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- Title
Public Bike Trip Purpose Inference Using Point-of-Interest Data.
- Authors
Lee, Jiwon; Yu, Kiyun; Kim, Jiyoung; Kainz, Wolfgang; Brovelli, Maria Antonia; Zhou, Xiaoguang; Abdulmuttalib, Hussein
- Abstract
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.
- Subjects
SEOUL (Korea); BICYCLE touring; DECISION trees; BICYCLES; MACHINE learning
- Publication
ISPRS International Journal of Geo-Information, 2021, Vol 10, Issue 5, p352
- ISSN
2220-9964
- Publication type
Article
- DOI
10.3390/ijgi10050352