Abstract
Abstract. Location based service (LBS) technologies provides a new perspective for the spatiotemporal dynamics analysis of urban systems. Previous studies have been performed by using data of mobile communications, public transport vehicles (taxis and buses), wireless hotspots and shared bicycles. However, the analysis based on shared electric bicycles (e-bike) has yet to be studied in the literature. Data cleansing and the extraction of origin-destination (O-D) are prerequisites for the study of urban systems spatiotemporal patterns. In this study, based on a dataset that contains a week of shared e-bike GPS data in Tengzhou City (Shandong Province), sparse characteristics of discontinuities and non-uniformities of trajectory GPS and a lack of riding status are captured. Based on the characteristics and combining with the actual road, we proposed a method for the extraction of O-D pairs for every trajectory segments from continuous and stateless trajectory GPS data. This method cleans the incomplete and invalid trajectory records, which is suitable for sparse trajectory data. Finally, a week-long shared e-bike GPS data in Tengzhou City is scrubbed, and by sampling method, the extraction accuracy of 91% is verified. In summary, we provide a preliminary cleansing rules for the sparse trajectory data of shared e-bikes at the first time, which is highly reliable, and is 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 (LBS) like global positioning satellite (GPS) technology, social networks and wireless communications (Long et al, 2017)
Our trajectory cleansing method, which is suitable for the sparse data of shared e-bikes, satisfies the following requirements of trajectory extraction: efficacy, completeness, accuracy and rationality
Results of the cleansing trajectory in Tengzhou city 11178 valid ride trajectories were scrubbed from the week of disordered GPS trajectory data of shared e-bikes in Tengzhou
Summary
The acquisition of a large number of individual spatiotemporal data is steadily becoming realized with the development and application of location-based services (LBS) like global positioning satellite (GPS) technology, social networks and wireless communications (Long et al, 2017). These large-scale individual datasets that contain spatiotemporal characteristics provide new ways for the scientific study of human mobility patterns, the spatial structures of urban residence and employment, and urban planning. Under the initiative of low-carbon transportation, cheap and convenient public bicycle rental systems rise, which have become one of the most popular modes of travel for urban residents These systems have effectively resolved the “last mile problem” in urban transportation, especially in the first tier cities like Beijing, Shanghai and Guangzhou.
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