scholarly journals Modeling Operating Speed and Deceleration on Two-Lane Rural Roads with Global Positioning System Data

2010 ◽  
Vol 2171 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Ana Maria Pérez Zuriaga ◽  
Alfredo García García ◽  
Francisco Javier Camacho Torregrosa ◽  
Pierangelo D'Attoma
Keyword(s):  
Global Positioning System ◽  
Operating Speed ◽  
Positioning System ◽  
Rural Roads ◽  
Global Positioning ◽  
System Data

Modeling Operating Speed on Multilane Highways Using Global Positioning System Data

2021 ◽  
Vol 12 (4) ◽  
pp. 237
Author(s):  
Abdulrazzaq J. Alkherret ◽  
Musab Y. Abuaddous ◽  
Ja’far A. Al Btoosh ◽  
Mousa I. Bani Baker
Keyword(s):  
Global Positioning System ◽  
Operating Speed ◽  
Positioning System ◽  
Global Positioning ◽  
System Data

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.

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