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“流空间”视角下高速公路交通流网络结构特征及其形成机制——以广东省为例
引用本文:李苑君,吴旗韬,张玉玲,张虹鸥,方顺,金双泉.“流空间”视角下高速公路交通流网络结构特征及其形成机制——以广东省为例[J].地理研究,2021,40(8):2204-2219.
作者姓名:李苑君  吴旗韬  张玉玲  张虹鸥  方顺  金双泉
作者单位:1. 广东省科学院广州地理研究所 广东省遥感与地理信息系统应用重点实验室/广东省地理空间信息技术与应用公共实验室,广州 5100702. 中国科学院广州地球化学研究所,广州 5106403. 中国科学院大学,北京 1000494. 广东省交通运输规划研究中心,广州 510101
基金项目:国家自然科学基金项目(42071165);国家自然科学基金项目(41801144);国家重点研发计划(2019YFB2103101);广东省自然科学基金(2018A030313197)
摘    要:交通流是城市经济联系最显性的表现,为更清晰地识别城市网络结构提供了新视角。本研究采用高速公路联网收费数据作为交通流“关系数据”,综合图论、社会网络分析法(SNA)和GIS空间分析,以广东省为研究区域探索高速公路交通流网络空间结构特征,并利用QAP回归法探究影响网络形成的因素。结果表明:① 广东省高速公路已形成典型的网络式空间结构,具有“小世界”网络效应。其节点度值服从幂律无标度分布,交通出入量基本平衡,节点重要性自区域中心向外围递减;网络有向边权值等级差异显著,优势交通流具有“向心指向”特征和距离依赖性。② GDP、常住人口、社会消费品零售总额、县区间行驶距离等已成为影响广东省高速公路交通流网络形成的重要因素。其中人口因素是影响城市间交通流动的主要因素,网络形成受制于距离因素,消费因素逐渐成为刺激城市间交通流动的强大动力。③ 广东省高速公路交通流动模式分为邻近指向型、中心指向型和等级指向型三种,以邻近指向型为典型模式。研究结果对于揭示交通流动规律,丰富城市网络理论,推动道路合理规划和区域协调发展具有重要理论和实践意义。

关 键 词:流空间  高速公路交通流  社会网络分析  流动模式  广东省  
收稿时间:2021-01-18

Spatial structure and formation mechanism of expressway traffic flow network based on space of flows:A case study of Guangdong province
LI Yuanjun,WU Qitao,ZHANG Yuling,ZHANG Hongou,FANG Shun,JIN Shuangquan.Spatial structure and formation mechanism of expressway traffic flow network based on space of flows:A case study of Guangdong province[J].Geographical Research,2021,40(8):2204-2219.
Authors:LI Yuanjun  WU Qitao  ZHANG Yuling  ZHANG Hongou  FANG Shun  JIN Shuangquan
Institution:1. Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System / Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China2. Guangzhou Institute of Geochemistry, CAS, Guangzhou 510640, China3. University of Chinese Academy of Sciences, Beijing 100049, China4. Guangdong Provincial Transportation Planning and Research Center, Guangzhou 510101, China
Abstract:Traffic flow is the most powerful indicator to map urban economic connections, which provides a new perspective to identify urban network spatial structure clearly. This study uses Guangdong's data to explore the spatial structure of expressway network based on theory of synthesized graph, social network analysis (SNA) and GIS spatial analysis. QAP regression method is used to explore the factors which influence the network characteristics. The results show that: (1) The Guangdong's expressway forms a typical network spatial structure with “small world” network effect. The out-degree and in-degree values of network nodes follow the power law scale-free distribution, traffic volume is basically balanced, and the importance of nodes decreases from regional center to periphery. There are significant differences in the weights of directed edges, and the dominant traffic flows have the characteristics of “centripetal direction” and “distance dependence”. (2) The results of QAP regression show that the character of Guangdong's expressway traffic flow network is significantly affected by GDP, resident population, total retail sales of consumer goods, and the distance. Population is the most powerful factor which affects inter-city traffic flows, the formation of network is restricted by distance, and the consumption factor is gradually becoming a powerful driving force to stimulate the inter-city traffic flows. (3) The traffic flow patterns of expressway in Guangdong are divided into three types, namely, proximity-oriented, central-oriented and hierarchical-oriented. The proximity-oriented traffic flow is typical, which is widely distributed in the province; the central-oriented traffic flow is concentrated in the Pearl River Delta urban agglomeration; and the hierarchical-oriented traffic flow is mainly distributed between Guangzhou and Qingyuan, and between Shenzhen and Shantou. The research results have important theoretical and practical significance for revealing the laws of traffic flow, and enriching urban network theory. Furthermore, it provides theoretical support for scientific road planning and coordinated regional development.
Keywords:space of flows  expressway traffic flow  social network analysis  flow pattern  Guangdong province  
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