scholarly journals Recognition of Operating Characteristics of Heavy Trucks Based on the Identification of GPS Trajectory Stay Points

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Long Wei ◽  
Gang Chen ◽  
Wenjie Sun ◽  
Guoqi Li
Keyword(s):  
Sichuan Province ◽  
Horizontal Line ◽  
Trajectory Data ◽  
Operating Characteristics ◽  
Heavy Trucks ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Freight Management ◽  
Freight Trucks ◽  
Operation Characteristics

As an emerging data source, feature identification based on GPS trajectory data has become a hot issue in the field of data mining and freight management. A method of trajectory data extraction, classification, and visualization based on stay points was proposed in this paper to analyze the operation characteristics of heavy trucks from the perspectives of intracity transportation, intraprovincial transportation, and interprovincial transportation. The GPS trajectory data of heavy trucks in Sichuan Province in March 2019 were taken as an example to analyze the operation characteristics. The results show that the heavy trucks in Sichuan Province are mainly transported within the province, and the freight efficiency is slightly better than the average level of the national freight trucks in the same period, failing to give full play to the advantages of long transport distance. The manufacturing industry is the main service object of heavy trucks, and the vehicles engaged in transportation within the province are more dependent on logistics enterprises and their ancillary facilities. The north-south longitudinal line and east-west horizontal line are the main interprovincial transport channels, and the provincial and municipal transport is mainly concentrated in some urban trunk lines, ring lines, and express routes. The proposed technical method can describe the operating characteristics of freight trucks from the perspective of microscopic and service market, not only to guide the layout of highway freight yards, logistics parks, and logistics hubs and the determination of service functions but also to provide a reference basis for freight management-related departments and drivers to formulate transportation plans and establish freight information platforms to improve freight efficiency and safety.

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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