Abstract
Public transport plays an important role in developing sustainable cities. A better understanding of how different public transit modes (bus, metro, and taxi) interact with each other will provide better sustainable strategies to transport and urban planners. However, most existing studies are either limited to small-scale surveys or focused on the identification of general interaction patterns during times of regular traffic. Transient demographic changes in a city (i.e., many people moving out and in) can lead to significant changes in such interaction patterns and provide a useful context for better investigating the changes in these patterns. Despite that, little has been done to explore how such interaction patterns change and how they are linked to the built environment from the perspective of transient demographic changes using urban big data. In this paper, the tap-in-tap-out smart card data of bus/metro and taxi GPS trajectory data before and after the Chinese Spring Festival in Shenzhen, China, are used to explore such interaction patterns. A time-series clustering method and an elasticity change index (ECI) are adopted to detect the changing transit mode patterns and the underlying dynamics. The findings indicate that the interactions between different transit modes vary over space and time and are competitive or complementary in different parts of the city. Both ordinary least-squares (OLS) and geographically weighted regression (GWR) models with built environment variables are used to reveal the impact of changes in different transit modes on ECIs and their linkage with the built environment. The results of this study will contribute to the planning and design of multi-modal transport services.
Highlights
As urbanization accelerates, urban and transportation planners have attempted to improve the sustainability of cities from the perspectives of urban design and urban transportation [1,2]
The research questions are as follows: (1) How to better evaluate the interaction patterns of multiple transport modes on the basis of the changes in travel patterns caused by transient demographic changes associated with the Spring Festival in a city? (2) How are such patterns related to the urban built environment? To answer these two questions, we first apply a clustering-based method to identify the interaction patterns of multiple transport modes and compare them for two specific periods between which significant demographic change occurred
The findings reveal that the interaction patterns of the public transit modes are dynamic, and transportation during the Spring Festival affects these patterns and their relationship with the built environment
Summary
Urban and transportation planners have attempted to improve the sustainability of cities from the perspectives of urban design and urban transportation [1,2]. This phenomenon is evident in Shenzhen, China, which is a young city with a large number of migrant workers It is still unclear whether and how the large-scale human movement and transient demographic change associated with the Spring Festival influence the interaction patterns of multi-modal transport. The research questions are as follows: (1) How to better evaluate the interaction patterns of multiple transport modes on the basis of the changes in travel patterns caused by transient demographic changes associated with the Spring Festival in a city? The findings reveal that the interaction patterns of the public transit modes are dynamic, and transportation during the Spring Festival affects these patterns and their relationship with the built environment These results shed light on the planning and design of multi-modal transportation services. We present our conclusions in the final section and suggest directions for future work
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