Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map
Abstract
:1. Introduction
2. Research Methodology
2.1. Study Area and Data
2.1.1. Study Area
2.1.2. Data
2.2. Construction of Networks
2.3. Theil Index
2.4. Use of Baidu Maps API to Obtain Time Distance
2.5. Weighted Average Travel Time
2.6. Community Detection Model of a Multiple Traffic Flow Network
2.6.1. Construction of Multiple Traffic Flow Networks
2.6.2. Community Detection Model
3. Results and Analysis
3.1. Overall Spatial Differentiation Characteristics of the Network
3.2. Accessibility Pattern of Multiple Traffic Flow Networks
- (1)
- The accessibility pattern based on the highway network presents a “core–edge” spatial pattern in space, and the average weighted travel time is 12.31 h. In general, the high-accessibility core area (8.60~10.08 h) is mainly concentrated in the “Two Lakes” and Hangzhou areas, radiating to the east and west wings. The medium-accessibility area (10.26~14.79 h) is mainly concentrated in the Chengdu–Chongqing metropolitan area. Due to the special topography of the Hengduan Mountains and the Yunnan-Guizhou Plateau in the western part of the Economic Belt, the low-accessibility area (18.05~24.04 h) is mainly concentrated in the Diqing Tibetan Autonomous Prefecture, Nujiang Lisu Autonomous Prefecture, and Xishuangbanna Dai Autonomous Prefecture in western Yunnan.
- (2)
- The accessibility pattern based on the railway network generally presents a spatial pattern of “strong in the east and weak in the west”, and the average weighted travel time is 13.09 h. With the advent of high-speed rail, the travel time cost between the core cities in the Yangtze River Economic Belt and the surrounding small- and medium-sized cities has been significantly reduced, dividing it into two parts: the eastern and western parts. The eastern region has a clear radiation trend to the Yangtze River Delta region, while the western region is centered on Chongqing and Guiyang and extends along the east and west sides. In general, the core areas of high accessibility (7.35~10.97 h) are mainly concentrated in the central and eastern provincial capitals, such as Wuhan, Changsha, Hefei, and Nanjing. The areas of medium accessibility (11.12~17.40 h) are mainly concentrated in the middle reaches of the Yangtze River and the urban agglomeration of Chengdu and Chongqing. Due to the special topography of the Hengduan Mountains and Yunnan-Guizhou Plateau in the western part of the Economic Belt, the railway network is relatively sparse, and the areas of low accessibility (18.04~32.08 h) are mainly concentrated in Diqing Tibetan Autonomous Prefecture, Nujiang Lisu Autonomous Prefecture, and Baoshan in western Yunnan.
- (3)
- The accessibility pattern based on the aviation network presents a spatial pattern of “time and space compression in western cities”. Compared with the sparse road and rail network, the average weighted travel time is improved to 7.4 h. In general, the high-accessibility core area (4.97~5.99 h) is mainly concentrated in provincial capitals and ethnic minority autonomous prefectures with tourist attractions. The medium-accessibility area (6.08~8.80 h) is mainly concentrated in the Yunnan-Guizhou region, of which Guizhou is affected by natural factors such as topography and landforms, it is the only province in the country where every city (state) has an airport, forming a provincial airport cluster. The low-accessibility area (9.04~11.51 h) is mainly concentrated in Tongling, Anshun City, Suzhou City, and Bengbu City in Anhui Province in the eastern region, as well as Aba Tibetan, Qiang Autonomous Prefecture, and Garze Tibetan Autonomous Prefecture in western Sichuan.
3.3. Hierarchical Structure and Effects of Network Communities
3.3.1. Hierarchical Structures of Clusters
3.3.2. Regional Effects of City Networks
3.4. Urban Cyberspace Organization Pattern
4. Conclusions and Implications
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Nodes | Total Links | Intra-Provincial Links | Inter-Provincial Links | Intra-Regional Links | Inter-Regional Links | |
---|---|---|---|---|---|---|
Highway | 128 | 3022 | 1231 | 1791 | 2136 | 886 |
Railway | 121 | 5949 | 1097 | 4852 | 2838 | 3111 |
Aviation | 70 | 946 | 66 | 880 | 236 | 710 |
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Liang, J.; Xie, S.; Bao, J. Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map. Sustainability 2024, 16, 1300. https://doi.org/10.3390/su16031300
Liang J, Xie S, Bao J. Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map. Sustainability. 2024; 16(3):1300. https://doi.org/10.3390/su16031300
Chicago/Turabian StyleLiang, Juanzhu, Shunyi Xie, and Jinjian Bao. 2024. "Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map" Sustainability 16, no. 3: 1300. https://doi.org/10.3390/su16031300