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.

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

2021 ◽  
Author(s):  
Alborz Soltankhah-Bidkhti
Keyword(s):  
Public Transportation ◽  
Road Network ◽  
Automatic Generation ◽  
Geographical Information ◽  
Gps Data ◽  
Time Intervals ◽  
Field Surveys ◽  
Gis Applications ◽  
Gps Trajectories ◽  
Road Maps

Keeping road network databases up-to-date is crucial to Geographical Information System (GIS) applications such as road networking. The vector road centerlines extracted from field surveys and satellite images are expensive and labor intensive with long updating processes. The GPS data crowd-sourced by public transportation users, provides an expanding source for enhancing road maps because of its rich spatial-temporal coverage and reasonable level of accuracy. The overall objective of this project is to implement an optimized methodology, which generates road centerline from GPS data obtained from taxis in Beijing without using any reference plans. Since the dataset used in this project has longer time intervals between trajectories compared to previous studies, the extracted road network on straight road segments are more accurate than the extracted road network on highway ramps in this project.

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

2021 ◽  
Author(s):  
Alborz Soltankhah-Bidkhti
Keyword(s):  
Public Transportation ◽  
Road Network ◽  
Automatic Generation ◽  
Geographical Information ◽  
Gps Data ◽  
Time Intervals ◽  
Field Surveys ◽  
Gis Applications ◽  
Gps Trajectories ◽  
Road Maps

Keeping road network databases up-to-date is crucial to Geographical Information System (GIS) applications such as road networking. The vector road centerlines extracted from field surveys and satellite images are expensive and labor intensive with long updating processes. The GPS data crowd-sourced by public transportation users, provides an expanding source for enhancing road maps because of its rich spatial-temporal coverage and reasonable level of accuracy. The overall objective of this project is to implement an optimized methodology, which generates road centerline from GPS data obtained from taxis in Beijing without using any reference plans. Since the dataset used in this project has longer time intervals between trajectories compared to previous studies, the extracted road network on straight road segments are more accurate than the extracted road network on highway ramps in this project.

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

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

Characterising the spatial and temporal activities of free-ranging cows from GPS data

2012 ◽  
Vol 34 (2) ◽  
pp. 149 ◽  
Author(s):  
Dean M. Anderson ◽  
Craig Winters ◽  
Rick E. Estell ◽  
Ed L. Fredrickson ◽  
Marek Doniec ◽  
...  
Keyword(s):  
Landscape Management ◽  
Beef Cows ◽  
Gps Data ◽  
Activity Data ◽  
Temporal Behaviour ◽  
Global Positioning ◽  
Spatio Temporal ◽  
Gps Devices ◽  
Electronic Tracking ◽  
Free Ranging

Electronic tracking provides a unique way to document behaviour by cows on a continuous basis. Over 2 years 17 beef cows with calves were fitted with global positioning system (GPS) devices programmed to record uncorrected GPS locations at 1-s intervals in a semi-desert rangeland. Each cow was periodically observed during daylight hours and foraging, walking and stationary (standing/lying) activity times were recorded across days and individual cows to calculate a mean travel rate for each activity. Data without observers present were collected immediately preceding and following the abrupt weaning of calves at between 223 and 234 days of age to evaluate the potential of classifying various travel rates into foraging, walking and stationary activity. The three activities were further characterised within a 24-h period based on the sun’s angle with respect to the horizon. Only data from cows whose equipment acquired ≥ 90% of the potential GPS positional data among consecutive days were analysed. Due to problems with the equipment, data from two cows in 2009 and two cows in 2011 met these criteria. The interval evaluated consisted of four 24-h periods before abrupt weaning and seven 24-h periods following weaning. Results suggested that uncorrected 1-s positional GPS data are satisfactory to classify the behaviour by free-ranging beef cows into foraging, walking and stationary activities. Furthermore, abrupt weaning caused cows to change their spatial and temporal behaviour across and within days. Overall, travel by cows increased post-weaning with subtle within-day behavioural changes. Further research will be required to fully understand the biological importance of spatio-temporal behaviour to optimise cattle and landscape management goals.

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

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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