Spatiotemporal Structure of Taxi Services in Shanghai:Using Exploratory Spatial Data Analysis (original) (raw)
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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.
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Mobility in urban environments is complex due to numerous interacting components , with many of those that are difficult to specify. Examples include the presence of transport hubs, which connect different modes of transport, public and private. The properties of these locations include a temporally changing surface area of operational function that is heavily dependent on the complex and dynamically changing human mobility. Besides public transport services with specified stations, there are taxi services that can be associated with established transport hubs in the way of assigning spatiotemporal service hotspots. This work proposes a technique for relating taxi trip origins and destination hotspots for gaining knowledge on the spatial uncertainties of transport hubs, more precisely their movements within specific times. The case study in NYC focuses on the services of yellow taxi and boro taxi trip data on Saturdays in June 2015. The outcomes of applying the technique are matter of further investigation of spatial uncertainty perception, representation, and visualization. In the stages of the approach, the outcomes of transport hub movements are related with more general functional transport regions resulting from NYC public transport services.