@article{ref1,
title="Assessing and predicting mobility improvement of integrating bike-sharing into multimodal public transport systems",
journal="Transportation research record",
year="2021",
author="Kapuku, Christian and Kho, Seung-Young and Kim, Dong-Kyu and Cho, Shin-Hyung",
volume="2675",
number="11",
pages="204-213",
abstract="New shared mobility services have become increasingly common in many cities and shown potential to address urban transportation challenges. This study aims to analyze the mobility performance of integrating bike-sharing into multimodal transport systems and develop a machine learning model to predict the performance of intermodal trips with bike-sharing compared with those without bike-sharing for a given trip using transit smart card data and bike-sharing GPS data from the city of Seoul. The results suggest that using bike-sharing in the intermodal trips where it performs better than buses could enhance the mobility performance by providing up to 34% savings in travel time per trip compared with the scenarios in which bus is used exclusively for the trips and up to 33% savings when bike-sharing trips are used exclusively. The results of the machine learning models suggest that the random forest classifier outperformed three other classifiers with an accuracy of 90% in predicting the performance of bike-sharing and intermodal transit trips. Further analysis and applications of the mobility performance of bike-sharing in Seoul are presented and discussed.<p /> <p>Language: en</p>",
language="en",
issn="0361-1981",
doi="10.1177/03611981211045071",
url="http://dx.doi.org/10.1177/03611981211045071"
}