6408519
doi
10.5281/zenodo.6408519
oai:zenodo.org:6408519
user-gisruk2022
Mahony, Michael
Tao, Sui
Machine Learning for Near-Real Time Bus Ridership Prediction During "Extreme" Weather
Rowe, Francisco
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
<p>Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non- stationarity have not been fully addressed in modelling and predicting transit ridership under the influence of weather conditions. Drawing on three-month smart card data in Brisbane, Australia, this research implements a suite of tree-based machine-learning algorithms, to model and predict near real-time bus ridership in relation to sudden change of weather conditions. The study confirms that there indeed exists a significant level of spatio-temporal variability of weather-ridership relationship, which produces equally dynamic patterns of prediction errors.</p>
Zenodo
2022-04-02
info:eu-repo/semantics/article
6408518
user-gisruk2022
1648950558.458458
104345
md5:23fbcfafa07ea6b3bef8b70cde04b2f9
https://zenodo.org/records/6408519/files/GISRUK_2022_paper_48.pdf
public
10.5281/zenodo.6408518
isVersionOf
doi