多元交通视角下异质旅游流分布特征及其空间共轭关系

异质性是旅游流研究亟待拓展的内容。以北京、武汉和西安为案例客源地，基于2017年获取的城市居民出游行为大样本问卷数据，利用数理统计法、地图分析法等方法，从客源地视角对自驾车、火车、飞机3种交通方式旅游流的分布特征进行研究，研究发现：① 随着出游交通时间的增加，自驾旅游流呈指数衰减，火车和飞机旅游流呈正偏态分布。② 3种交通方式旅游流的空间分布格局存在差异。自驾高到访率区域围绕在客源城市周围；火车与飞机高到访率区域远离客源城市。③ 不同交通方式黄金出游空间具有共轭性，距客源城市球面距离0~400 km区域为自驾黄金出游空间，400~1 200 km乘火车（包括动车和高铁）6.00 h可达区域为火车黄金出游空间，1 200~2 600 km区域为飞机黄金出游空间。基于研究结果，提出了异质交通方式黄金出游空间共轭模型，刻画了自驾车、火车、飞机3种主要交通方式黄金出游空间的位置和共轭关系。

Distribution Characteristics and Spatial Conjugate Relationship of Heterogeneous Tourist Flows from the Perspective of Multiple Transportation

Tourist flow is mostly regarded as homogeneous and its heterogeneity is ignored in the studies available. Based on the questionnaire data of a large sample of urban residents’ travel from Beijing, Xi'an and Wuhan of China in 2017, this paper divided the tourist flow into three types by different modes of transportation, which were, self-driving, train and plane, from the perspective of tourist origin, and investigated their spatial distribution characteristics based on the methods of mathematical statistics and map analysis. The results are as follows: 1) With the increase of traffic time between the tourist origin and destination, the self-driving tourist flow is exponentially decreasing in distribution, while the train tourist flow and plane tourist flow are in a positively skewed distribution that rises first and then falls. 2) The tourist flows of the three modes of transportation differ in their spatial distribution patterns. The scenic spots with high visiting rate by self-driving are around the tourist origin cities, characterized by continuity and gradual change; most of the scenic spots with high visiting rate by train or plane are far away from the tourist origin cities, characterized by wide dispersion and small contiguous areas. Scenic spots with high visiting rate by train are mostly well-known attractions in economically developed regions within the spherical distance of 400-1 200 km from the tourist origin, while scenic spots with high visiting rate by plane are mostly well-known attractions in economically developed regions beyond the spherical distance of 800 km. 3) The prime travel space by different modes of transportation is conjugate, the area with a spherical distance of 0-400 km from the tourist origin cities is the prime space for self-driving travelling, the area within 400-1 200 km that can be reached by train within 6 h is the prime space for travelling by train, and the area within 1 200-2 600 km is the prime space for travelling by plane. This paper puts forward a Conjugate model of the prime travel space for heterogeneous transportation modes. This model considers the proportional structure of tourist flows by different modes of transportation under different distance. Besides, the law of spatial distribution of different tourist flows and tiring time by travelling in different modes of transportation are also included. This model ascertains the locations of the prime travel space by different modes of transportation in a tourism system centered on the tourist origin, and reveals conjugate connection among the prime travel space for different modes of transportation, which is the macro-geographical expression of the spatial distribution of tourist flows by heterogeneous transportation modes.