Global Positioning System Data Processing Research Articles

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data...

A review of travel behavioural pattern using GPS dataset: A systematic literature review

In the last few years, the Global positioning system (GPS) has been used to study travel patterns with the advancement of data collection techniques. A GPS device precisely records the time and the location data of an individual travel. The accuracy of travel studies is usually improved by integrating travel surveys or travel activity diaries with GPS data. GPS surveys are implemented to replace paper-based techniques and reduce human error on trip-misreporting issues. Several reviews have been performed previously on GPS travel studies. Still, a detailed systematic review on the comparison of different modes and methods on the entire procedure of GPS data processing has not been thoroughly studied. This review identified 116 peer-reviewed studies and journal papers from 2013 to 2020, and 53 papers were studied. The review focused on travel behavior pattern (TBP) analysis using GPS studies only and a combination of travel surveys. Our review reveals that 53 only 40 have quality assessment scores of >50 %, meaning the remaining TBP studies are conventional methods. The study also benefits data analysis and researchers to select the most appropriate data collection techniques for studying TBP and improvise them further for meta-analysis travel behavior studies.

Perbandingan Ketelitian Koordinat 3-D Antara Hasil Pengolahan Data GNSS Menggunakan Perangkat Lunak Hi-Target Geomatics Office dengan Trimble Business Center

The Global Positioning System (GPS) data processing method is a key to obtaining the accuracy of the 3-D coordinates. Therefore, there is commercial software issued by tool vendors and can be accessed by users. Among the software are Hi-Target Geomatics Office (HGO) and Trimble Business Center (TBC). Both of these software can be used to process GPS data by post-processing and can be accessed by users. In this study, a comparison of the results of GPS data processing using the two software will be carried out by comparing the accuracy of each software for processing GPS data at baseline of 5 – 10 m and 5 – 20 km. The results of data processing that was carried out in this study found that GPS data processing using both software at a baseline of 5 – 10 m did not show significant differences with the two having a difference of 1 mm in each coordinate component. However, at 5 – 20 km baseline the results of GPS data processing using TBC are more accurate with a difference of -0.0050 m in Northing, -0.0800 m in Easting and -0.0550 m in Elevation compared to the actual coordinates. With these results, the TBC software is more recommended.

Automatic Observation of Astronomical Coordinates Using the Shandong University of Science and Technology/National Astronomical Observatories Digital Zenith Tube

Abstract The digital zenith tube (DZT) is developed based on the astrogeodetic and photogrammetric theory. One prototype of DZT integrating techniques of global positioning system (GPS) and charge-coupled device (CCD) photogrammetry has been developed jointly by Shandong University of Science and Technology and National Astronomical Observatories of Chinese Science Academy, China. It can automatically estimate astronomical coordinates in real time by CCD star image processing and GPS data processing. The DZT’s errors are analyzed in three aspects, namely, the instrument error, observation error, and data processing error. Reliability and accuracy of DZT integrating GPS and CCD are verified by the practical field operations. Observing results on many unknown points by the repeated observations of each point show the internal precision of the astronomical latitude and longitude. Observing results on several known points and the comparison with the EGM2008-derived results verify the external accuracy of the DZT, and the differences of these points in astronomical latitude and longitude are within ±0.15″ and greater than 1″, respectively.

Evaluation of map‐matching algorithms for smartphone‐based active travel data

Global Positioning System (GPS) data on walking and cycling trips can generate useful insights for transportation systems but require substantial processing. One of the key GPS data processing steps is “map-matching”, or inference of the sequence of network links traversed during travel. The objective of this research is to evaluate the accuracy of existing map-matching algorithms for GPS data on active travel. A method to flag erroneous map-matching results without requiring ground-truth data and improvements for active travel data are also proposed. Six map-matching algorithms are applied to a sample of 63 trajectories, stratified on network density and average heading change, extracted from a large set of real-world trips from metropolitan Vancouver, Canada. Results show that the best performing method is PgMapMatch, which can be further improved by adjustments to link costs and allowing wrong-way travel. Two other algorithms have similarly accurate routes (70–90% accuracy, depending on the measure), but fail to generate routes for about a third of trips. The proposed error detection measure can be used (without ground truth data) to flag matched routes requiring visual inspection, with a recommendation to look for: Wrong-way travel, missing links in the network data, and parallel facilities on the same street.

