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The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou
الگوهای مختلف تفریحی حمل و نقل ریلی و روابط آنها با عوامل محیطی ساخته شده در مقیاس خوب: تجزیه و تحلیل داده های بزرگ از گوانگژو-2020 Investigating the varying ridership patterns of rail transit ridership and their influencing factors at the station
level is essential for station planning, urban planning, and passenger flow management. Although many studies
have investigated the associations between rail transit ridership and built environment, few studies combined
spatial big data to characterize the built environment factors at a fine scale and linked those factors with the
varying patterns of rail transit ridership. In this study, we characterized the fine-scale built environment factors
in the central urban area of Guangzhou, China, by integrating multi-source geospatial big data including Tencent
user data, building footprint and stories, points of interest (POI) data and Google Earth high-resolution images.
Six direct ridership models (DRMs) based on the backward stepwise regression method were built to compare the
different effects between daily, temporal and directional ridership. The results indicated that number of station
entrances/exits and transfer dummy, were positively associated with rail transit ridership, while connecting bus
station sites and the parking lots were not significantly related to ridership. Population density and common
residences land were found to be dominating factors in promoting morning boarding & evening alighting ridership,
which implied that these two factors should be focused on to encourage commuting-purpose rail transit usage.
However, the indistinct effect of urban villages on rail transit ridership suggested planners to pay more attentions
on urban regeneration at the pedestrian catchment areas (PCAs) with urban villages. High employment
density and a large FAR were suggested at the employment-oriented areas owing to their importance in promoting
rail transit ridership, especially the morning alighting & evening boarding ridership. Moreover, educational
research land use significantly affected weekday ridership while sports land use positively influenced weekend
ridership, which suggested planners to pay more attention on the non-commuting trips. The different influencing
mechanisms of various types of rail transit ridership highlighted the need to consider land use balance planning
and trip demand optimization in highly urbanized metropolises in developing countries. Keywords: Rail transit ridership | Big data | Fine-scale | Built environment | Guangzhou |
مقاله انگلیسی |
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Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China
تأثیرات مکانی متغیر از عوامل محیطی ساخته شده بر رکود حمل و نقل ریلی در سطح ایستگاه: یک مطالعه موردی در گوانگژو ، چین-2020 Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting
transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR)
models have previously been employed to reveal the spatial differences in such relationships at the station level.
However, few studies characterized the built environment at a fine scale and associated them with rail transit
usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering
varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multisource
spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social
media and building footprint data to precisely quantify the characteristics of the built environment. This was
combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the
rail transit ridership vary across the study region. The k-means clustering method was employed to identify
distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was
proposed based on the results and differentiated planning guidance was suggested for different zones. These
recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the
traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute
for the residents. The policy and planning implications are crucial for the coordinated development of the rail
transit system and land use. Keywords: Transit ridership | Built environment | Geographically weighted regression | K-means | Guangzhou |
مقاله انگلیسی |