Understanding the impact of built environment on metro ridership using open source in Shanghai-Reference-Cited by-同舟云学术

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Understanding the impact of built environment on metro ridership using open source in Shanghai

Published:2019-10 Issue: Volume:93 Page:177-187
ISSN:0264-2751
Container-title:Cities
language:en
Short-container-title:Cities

Author:

An Dadi,Tong Xin,Liu Kun,Chan Edwin H.W.

Funder

Hong Kong Scholar Program

Hong Kong Polytechnic University

Shanghai Pujiang Program

Shanghai Summit Discipline in Design

Publisher

Elsevier BV

Subject

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

Reference48 articles.

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

2. Built environments and mode choice: toward a normative framework;Cervero;Transportation Research Part D Transport & Environment,2002

3. Alternative approaches to modeling the travel-demand impacts of smart growth;Cervero;Journal of the American Planning Association,2006

4. Travel demand and the 3Ds: Density, diversity, and design;Cervero;Transportation Research Part D: Transport and Environment,1997

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1. Aged and wheeled mobility in transit-oriented development: The capabilities approach;Transportation Research Part D: Transport and Environment;2024-02

2. Analysis of the relationship between metro ridership and built environment: A machine learning method considering combinational features;Tunnelling and Underground Space Technology;2024-02

3. Analyzing spatiotemporal distribution patterns of metro ridership: Comparison between common-class and business-class carriage service;Journal of Transport Geography;2024-02

4. Understanding post-pandemic spatiotemporal differences in the recovery of metro travel behavior among different groups by considering the built environment;Journal of Engineering and Applied Science;2024-01-20

5. Effects of buffer size on associations between the built environment and metro ridership: A machine learning-based sensitive analysis;Journal of Transport Geography;2023-12

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