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

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.

Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

“…Cases of e-bike relocation using high-speed cars: for all continued interval(s) with speed > 35 km/h, abandon the interval(s) and redefine O-D pairs. Contrary to Li et al, 2019 research [24], to avoid the appearance of apparent O-D path discontinuity, this study set the filtering speed to 35 km/h instead of 20 km/h (government speed limit). This can also provide insight into some behaviors and phenomena that violate traffic rules.…”

“…The topography appears to be high in the northeast and low in the southwest. E-bikes are widely used, especially in the downtown areas of Tengzhou City [24].…”

“…The data cleansing approach proposed by Li et al, 2019 is taken as a reference. Speed factors and traffic lights are accounted for when cleansing the disordered raw GPS points to acquire trajectory segments that contain only valid O-D pairs [24]. Python language is applied in this study to perform cleansing procedures and statistical analysis, and the process is as follows.…”

Optimal Fleet Deployment Strategy: Model the Effect of Shared E-Bikes on Bike-Sharing System

“…Ji et al [33] simulated the user demand and system availability of EBSS based on a pilot e-bike-sharing project. Li et al [34] proposed a method for sharing e-bikes trajectory data cleansing. To optimise the EBSS design systematically, several studies have formulated optimisation models.…”

Trip Extraction of Shared Electric Bikes Based on Multi-Rule-Constrained Homomorphic Linear Clustering Algorithm

“…Fewer existing studies are related to shared e-bikes, most of which were based on survey data to study the performance of e-bikes [44], users' mobility behavior [45][46][47], and travel mode influencing factors [48]. Li and Dai [49] completed data cleaning of shared e-bike trajectories based on the speed and time interval rules for the first time, without considering the trajectory semantic information. To utilize the trajectory semantic information, a new method, MCHLC algorithm, is proposed to extract trips from the shared e-bike trajectories.…”