2023
Zhou, Ting; Feng, Tao; Kemperman, Astrid
Assessing the effects of the built environment and microclimate on cycling volume Journal Article
In: Transportation Research Part D: Transport and Environment, vol. 124, pp. 103936, 2023, ISSN: 1361-9209.
Abstract | Links | BibTeX | Tags: Cycling volume, Gradient Boosting Decision Tree, Multi-scale urban built environment
@article{ZHOU2023103936,
title = {Assessing the effects of the built environment and microclimate on cycling volume},
author = {Ting Zhou and Tao Feng and Astrid Kemperman},
url = {https://www.sciencedirect.com/science/article/pii/S1361920923003334},
doi = {https://doi.org/10.1016/j.trd.2023.103936},
issn = {1361-9209},
year = {2023},
date = {2023-01-01},
journal = {Transportation Research Part D: Transport and Environment},
volume = {124},
pages = {103936},
abstract = {Cycling benefits human health and helps to mitigate environmental issues. However, limited evidence exists regarding how the built environment influences cycling volume under different time and weather conditions. In this paper, we employed a Gradient Boosting Decision tree method to analyze the non-linear and threshold effects of the multiscale built environment and microclimate on cycling volume. Results based on the multisource data of The Netherlands show that 27 out of the 28 variables have a non-linear and threshold effect on cycling volume. Temperature is found to be a dominant factor among all variables. At street level, slope is the most important factor, followed by the green view and sky view indexes. At neighborhood level, population density is the most important factor, followed by residential density, and the density of bus stops. These findings offer useful insights for planning a cycling-friendly urban built environment at different scales.},
keywords = {Cycling volume, Gradient Boosting Decision Tree, Multi-scale urban built environment},
pubstate = {published},
tppubtype = {article}
}
Cycling benefits human health and helps to mitigate environmental issues. However, limited evidence exists regarding how the built environment influences cycling volume under different time and weather conditions. In this paper, we employed a Gradient Boosting Decision tree method to analyze the non-linear and threshold effects of the multiscale built environment and microclimate on cycling volume. Results based on the multisource data of The Netherlands show that 27 out of the 28 variables have a non-linear and threshold effect on cycling volume. Temperature is found to be a dominant factor among all variables. At street level, slope is the most important factor, followed by the green view and sky view indexes. At neighborhood level, population density is the most important factor, followed by residential density, and the density of bus stops. These findings offer useful insights for planning a cycling-friendly urban built environment at different scales.