist potential. Clarifying tourist preferences and the associated influencing factors would help tourism marketing agencies and service enterprises conduct targeted optimization in their travel itineraries.