The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou-Reference-Cited by-同舟云学术

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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

Published:2020-04 Issue: Volume:99 Page:102580
ISSN:0264-2751
Container-title:Cities
language:en
Short-container-title:Cities

Author:

Li Shaoying,Lyu Dijiang,Liu Xiaoping,Tan Zhangzhi,Gao Feng,Huang Guanping,Wu Zhifeng

Funder

National Natural Science Foundation of China

Universities in Guangdong Province

Publisher

Elsevier BV

Subject

Tourism, Leisure and Hospitality Management,Urban Studies,Sociology and Political Science,Development

Reference53 articles.

1. Understanding the impact of built environment on metro ridership using open source in Shanghai;An;Cities,2019

2. Trip end models of local rail demand in England and Wales;Blainey;Journal of Transport Geography,2010

3. Using multi-source geospatial big data to identify the structure of polycentric cities;Cai;Remote Sensing of Environment,2017

4. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level;Cardozo;Applied Geography,2012

5. A station-level ridership model for the metro network in Montreal, Quebec;Chan;Canadian Journal of Civil Engineering,2013

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5. Spatial and Temporal Variation of Subway Ridership before and during the COVID-19 Period in Beijing;Transportation Research Record: Journal of the Transportation Research Board;2024-07-30

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