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Article

Public Bike Trip Purpose Inference Using Point-of-Interest Data

by 1, 1 and 2,*
1
Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
2
Social Eco Tech Institute, Konkuk University, Seoul 05029, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz, Maria Antonia Brovelli, Xiaoguang Zhou and Hussein Abdulmuttalib
ISPRS Int. J. Geo-Inf. 2021, 10(5), 352; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050352
Received: 5 March 2021 / Revised: 26 April 2021 / Accepted: 16 May 2021 / Published: 20 May 2021
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest (POI) data. Because the purpose of a trip involves decision-making, its inference necessitates an understanding of the spatiotemporal complexity of human activities. Thus, the spatiotemporal features affecting bike trips were selected from the bike-share data, and the land uses at the origin and destination of the trips were extracted from the POI data. During POI type embedding, the data were augmented considering the geographical distance between the POIs and the number of bike rentals at each bike station. We further developed a ground truth data construction method that uses temporal mobile and POI data. The inference model was built using machine learning and applied to experiments involving bike stations in Seocho-gu, Seoul, Korea. The experimental results revealed that optimal performance was achieved with the use of decision tree algorithms, as demonstrated by a 78.95% overall accuracy and 66.43% F1-score. The proposed method contributes to a better understanding of the causes of movement within cities. View Full-Text
Keywords: bike trip purpose; point-of-interest embedding; land use extraction; temporal mobile data; machine learning bike trip purpose; point-of-interest embedding; land use extraction; temporal mobile data; machine learning
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MDPI and ACS Style

Lee, J.; Yu, K.; Kim, J. Public Bike Trip Purpose Inference Using Point-of-Interest Data. ISPRS Int. J. Geo-Inf. 2021, 10, 352. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050352

AMA Style

Lee J, Yu K, Kim J. Public Bike Trip Purpose Inference Using Point-of-Interest Data. ISPRS International Journal of Geo-Information. 2021; 10(5):352. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050352

Chicago/Turabian Style

Lee, Jiwon, Kiyun Yu, and Jiyoung Kim. 2021. "Public Bike Trip Purpose Inference Using Point-of-Interest Data" ISPRS International Journal of Geo-Information 10, no. 5: 352. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050352

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