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

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (6): 1240-1253.doi: 10.12082/dqxxkx.2020.190686

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

李涛^1,², 王姣娥^2,^3,^*(), 黄洁²

1. 陕西师范大学西北国土资源研究中心,西安 710119
2. 中国科学院地理科学与资源研究所中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
3. 中国科学院大学资源与环境学院,北京 100049

收稿日期:2019-11-13 修回日期:2020-04-26 出版日期:2020-06-25 发布日期:2020-08-25
通讯作者: 王姣娥 E-mail:wangje@igsnrr.ac.cn
作者简介:李涛（1985— ）,男,山西长治人,博士后,副教授,主要从事交通运输地理研究。E-mail: taoli-2008@163.com
基金资助:
中国科学院战略性先导科技专项(A类)(XDA19040402);国家自然科学基金项目(41501120);国家自然科学基金项目(41722103);中央高校基本科研业务费项目(18SZYB01);中国科学青年人才托举工程项目(2019QNRC001)

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

LI Tao^1,², WANG Jiaoe^2,^3,^*(), HUANG Jie²

1. Northwest Land and Resource Research Center, Shaanxi Normal University, Xi'an 710119, China
2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-11-13 Revised:2020-04-26 Online:2020-06-25 Published:2020-08-25
Contact: WANG Jiaoe E-mail:wangje@igsnrr.ac.cn
Supported by:
National Natural Science Foundation of China(41501120);National Natural Science Foundation of China(41722103);The Fundamental Research Funds for the Central Universities, SNNU(18SZYB01);Young Elite Scientists Sponsorship Program by CAS(2019QNRC001)

摘要/Abstract

摘要：

城际出行具有时间依赖性,不同时间约束与特定时期的城际出行具有相异性,反映的出行模式与表达的地理空间联系规律具有差异性。迁徙大数据记录的人口移动实时记录为开展基于时间依赖的城际出行网络提供了可能。本文以全国 19个城市群为研究区域,利用腾讯平台提供的居民城际出行数据,对国庆长假期间（2016年10月1—7日）中国城市群城际出行时段变化特征、城际出行模式及其网络结构进行了研究。结果表明：① 黄金周城际出行具有明显的基于出行期、返程期和旅途期的时段变化规律;② 国庆长假期间的中国城市群城际出行分别形成了轴辐式、多中心与单中心3种城际出行模式; ③ 出行期、返程期的城际出行具有类似于春运人口流动的时空对称规律,城市群城际出行呈现出以主要城市群整体、城市群核心城市与邻近外围城市间的中短距离流动的长假出行特征,中西部城市群城际出行具有典型的“潮汐式”流动特征;④ 基于腾讯人口迁徙大数据,通过对黄金周期间出行期、返程期与旅途期的科学划分,较好地实现了国庆长假城际出行特征与模式的挖掘,同时也为长假城际交通管理与道路资源优化调配方案的制定提供支撑。

关键词: 城际出行, 出行模式, 网络结构, 时间依赖, 腾讯迁徙, 大数据, 国庆黄金周, 城市群

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

李涛, 王姣娥, 黄洁. 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[J]. 地球信息科学学报, 2020, 22(6): 1240-1253.DOI:10.12082/dqxxkx.2020.190686

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

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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

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

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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