Public Bike Trip Purpose Inference Using Point-of-Interest Data
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 (P...
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doaj-a06cf37c4ea44361899021277354ccf32021-06-01T00:32:52ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-05-011035235210.3390/ijgi10050352Public Bike Trip Purpose Inference Using Point-of-Interest DataJiwon Lee0Kiyun Yu1Jiyoung Kim2Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, KoreaDepartment of Civil and Environmental Engineering, Seoul National University, Seoul 08826, KoreaSocial Eco Tech Institute, Konkuk University, Seoul 05029, KoreaPublic 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.https://www.mdpi.com/2220-9964/10/5/352bike trip purposepoint-of-interest embeddingland use extractiontemporal mobile datamachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiwon Lee Kiyun Yu Jiyoung Kim |
spellingShingle |
Jiwon Lee Kiyun Yu Jiyoung Kim Public Bike Trip Purpose Inference Using Point-of-Interest Data ISPRS International Journal of Geo-Information bike trip purpose point-of-interest embedding land use extraction temporal mobile data machine learning |
author_facet |
Jiwon Lee Kiyun Yu Jiyoung Kim |
author_sort |
Jiwon Lee |
title |
Public Bike Trip Purpose Inference Using Point-of-Interest Data |
title_short |
Public Bike Trip Purpose Inference Using Point-of-Interest Data |
title_full |
Public Bike Trip Purpose Inference Using Point-of-Interest Data |
title_fullStr |
Public Bike Trip Purpose Inference Using Point-of-Interest Data |
title_full_unstemmed |
Public Bike Trip Purpose Inference Using Point-of-Interest Data |
title_sort |
public bike trip purpose inference using point-of-interest data |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2021-05-01 |
description |
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. |
topic |
bike trip purpose point-of-interest embedding land use extraction temporal mobile data machine learning |
url |
https://www.mdpi.com/2220-9964/10/5/352 |
work_keys_str_mv |
AT jiwonlee publicbiketrippurposeinferenceusingpointofinterestdata AT kiyunyu publicbiketrippurposeinferenceusingpointofinterestdata AT jiyoungkim publicbiketrippurposeinferenceusingpointofinterestdata |
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