Evidence map›Paper›PMID 39559377›Full record

ArticleFrontiers in public health2024

How to improve public environmental health by facilitating metro usage on weekend: exploring the non-linear and threshold impacts of the built environment.

Bozhezi Peng, Tao Wang, Yi Zhang, Chaoyang Li

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bozhezi PengState Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Tao WangState Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yi ZhangState Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
Chaoyang LiState Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The accelerated motorization has brought a series of environmental concerns and damaged public environmental health by causing severe air and noise pollution. The advocate of urban rail transit system such as metro is effective to reduce the private car dependence and alleviate associated environmental outcomes. Meanwhile, the increased metro usage can also benefit public and individual health by facilitating physical activities such as walking or cycling to the metro station. Therefore, promoting metro usage by discovering the nonlinear associations between the built environment and metro ridership is critical for the government to benefit public health, while most studies ignored the non-linear and threshold effects of built environment on weekend metro usage. Method: Using multi-source datasets in Shanghai, this study applies Gradient Boosting Decision Trees (GBDT), a nonlinear machine learning approach to estimate the non-linear and threshold effects of the built environment on weekend metro ridership. Results: Results show that land use mixture, distance to CBD, number of bus line, employment density and rooftop density are top five most important variables by both relative importance analysis and Shapley additive explanations (SHAP) values. Employment density and distance to city center are top five important variables by feature importance. According to the Partial Dependence Plots (PDPs), every built environment variable shows non-linear impacts on weekend metro ridership, while most of them have certain effective ranges to facilitate the metro usage. Maximum weekend ridership occurs when land use mixture entropy index is less than 0.7, number of bus lines reaches 35, rooftop density reaches 0.25, and number of bus stops reaches 10. Implication: Research findings can not only help government the non-linear and threshold effects of the built environment in planning practice, but also benefit public health by providing practical guidance for policymakers to increase weekend metro usage with station-level built environment optimization.

Indexed as

Built EnvironmentPublic HealthChinaCitiesEnvironmental HealthHumansRailroadsbuilt environmentmachine learningmetro ridershipnonlinearitypublic environmental health

Identifiers

PMID39559377
PMCPMC11572129

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.