疫情背景下中国居民城际出行恢复力、恢复模式与影响因素

新冠疫情爆发以来,居民城际出行受到显著影响,其时空波动规律反映了居民城际出行恢复力和恢复模式。文章基于百度迁徙数据,着眼于疫情防控常态化阶段,分析城际出行恢复力的分异格局,归纳总结时序波动规律与模式,并构建计量模型探究影响城际出行恢复力的因素。研究构建了波动比率、恢复比率、恢复弹性和恢复指数4个指标,用以衡量城际出行恢复力大小;将2021年中国新冠疫情划分为4个波次,各轮疫情持续时长不一,涉及地区范围各异。研究发现：① 居民城际出行恢复力表现出一定的空间差异,东部地区最好,西部地区和中部地区其次,东北地区最差;② 居民城际出行恢复模式时序与传统韧性三角形模式相似,根据疫情传播特征和性质具体可归纳为相对独立型、中间波动型、起点关联型、终点关联型、双向受制型等5种模式,表现出各异的曲线形态和特征;③ 对于居民城际出行恢复力的影响因素,机场、高铁等交通因素具有正向相关关系,而与GDP、产业结构等经济因素的影响表现为U型关系。疫情防控背景下,城际出行恢复模式和恢复力是城市韧性的重要方面,为制定相关城市政策提供了科学依据。

Intercity Travel Resilience,Recovery Patterns and Influencing Factors of Resilience in Chinese Cities in the Context of COVID-19

Since the outbreak of the COVID-19 epidemic in 2020, the intercity travel in China has been significantly affected. With the popularity of big data, spatiotemporal modeling and analysis are widely used in epidemic and transportation research. In the post epidemic era, residents' intercity travel shows a certain recovery mode under the influence of local epidemic. The recovery mode and resilience of intercity travel reflects the resilience of cities and can provide information for cities' epidemic prevention and control. Exploring different urban modes and factors affecting the resilience of intercity travel under the influence of epidemic situation has practical significance for normalized epidemic prevention and control management. Based on the migration big data, this paper describes the differentiation pattern of intercity travel resilience under the COVID-19 epidemic from different perspectives, summarizes the time series model, and explores the factors affecting intercity travel resilience. Four indicators, namely fluctuation ratio, recovery ratio, resilience, and recovery index, are constructed to measure the resilience of intercity travel. The results show that: (1) During the epidemic period, residents' resilience to travel shows certain spatial variation. On the whole, the eastern region is the best, followed by the western region and the central region, and the northeast region is the worst; (2) The temporal patterns of intercity travel in epidemic cities are consistent with "resilience triangle" of the typical model. According to the propagation mode and correlation of the epidemic, the specific temporal patterns can be classified into five types: Relative independence mode, intermediate fluctuation mode, starting-point correlation mode, end-point correlation mode, and bidirectional restraint mode, showing different curve forms and characteristics; (3) The resilience of intercity travel is affected by complex factors. When the epidemic wave and regional variables are controlled, economic and transportation factors have a significant impact on the recovery of intercity travel. There may be a U-shaped relationship between per capita GDP and industrial structure and the resilience of intercity travel. When the economic development reaches a certain level, the supporting effect of economy on the resilience of intercity travel becomes increasingly prominent. There is a positive correlation between high-speed rail and airport and the resilience of intercity travel, which plays an important role in increasing the resilience of intercity travel. The results of this study indicate that the application of spatiotemporal big data to analyze the mode and mechanism of urban recovery in the post epidemic era is a novel research method. Subsequent research can further explore the spatiotemporal pattern and mode mechanism of epidemic recovery, in order to provide scientific basis and guidance for epidemic prevention and control of cities.