Examining active travel behavior through explainable machine learning: Insights from Beijing, China-Reference-Cited by-同舟云学术

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Examining active travel behavior through explainable machine learning: Insights from Beijing, China

Published:2024-02 Issue: Volume:127 Page:104038
ISSN:1361-9209
Container-title:Transportation Research Part D: Transport and Environment
language:en
Short-container-title:Transportation Research Part D: Transport and Environment

Author:

Yin Ganmin,Huang Zhou^ORCID,Fu Chen,Ren Shuliang,Bao Yi,Ma Xiaolei

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference82 articles.

1. Beijing 2022 national economic and social development statistical communiqué;Beijing Municipal Bureau of Statistics,2023

2. Association of the built environment with physical activity and obesity in older persons;Berke;Amer. J. Public Health,2007

3. Random forests;Breiman;Mach. Learn.,2001

4. Trends in walking and cycling safety: recent evidence from high-income countries, with a focus on the United States and Germany;Buehler;Amer. J. Public Health,2017

5. Active travel behavior;Burbidge;Transp. Lett.,2009

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1. Explaining deep learning-based activity schedule models using SHapley Additive exPlanations;Transportation Letters;2024-06-03

2. A machine learning approach for unraveling the influence of air quality awareness on travel behavior;Decision Analytics Journal;2024-06

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