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

2019 ◽  
Vol 11 (5) ◽  
pp. 1429 ◽  
Author(s):  
Chengming Li ◽  
Zhaoxin Dai ◽  
Weixiang Peng ◽  
Jianming Shen
Keyword(s):  
Mobile Communications ◽  
Urban Systems ◽  
Gps Data ◽  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Electric Bicycle ◽  
Gps Trajectory ◽  
New Perspective ◽  
Using Data ◽  
First Time

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.

scholarly journals Data Cleansing Method for Sparse Trajectory Data: A Case Study of Shared Electric Bicycles in Tengzhou

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Zhaoxin Dai ◽  
Weixiang Peng ◽  
Chengcheng Zhang
Keyword(s):  
Mobile Communications ◽  
Urban Systems ◽  
Gps Data ◽  
Trajectory Data ◽  
Data Cleansing ◽  
Gps Trajectory Data ◽  
Electric Bicycles ◽  
New Perspective ◽  
Using Data ◽  
First Time

<p><strong>Abstract.</strong> 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.</p>

Identifying the Stay Point Using GPS Trajectory of Taxis

2013 ◽  
Vol 353-356 ◽  
pp. 3511-3515 ◽  
Author(s):  
Hao Xiao ◽  
Wen Jun Wang ◽  
Xu Zhang
Keyword(s):  
Mobile Communications ◽  
Information Services ◽  
Gps Data ◽  
Trajectory Data ◽  
Clustering Method ◽  
Travel Information ◽  
Identification Methods ◽  
Location Aware ◽  
Gps Trajectory ◽  
Basic Characteristics

With the widespread use of personal mobile communications location-aware devices, a large amount of data of trajectory produced and can be used in information services. These huge amounts of data involves the pattern of human behavior information and cause numerous researchers' research interests. As is known,the key to travel information mining from the trajectory data is the stay point recognition and semantic annotation.Overcoming the shortcomings on adaptability and resistance to noise exists in existed stay points identification methods, and also combined with the basic characteristics of the taxi GPS data,We proposed a way with an parameter optimization stratage to get the stay points from a single trajectory and the figure shows it really works well, with high precision and strong adaptability on the recall ratio and precision ratio.And then,based on this significant achievements,we applies a refined clustering method based on the clustering radius and frequency parameters and get the POI results.

A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Pengfei HAO ◽  
Chunlong YAO ◽  
Qingbin MENG ◽  
Xiaoqiang YU ◽  
Xu LI
Keyword(s):  
Compression Algorithm ◽  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Enabling Smart Urban Services with GPS Trajectory Data

2021 ◽  
Author(s):  
Chao Chen ◽  
Daqing Zhang ◽  
Yasha Wang ◽  
Hongyu Huang
Keyword(s):  
Trajectory Data ◽  
Urban Services ◽  
Gps Trajectory Data ◽  
Gps Trajectory

scholarly journals An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

2019 ◽  
Vol 8 (9) ◽  
pp. 411 ◽  
Author(s):  
Tang ◽  
Deng ◽  
Huang ◽  
Liu ◽  
Chen
Keyword(s):  
Road Network ◽  
Sampling Rate ◽  
Quality Data ◽  
Automatic Method ◽  
Trajectory Data ◽  
The Road ◽  
High Sampling Rate ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

scholarly journals Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

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