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.
Highlights
The acquisition of a large number of individual spatiotemporal data is steadily becoming realized with the development and application of location-based services (LBSs) such as global positioning satellite (GPS) technology, social networks and wireless communications [1]
Data cleaning and O-D pair extraction are prerequisites for analysis of urban structures and human mobility patterns based on spatiotemporal LBS data
The data used in this study are the GPS trajectory points of shared e-bikes in Tengzhou that were acquired between 19 May 2018 and 26 May 2018
Summary
The acquisition of a large number of individual spatiotemporal data is steadily becoming realized with the development and application of location-based services (LBSs) such as global positioning satellite (GPS) technology, social networks and wireless communications [1]. These large-scale individual datasets that contain spatiotemporal characteristics provide new ways to scientifically study human mobility patterns, the spatial structures of urban residence and employment and urban planning. Unlike taxi GPS data or shared bicycle data, shared e-bikes travel at relatively high speeds and have limited battery usage, the GPS trajectory points tend to exhibit discontinuities and nonuniformities and it is difficult to obtain their riding status information. The results of this study are significant for reallocation of e-bikes and provide scientific data for human mobility pattern analysis, urban functional zones sectorization and spatial distribution of occupation and residence
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