On the Global Kinematic Positioning Variations During the September 2017 Solar Flare Events

AbstractSeveral X‐class solar flares (SFs) with different intensities and locations on the solar disk occurred in September 2017. Among them, the X9.3 SF on 6 September 2017 was the most intensive SF in the 24th solar cycle. In this study, we investigated and compared the ionospheric response to the different X‐class SFs and their impacts on the Global Positioning System (GPS) kinematic precise point positioning (PPP) solutions. We aim to study the mechanism behind the positioning degradation from the perspective of the impacts of the SF‐induced ionosphere disturbance on the GPS data processing. By comparing the sudden ionospheric disturbance (SID) induced by the SFs, we observed that the SID is controlled by the intensity, the location, and the duration time of the SF. We found that the SID induced by the SF may deteriorate the cycle slip (CS) detection algorithms seriously. The threshold of the CS detection observables is sensitive to the SID, making the CS easier to be falsely detected. On the other hand, for the SF that occurred during the recovery phase of the geomagnetic storm, as the case of the X8.2 SF, the effects of the SF can reduce the precision of the GPS measurements, thus affecting the positioning accuracy. These mechanisms are essential and significant for the accuracy and stability of the kinematic PPP solution obtained with GPS during the SF events.

GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data.

Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.

Map Matching Algorithm for Real-Time Data Processing of Non-route GPS Data in Seoul

Global Positioning System (GPS) data are satellite-based longitude and latitude coordinates for which map matching with road links, which are electronic maps, may be used in traffic operation management. Various studies have been conducted to improve the accuracy of map matching; however, research on the application of map matching algorithms for real-time data processing systems has not been widely conducted. Therefore, this study investigated a map matching algorithm that can shorten the processing time required for application to systems that process large volumes of GPS data collected for a wide network. To reduce the processing time, a candidate link selection method and a grid-type geospatial indexing process were designed. The processing time was analyzed based on an emulator before applying the map matching algorithm to the Transport Operation and Information Service Center, which generates the real-time travel speed data in Seoul City. The results obtained from comparing the prior and new algorithms showed a 96% decrease in the processing time. We believe that the findings of this study will benefit the real-time processing of GPS data, which are expected to be used in various ways in traffic operations and management in the future.

On Linear and Circular Approach to GPS Data Processing: Analyses of the Horizontal Positioning Deviations Based on the Adriatic Region IGS Observables

Global and regional positional accuracy assessment is of the highest importance for any satellite navigation system, including the Global Positioning System (GPS). Although positioning error can be expressed as a vector quantity with direction and magnitude, most of the research focuses on error magnitude only. The positional accuracy can be evaluated in terms of navigational quadrants as further refinement of error distribution, as it was shown here. This research was conducted in the wider area of the Northern Adriatic Region, employing the International Global Navigation Satellite Systems (GNSS) Service (IGS) data and products. Similarities of positional accuracy and deviations distributions for Single Point Positioning (SPP) were addressed in terms of magnitudes. Data were analyzed during the 11-day period. Linear and circular statistical methods were used to quantify regional positional accuracy and error behavior. This was conducted in terms of both scalar and vector values, with assessment of the underlying probability distributions. Navigational quadrantal positioning error subset analysis was carried out. Similarity in the positional accuracy and positioning deviations behavior, with uneven positional distribution between quadrants, indicated the directionality of the total positioning error. The underlying distributions for latitude and longitude deviations followed approximately normal distributions, while the radius was approximated by the Rayleigh distribution. The Weibull and gamma distributions were considered, as well. Possible causes of the analyzed positioning deviations were not investigated, but the ultimate positioning products were obtained as in standard, single-frequency positioning scenarios.

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

Abstract Post 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.

