Fusion of weigh-in-motion and global positioning system data to estimate truck weight distributions at traffic count sites

2019 ◽  
Vol 24 (2) ◽  
pp. 201-215 ◽  
Author(s):  
Sarah Hernandez ◽  
Kyung (Kate) Hyun
Keyword(s):  
Global Positioning System ◽  
Positioning System ◽  
Weigh In Motion ◽  
Weight Distributions ◽  
Global Positioning ◽  
Traffic Count ◽  
System Data ◽  
Truck Weight

scholarly journals Development of Time-location Weighted Spatial Measures Using Global Positioning System Data

2013 ◽  
Vol 28 ◽  
pp. e2013005 ◽  
Author(s):  
Daikwon Han ◽  
Kiyoung Lee ◽  
Jongyun Kim ◽  
Deborah H. Bennett ◽  
Diana Cassady ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Positioning System ◽  
Global Positioning ◽  
Spatial Measures ◽  
System Data

scholarly journals Mode Inference using enhanced Segmentation and Pre-processing on raw Global Positioning System data

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1144-1158 ◽  
Author(s):  
Asif Nawaz ◽  
Huang Zhiqiu ◽  
Wang Senzhang ◽  
Yasir Hussain ◽  
Amara Naseer ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Mobile Communications ◽  
Single Mode ◽  
Positioning System ◽  
Trajectory Data ◽  
Transportation Mode ◽  
Extensive Evaluation ◽  
Global Positioning ◽  
Significant Performance ◽  
System Data

Many applications use the Global Positioning System data that provide rich context information for multiple purposes. Easier availability and access of Global Positioning System data can facilitate various mobile applications, and one of such applications is to infer the mobility of a user. Most existing works for inferring users’ transportation modes need the combination of Global Positioning System data and other types of data such as accelerometer and Global System for Mobile Communications. However, the dependency of the applications to use data sources other than the Global Positioning System makes the use of application difficult if peer data source is not available. In this paper, we introduce a new generic framework for the inference of transportation mode by only using the Global Positioning System data. Our contribution is threefold. First, we propose a new method for Global Positioning System trajectory data preprocessing using grid probability distribution function. Second, we introduce an algorithm for the change point–based trajectory segmentation, to more effectively identify the single-mode segments from Global Positioning System trajectories. Third, we introduce new statistical-based topographic features that are more discriminative for transportation mode detection. Through extensive evaluation on the large trajectory data GeoLife, our approach shows significant performance improvement in terms of accuracy over state-of-the-art baseline models.

Subsidence is determined in the heart of the Central Valley using Post Processed Static and Precise Point Positioning techniques

2020 ◽  
Vol 14 (1) ◽  
pp. 113-118 ◽  
Author(s):  
Y. Facio ◽  
M. Berber
Keyword(s):  
Data Processing ◽  
Global Positioning System ◽  
Precise Point Positioning ◽  
Central Valley ◽  
Gps Data ◽  
Positioning System ◽  
Online Services ◽  
Global Positioning ◽  
System Data ◽  
Point Positioning

AbstractPost Processed Static (PPS) and Precise Point Positioning (PPP) techniques are not new; however, they have been refined over the decades. As such, today these techniques are offered online via GPS (Global Positioning System) data processing services. In this study, one Post Processed Static (OPUS) and one Precise Point Positioning (CSRS-PPP) technique is used to process 24 h GPS data for a CORS (Continuously Operating Reference Stations) station (P565) duration of year 2016. By analyzing the results sent by these two online services, subsidence is determined for the location of CORS station, P565, as 3–4 cm for the entire year of 2016. In addition, precision of these two techniques is determined as ∼2 cm. Accuracy of PPS and PPP results is 0.46 cm and 1.21 cm, respectively. Additionally, these two techniques are compared and variations between them is determined as 2.5 cm.

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