Spatio-temporal autocorrelation of road network data

2011 ◽  
Vol 14 (4) ◽  
pp. 389-413 ◽  
Author(s):  
Tao Cheng ◽  
James Haworth ◽  
Jiaqiu Wang
Keyword(s):  
Road Network ◽  
Network Data ◽  
Temporal Autocorrelation ◽  
Spatio Temporal

scholarly journals Automatic generation of road network data from smartphone GPS trajectories

2021 ◽  
Author(s):  
Zheng Niu
Keyword(s):  
Road Network ◽  
Automatic Generation ◽  
Geographical Information ◽  
Network Data ◽  
Gps Data ◽  
Gis Applications ◽  
Road Map ◽  
Gps Trajectories ◽  
Spatio Temporal ◽  
Gps Devices

Keeping road network databases up-to-date is crucial to Geographical Information System (GIS) applications such as vehicle navigation. The vector road centerlines extracted from satellite images or in-car Global Positioning System (GPS) devices are likely to be inaccurate due to costly and labour intensive or long updating circle. The GPS data crowdsourced through smartphones provides an emerging source for refining road map due to its rich spatio-temporal coverage and reasonable level of accuracy. This thesis introduces an optimized methodology to automatically generate road network data from smartphone GPS data without using any reference maps. The horizontal accuracy of the extracted road centerlines, measured as a root mean square of 1.424 m and 1.252 m for curved and straight road segments respectively, is better than that of some existing road datasets. The outcome of this research will provide a new way of generating a more accurate and up-to-date road network databases.

scholarly journals Automatic generation of road network data from smartphone GPS trajectories

2021 ◽  
Author(s):  
Zheng Niu
Keyword(s):  
Road Network ◽  
Automatic Generation ◽  
Geographical Information ◽  
Network Data ◽  
Gps Data ◽  
Gis Applications ◽  
Road Map ◽  
Gps Trajectories ◽  
Spatio Temporal ◽  
Gps Devices

Keeping road network databases up-to-date is crucial to Geographical Information System (GIS) applications such as vehicle navigation. The vector road centerlines extracted from satellite images or in-car Global Positioning System (GPS) devices are likely to be inaccurate due to costly and labour intensive or long updating circle. The GPS data crowdsourced through smartphones provides an emerging source for refining road map due to its rich spatio-temporal coverage and reasonable level of accuracy. This thesis introduces an optimized methodology to automatically generate road network data from smartphone GPS data without using any reference maps. The horizontal accuracy of the extracted road centerlines, measured as a root mean square of 1.424 m and 1.252 m for curved and straight road segments respectively, is better than that of some existing road datasets. The outcome of this research will provide a new way of generating a more accurate and up-to-date road network databases.

A UML-based Representation of Spatio-Temporal Evolution in Road Network Data

2010 ◽  
Vol 14 (6) ◽  
pp. 853-872 ◽  
Author(s):  
Alex Lohfink ◽  
Duncan McPhee ◽  
Mark Ware
Keyword(s):  
Road Network ◽  
Temporal Evolution ◽  
Network Data ◽  
Spatio Temporal

Forest path condition monitoring based on crowd-based trajectory data analysis

2020 ◽  
pp. 1-18
Author(s):  
Francisco Arcas-Tunez ◽  
Fernando Terroso-Saenz
Keyword(s):  
Information Acquisition ◽  
Road Network ◽  
Road Networks ◽  
Trajectory Data ◽  
Country Road ◽  
Wide Range ◽  
Sport Activities ◽  
Path Condition ◽  
Spatio Temporal ◽  
The City

The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and country road networks. In that sense, forest paths are used for a wide range of recreational and sport activities by many different people and they can be also affected by different problems or obstacles blocking them. As a result, this work introduces SAMARITAN, a framework for rural-road network monitoring based on MCS. SAMARITAN analyzes the spatio-temporal trajectories from cyclists extracted from the fitness application Strava so as to uncover potential obstacles in a target road network. The framework has been evaluated in a real-world network of forest paths in the city of Cieza (Spain) showing quite promising results.

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