On the information content in linear horizontal delay gradients estimated from space geodesy observations

Abstract. We have studied linear horizontal gradients in the atmospheric propagation delay above ground-based stations receiving signals from the Global Positioning System (GPS). Gradients were estimated from 11 years of observations from five sites in Sweden. Comparing these gradients with the corresponding ones from the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses shows that GPS gradients detect effects over different timescales caused by the hydrostatic and the wet components. The two stations equipped with microwave-absorbing material below the antenna, in general, show higher correlation coefficients with the ECMWF gradients compared to the other three stations. We also estimated gradients using 4 years of GPS data from two co-located antenna installations at the Onsala Space Observatory. Correlation coefficients for the east and the north wet gradients, estimated with a temporal resolution of 15 min from GPS data, can reach up to 0.8 for specific months when compared to simultaneously estimated wet gradients from microwave radiometry. The best agreement is obtained when an elevation cut-off angle of 3∘ is applied in the GPS data processing, in spite of the fact that the radiometer does not observe below 20∘. We also note a strong seasonal dependence in the correlation coefficients, from 0.3 during months with smaller gradients to 0.8 during months with larger gradients, typically during the warmer and more humid part of the year. Finally, a case study using a 15 d long continuous very-long-baseline interferometry (VLBI) campaign was carried out. The comparison of the gradients estimated from VLBI and GPS data indicates that a homogeneous and frequent sampling of the sky is a critical parameter.

Travel mode imputation using GPS and accelerometer data from a multi-day travel survey

Over the past decade, interest has grown in using Global Positioning System (GPS) data to augment or even to replace traditional travel survey or activity diaries. If the full potential of this new class of data is to be realized, processing techniques will need to be standardized and automated to some degree. This paper develops a multinomial logit (MNL) model to impute travel mode from GPS and hip-mounted accelerometer data. The MNL model is the workhorse of travel demand modeling, but it has rarely been applied to GPS data processing. A web-based recall survey provided over 900 trips for estimation and 500 plus trips for validation from a larger multi-day GPS travel survey in Portland, Oregon. Special attention is given to the imputation of bicycle travel, the identification of which has been given little attention in the North American context. We also apply two existing non-MNL mode imputation models to our Portland data and to compare and test the broader transferability of specific techniques. We find that the MNL model as specified performs well overall, generally outperforming competing model forms on the Portland GPS data. Transit network data and accelerometer data significantly improve model fit for specific modes. Accelerometer data is found in particular to aid model fit for bicycling; however, external validation results were less clear. No benefit is found to segmenting models by traveler age, although not all age groups were covered by the sample. The MNL model shows strong potential for automated GPS processing and, as a commonly used transportation modeling technique, should be relatively easy to implement elsewhere.

Calibrating spatial interaction models from GPS tracking data: An example of retail behaviour

Global Positioning System (GPS) technology has changed the world. We now depend on it for navigating vehicles, for route finding and we use it in our everyday lives to extract information about our locations and to track our movements. The latter use offers a potential alternative to more traditional sources of movement data through the construction of trip trajectories and, ultimately, the construction of origin-destination flow matrices. The advantage of being able to use GPS-derived movement data is that such data are potentially much richer than traditional sources of movement data both temporally and spatially. GPS-derived movement data potentially allow the calibration of spatial interaction models specific to very short time intervals, such as daily or even hourly, and for user-specified origins and destinations. Ultimately, it should be possible to calibrate continuously updated models in near real-time. However, the processing of GPS data into trajectories and then origin-destination flow matrices is not straightforward and is not well understood. This paper describes the process of transferring GPS tracking data into matrices that can be used to calibrate spatial interaction models. An example is given using retail behaviour in two towns in Scotland with an origin-constrained spatial interaction model calibrated for each day of the week and under different weather conditions (normal, rainy, windy). Although the study is small in terms of individuals and spatial context, it serves to demonstrate a future for spatial interaction modelling free from the tyranny of temporally static and spatially predefined data sets.

Study of time link calibration based on GPS carrier phase observation

The time link calibration of Global Positioning System (GPS) based on carrier phase observation has been performed in the past few years. However, the calibration accuracy is expected to be improved. In this paper, an approach of time link calibration for reducing the uncertainty is proposed. The mechanism and procedure for time link calibration based on GPS are investigated in depth firstly. And then GPS data processing method and strategies in calibration are discussed, where the precise time transfer solution (PTTSol) software is also developed. The experiment of zero baseline time link is employed, of which the standard deviation (STD) value of clock difference reaches 0.03 ns. The root mean square (RMS) value of clock difference in long distance time link is 0.11 ns which compared with the international GPS service (IGS) value. At last, one calibration campaign was organized at the national time service center (NTSC) from modified Julian day (MJD) 57598 to MJD 57616. With a travel receiver forming three common clock difference (CCD) modes, the estimated uncertainty of the calibration value was 0.13 ns, which neglected uncertainty of 1 pulse per second (1 PPS) signal relative to the local time reference point.

