Interactions between Bus, Metro, and Taxi Use before and after the Chinese Spring Festival

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.

Analysis of urban agglomeration structure through spatial network and mobile phone data

“…Current spatial network analysis techniques are convenient for exploring the structure of urban agglomerations because they are often tied to mobility‐related big data (e.g., taxi GPS trajectories, social media, smart card data) that can be used to examine spatial interaction patterns (Huang et al, 2019; Liu, Kang, Gao, Xiao, & Tian, 2012; Liu, Sui, Kang, & Gao, 2014). However, the use of spatial networks via mobility big data presents a level of uncertainty, as the revealed spatial structure of an urban agglomeration may be affected by the source of the data.…”

The superspreading places of COVID-19 and the associated built-environment and socio-demographic features: A study using a spatial network framework and individual-level activity data

“…Further, most people travel to different places in their daily life for various activities ( Kwan, 2012 ), and people's daily mobility outside their homes may not be reduced since they still have to obtain groceries, medicines, essential services, or go to work during a pandemic ( Huang et al, 2020 ). Thus, people's daily mobility is also shaped by various built-environment and socio-demographic features, which may in turn render some places more risky than others ( Hutch et al, 2011 ; Huang et al, 2019 ; Lai et al, 2020 ). For instance, using contact tracing data collected in Hong Kong, Huang et al (2020) , Kan et al (2021a , b) , Kwok et al (2021) , and Yip et al (2021) found that certain socio-demographic features (e.g., population density, household income, workplace location, and occupation) and built-environment features (e.g., green space, sky view, building density and height, transport nodal accessibility, land use configuration, and street length) significantly affected the spatial patterns of COVID-19 transmission in Hong Kong.…”

“…Previous studies have used human mobility data (e.g., smart card data) and spatial networks to examine superspreading places (SSPs). In a spatial network framework, the spatial units (e.g., public transport stations, census tracts, counties) are represented as nodes , and the intensity of human movements between the nodes are presented as weighted edges that indicate the volume of movements among the nodes ( Rizzo et al, 2014 ; Huang et al, 2019 ; Kan et al, 2021c ; Liu et al, 2021 ). In a spatial network, the degree of a node indicates that the number of edges connected to it; the strength of a node refers to the weights of all edges (i.e., the intensity of human movements) connected to it.…”

Automatic Identification of the Social Functions of Areas of Interest (AOIs) Using the Standard Hour-Day-Spectrum Approach

“…Apart from the abovementioned data sets, travel data such as currency data [28], mobile phone data [29,33], subway bus card data [34] and floating car data [35,36] are widely used in the analysis of travel behaviors. Among these, the trajectory data of taxicabs record the spatial location more closely to the destination, which is more suitable for fine-grained research.…”