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Understanding the scaling patterns of commuting in the Guangdong-Hong Kong-Macao Greater Bay Area with location-based service big data |
| CHEN Ruoyu1,2,3, ZHOU Jiangping1,2,4 |
1. Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen 518057, China;
2. Guangdong-Hong Kong-Macao Joint Laboratory on Smart Cities, Hong Kong 999077, China;
3. School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China;
4. Faculty of Architecture, The University of Hong Kong, Hong Kong 999077, China |
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Abstract Urban clusters are important in China's development. The integration of transportation systems in these urban clusters is one of the most important tasks for promoting the development of these clusters, which requires a full understanding of the current travel patterns. Location-based service (LBS) big data for the Guangdong-Hong Kong-Macao Greater Bay Area was analyzed to identify the commuter travel scaling patterns. The results show that most commuting trips are still inside the cities, but there are sizable inter-city commutes in some areas. The results also show that the probability density of the commute distance is exponentially related to the distance. In addition, the scaling parameter for intra-city commuting is generally larger than that of inter-city commuting, which indicates that trips within cities are more sensitive to the commuting distance. The scaling parameter further reflects the connections between the transportation and land use, which can be useful for policy making.
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| Keywords
urban clusters
commuting travel
distance decay
transportation and land use
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Issue Date: 16 June 2022
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2022,
Vol. 62
