Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China

Li, Shaoying; Lyu, Dijiang; Huang, Guanping; Zhang, Xiaohu; Gao, Feng; Chen, Yuting; Li, Xiaoping

doi:10.1016/j.jtrangeo.2019.102631

Cited by 144 publications

(95 citation statements)

References 47 publications

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“…ere have not been any studies that apply the equal division method, but iessen polygons have been used by some researchers. For example, Li et al [22] and Sun et al [12] used iessen polygons to deal with the overlapping area of circular buffers. In the future, the equal division method could also be used to deal with the overlapping buffer area.…”

Section: Discussionmentioning

confidence: 99%

“…Former studies show that socioeconomic characteristics such as population and employment are positively correlated with transit ridership [4,17,[19][20][21]. Land use characteristics include but are not limited to 3Ds, land use density, design, diversity, and mixed land use levels [22][23][24][25][26]. Scholars found land use density and diversity have a positive impact on ridership [21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Still, there are some other methods. For example, Li et al [22] used iessen polygons to deal with the overlapping issue with the 800 meters circular buffer. When Sun et al [12] divided the multilevel water catchment area, the radius of the pedestrian and traffic area was determined by the residents' travel survey, and the division of the potential catchment area was determined by the iessen polygon.…”

Section: Literature Reviewmentioning

confidence: 99%

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How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?

Yang

et al. 2021

Journal of Advanced Transportation

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Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model’s goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?

Yang

et al. 2021

Journal of Advanced Transportation

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“…Most studies on the influence of the built environment on the choice of metro focused on meso-level transportation-related facilities and land use patterns using regression models, except for Jun et al [12] who used a discrete choice model with the sum of boarding and alighting as the dependent variable. However, other street-scale built environment variables may influence metro use [14]. To study their effect, individual-level trips and advanced quantitative methods are needed rather than the aggregate models that have been employed in the above direct ridership analyses.…”

Section: Introductionmentioning

confidence: 99%

The influence of street-scale built environment on transportation mode choice of travelers around metro stations

Liu

2021

E3S Web Conf.

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To understand the determinants of transportation mode choice of travelers around metro stations, more and more built environment attributes have been studied and shown significant results. This study focuses on the impacts of street-scale built environment on the travelers around the metro stations. 754 trips stemming from 309 respondents were collected around Yingkoudao metro station in Tianjin, China. A multinomial logit model was estimated to predict transportation mode choice to/from the study area as a function of socio-demographic information, trip characteristics, and street-scale built environment attributes (including land use mix, road width, the shortest distance to the station, sidewalk width, number of lamps, greenery area, and traffic light presence) along the shortest route between the Yingkoudao metro station and the origin/destination around the station for the choice of metro+walk. The results show that not only the socio-demographic characteristics, but also the distance to the station, land use mix, and number of lamps significantly influence the choice of metro+walk.

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“…As a first-tier city in China with a robust economy, Guangzhou has attracted highly educated people as a place of employment. The city has a noticeably heterogeneous social space [39][40][41], and there is significant spatial heterogeneity in the distribution of population by education levels [42]. In addition, Guangzhou's urban landscape and environment are diverse and complex, with significant variances [39,43,44], making it a suitable city for a case study.…”

Section: Introductionmentioning

confidence: 99%

Examining Spatial Heterogeneity Effects of Landscape and Environment on the Residential Location Choice of the Highly Educated Population in Guangzhou, China

Wang

Qin

et al. 2020

Sustainability

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The residential location choice of the highly educated population is an important consideration to construct a livable city. While landscape and environment are important factors, few studies have deeply analyzed the spatial heterogeneity effects of landscape and environment on the residential location choices of a highly educated population. Taking Guangzhou as the sample, we built a livability-oriented conceptual framework of landscape and environment, and constructed datasets for highly educated population proportion, landscape, and environment factors, and other influencing factors for Guangzhou’s 1364 communities. Global regression and geographically weighted regression (GWR) models are used for analysis. The GWR model is more effective than the global regression model. We found spatial heterogeneity in the strength and direction of the relationship between the highly educated population proportion and landscape and environment. We find that landscape and environment exert spatial heterogeneity effects on the residential location choice of the highly educated population in Guangzhou. The conclusions will be of reference value to further understand how the spatial limitations of landscape and environment affect residential location choices. This study will help city managers formulate spatially differentiated environment improvement policies, thereby increasing the city’s sustainable development capabilities.

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Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China

Cited by 144 publications

References 47 publications

How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?

How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?

The influence of street-scale built environment on transportation mode choice of travelers around metro stations

Examining Spatial Heterogeneity Effects of Landscape and Environment on the Residential Location Choice of the Highly Educated Population in Guangzhou, China

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