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

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.

城市群是中国发展的重要增长极点和核心节点，城市群交通一体化是推进城市群协调发展的首要任务之一，而对城市群内多种交通出行模式的充分认识是实现这一目标的重要基础。该文基于位置服务大数据，以粤港澳大湾区“9+2”城市群为例，分析了城市群内部通勤出行标度特征的一致性与差异性，得到以下结果：粤港澳大湾区的市内通勤仍占主导地位，但部分区域存在相当规模的跨市通勤；粤港澳大湾区通勤联系的“概率-距离”分布具有指数标度特征；对各城市而言，市内通勤的标度参数比跨市通勤更大，意味着市内通勤者对距离更加敏感；标度参数可以进一步反映交通与土地利用联系的紧密程度，进而为政策制定提供参考。