Understanding the scaling patterns of commuting in the Guangdong-Hong Kong-Macao Greater Bay Area with location-based service big data

CHEN Ruoyu; ZHOU Jiangping

doi:10.16511/j.cnki.qhdxxb.2022.26.019

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Journal of Tsinghua University(Science and Technology) ›› 2022, Vol. 62 ›› Issue (7) : 1195-1202. DOI: 10.16511/j.cnki.qhdxxb.2022.26.019

Research Article

Understanding the scaling patterns of commuting in the Guangdong-Hong Kong-Macao Greater Bay Area with location-based service big data

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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.

Key words

urban clusters / commuting travel / distance decay / transportation and land use

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CHEN Ruoyu, ZHOU Jiangping. Understanding the scaling patterns of commuting in the Guangdong-Hong Kong-Macao Greater Bay Area with location-based service big data[J]. Journal of Tsinghua University(Science and Technology). 2022, 62(7): 1195-1202 https://doi.org/10.16511/j.cnki.qhdxxb.2022.26.019

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