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Network Structure of Intercity Trips by Chinese Residents under Different Travel Modes: A Case Study of the Spring Festival Travel Rush

机译:不同旅游模式下汉居民间城区网络结构 - 以春节旅游匆忙为例

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With the advent of big data, the use of network data to characterize travel has gradually become a trend. Tencent Migration big data can fully, dynamically, immediately, and visually record the trajectories of population migrations with location-based service technology. Here, the daily population flow data of 346 cities during the Spring Festival travel rush in China were combined with different travel modes to measure the spatial structure and spatial patterns of an intercity trip network of Chinese residents. These data were then used for a comprehensive depiction of the complex relationships between the population flows of cities. The results showed that there were obvious differences in the characteristics of urban networks from the perspective of different modes of travel. The intercity flow of aviation trips showed a core-periphery structure with national hub cities as the core distribution. Trips by train showed a core-periphery structure with cities along the national railway artery as the core. This gradually decreased toward hinterland cities. Moreover, the intercity flow of highway trips indicated a spatial pattern of strong local aggregation that matched the population scale.
机译:随着大数据的出现,使用网络数据来表征旅行已经逐渐成为一种趋势。腾讯迁移大数据可以通过基于位置的服务技术完全,动态地,立即动态地,立即录制人口迁移的轨迹。在这里,在中国春节旅行中的346个城市的日常人口流量数据与不同的旅行模式相结合,以衡量中国居民的间歇性旅行网络的空间结构和空间模式。然后将这些数据用于综合描绘城市人口流量之间的复杂关系。结果表明,从不同旅行方式的角度来看,城市网络的特点存在明显差异。航空旅行的城市间流量显示了具有国家中心城市的核心周边结构,作为核心分布。乘火车旅行展示了核心周边结构,沿着国家铁路动脉作为核心的城市。这逐渐减少到腹地城市。此外,高速公路旅行的城际流动指示了与人口规模相匹配的强局部聚集的空间模式。

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