Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles

Chengming Li, Zhaoxin Dai, Weixiang Peng and Jianming Shen
Additional contact information
Chengming Li: Chinese Academy of Surveying and mapping, Beijing 100830, China
Zhaoxin Dai: Chinese Academy of Surveying and mapping, Beijing 100830, China
Weixiang Peng: China University of Geosciences (Wuhan Campus), Wuhan 430074, China
Jianming Shen: Chinese Academy of Surveying and mapping, Beijing 100830, China

Sustainability, 2019, vol. 11, issue 5, 1-14

Abstract: Location-based service (LBS) technologies provide a new perspective for the analysis of the spatiotemporal dynamics of urban systems. Previous studies have been performed using data from mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, corresponding analyses based on shared electric bicycle (e-bike) have not yet been reported in the literature. Data cleaning and extraction of the origin-destination (O-D) are prerequisites for the study of the spatiotemporal patterns of urban systems. In this study, based on a dataset of a week of shared e-bike GPS data in the city of Tengzhou (Shandong Province), sparse characteristics of discontinuities and nonuniformities of the GPS trajectory and a lack of riding status are observed. Based on the characteristics and the actual road, we proposed a method for the extraction of O-D pairs for every trajectory segment from continuous and stateless trajectory GPS data. This method cleans the incomplete and invalid trajectory records, which is suitable for sparse trajectory data. A week of shared e-bike GPS data in Tengzhou is scrubbed and, by the sampling method, the extraction accuracy of 91% is verified. We provide preliminary cleaning rules for sparse trajectory shared e-bike data for the first time, which are highly reliable and suitable for data mining from other forms of sparse GPS trajectory data.

Keywords: green shared e-bike; GPS trajectory data; recognition of O-D pairs; sparse data; Tengzhou (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/5/1429/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/5/1429/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:5:p:1429-:d:212040

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().