基于腾讯迁徙数据的中国城市群国庆长假城际出行模式与网络特征

doi:10.12082/dqxxkx.2020.190686

Abstract

Abstract:

Intercity travel is a time-dependent behavior, which has different spatial characteristics with different time constraints or during different time periods. The patterns of intercity travel and geographical spatial connections revealed by intercity travel could be different with time. However, intercity travel with varying travel time has been studied little so far, in particular for holiday travel. With the booming of holiday tourism, analyzing intercity travel during holidays is of great significance to uncover the spatial movement rules and travel patterns among urban agglomerations. In the era of big data, real-time records of population movement provide a possibility to examine the characteristics of intercity travel in detail. Hence, this paper explores the characteristics, patterns, and structure of intercity travel in 19 urban agglomerations of China during the National Day holiday period (October 1-7) in 2016. The intercity travel data derived from the Tencent Location Big Data and network analysis methods are employed to evaluate intercity travel patterns between urban agglomerations. Using the community detection method, we identify 24 city communities during the National Day holiday, and the directions of intercity travel in urban agglomerations are explored. Results show that intercity travel during this golden week has an obvious timing feature, which is observed as leave period, return period, and journey period receptively. There have formed three intercity travel patterns, namely, hub-and-spoke, polycentric, and monocentric patterns. Meanwhile, the features of intercity travel in the leave period and return period are similar to the Spring Festival, which is characterized by space-time symmetry of population flow. Intercity travel in main urban agglomerations presents a typical long-holiday travel feature, which is characterized by short- and medium- distance travel between core cities and neighboring peripheral cities. While the intercity travel in urban agglomerations in the central and west of China has a typical tidal feature. Based on the population movement records from the Tencent Location Platform, this study has investigated intercity travel features and travel patterns during the National Day holiday in three time periods mentioned above. In addition, our results can provide useful support for intercity traffic management, road resource optimization, and allocation plan in long holidays in China.

Key words: intercity travel, travel pattern, network structure, time dependent, Tencent Migration, big data, National Day holiday, urban agglomeration

LI Tao, WANG Jiaoe, HUANG Jie. Research on Travel Pattern and Network Characteristics of Inter-city Travel in China's Urban Agglomeration during the National Day Week based on Tencent Migration Data[J].Journal of Geo-information Science, 2020, 22(6): 1240-1253.DOI:10.12082/dqxxkx.2020.190686

Figures/Tables 12

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References 32

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	函数	函数公式	调整判定系数R²
对内联系	幂函数	I_ij=0.1879×O_outiD_inj/d_ij^0.706	0.510^*
	修正幂函数	I_ij=0.2337×O_outi^0.689D_inj^0.563/d_ij^0.733	0763^**
	指数函数	I_ij=0.1110×O_outiD_inj/e^0.456^dij	0.423
	修正指数函数	I_ij=0.1586×O_outi^0.563D_inj^0.456/e^0.533^dij	0.485
对外联系	幂函数	I_ij=0.2333×O_outiD_inj/d_ij^0.825	0.633^*
	修正幂函数	I_ij=0.3065×O_outi^0.429D_inj^0.333/d_ij^0.896	0.802^**
	指数函数	I_ij=0.2001×O_outiD_inj/e^0.700^dij	0.496^*
	修正指数函数	I_ij=0.2045×O_outi^0.436D_inj^0.433/e^0.706^dij	0.502

	强联系及占比	次强联系及占比	弱联系及占比	对称联系及占比	平均首位联系强度
	（≥0.6）	（<0.6, ≥0.3）	（<0.3）	（互为首位联系）	平均首位联系强度
对内联系	47（21.17）	102（46.15）	42（18.92）	30（13.51）	0.46
对外联系	25（11.42）	139（63.47）	52（22.42）	6（2.74）	0.42

Research on Travel Pattern and Network Characteristics of Inter-city Travel in China's Urban Agglomeration during the National Day Week based on Tencent Migration Data

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