The Magnitude of Diurnal/Semidiurnal Atmospheric Tides (S1/S2) and Their Impacts on the Continuous GPS Coordinate Time Series

The site displacement due to diurnal and semidiurnal atmospheric tides (S1/S2) is often neglected in the routine precise GPS data processing. We recall the S1/S2 modeling method and show the magnitude of the S1/S2 tides under the Center of Mass (CM) frame. The results show that the annual amplitudes caused by both S1 and S2 tides exceed 1 mm for stations near the equator. The impact of S1/S2 on the 24-h Global Positioning System (GPS) solution is typically at the sub-mm level, and the scatter of the S1/S2 caused displacement difference increases with the decreasing latitude for northern hemisphere stations, among which the maximum Standard Deviation (STD) reaches up to 1.5 mm, 1 mm and 0.7 mm for the Up, East and North components, respectively, at low-latitude stations. We also find that 65% of the stations’ vertical velocity change caused by S1/S2 is larger than 1%, among which the maximum velocity variation rate reaches more than 40% for some coastal/island stations, while stations with the weighted root mean square reduced account for 65%, 39%, and 38% for the up, east, and north components respectively, in particular for most coastal/island stations. Furthermore, the S1/S2 correction could partly reduce the annual and the semi-annual signals of the global stacked vertical component together with the semi-annual amplitude of the east component. The power of some anomalous harmonics of 1.04 cycle per year also decreased a lot. These results further prove the benefits of S1/S2 correction in the precise GPS data processing.

Study the Effect of New Egypt Wet Mapping Function on Space Geodetic Measurements

Atmospheric water vapour degrades the accuracy of the results of space geodetic observations due to permanent electric dipole moments. It creates excess path lengths by retarding (slowing and bending) the propagation of the electromagnetic waves that are used in global positioning system (GPS) and very long baseline interferometry (VLBI) observations. It is known that the excess path lengths are less than 30~40 cm at the most, and are the primary obstacles of space geodesy because of the highly variable distribution of water vapour in the atmosphere. In this study, we compared modern five wet mapping functions by evaluating their effects on the tropospheric signal delay and position estimates in GPS data processing, and precise Egypt wet mapping function model is derived based on eight stations of radiosonde data well-distributed over and around Egypt (five stations used to estimate new model and other three as check points). To derive the new Egypt wet mapping function, the troposphere is divided into regular small layers. Ray tracing technique of actual signal path traveled in the troposphere is used to estimate tropospheric slant delay. Real GPS data of five stations (RTK-Network methods) were used for the assessment of new model against the available international models. These international models include Niell (NMF), Black & Eisner (B&EMF), Ifidas (IFMF), Hearing (HMF), and UNBabc MF. The data were processed using Bernese software version 5.0. The results indicate that the new Egypt wet MF model is the best model at Egypt region and has improved the wet tropospheric delay estimation up to 23.3 percent at five degree elevation angles.

Analysis of ocean tide loading displacements by GPS kinematic precise point positioning: a case study at the China coastal site SHAO

Ocean tide loading (OTL) displacement amplitudes and phase lags of SHAO site are estimated by global positioning system (GPS), kinematic precise point positioning (PPP) and spectral analysis using 19 years of continuous GPS observations. In kinematic PPP, the 66 additional harmonic displacement parameters are replaced by the three time-varying displacement parameters without a priori modelled OTL displacements. By comparing the results with predictions from hybrid regional/global models, we are able to demonstrate that GPS/model agreements are at the level of 0.2 mm (horizontal) and 0.6 mm (vertical) for the four lunar constituents, 0.4 mm (horizontal) and 1.35 mm (vertical) for the four solar/sidereal constituents, and 0.2 mm (horizontal) and 0.3 mm (vertical) for the three long-period constituents. Finally, we conclude that GPS-derived lunar constituents can substitute for the model corrections in GPS data processing and the accuracy of GPS-derived solar/sidereal constituents needs to be improved by further studies.

Research on parallelized real-time map matching algorithm for massive GPS data

In construction of smart city, numerous vehicles’ trajectory data are produced by Global Positioning System (GPS) to track their real time location. When these GPS data are processed by map matching, results can be used to support a large number of ITS applications such as real time road condition calculation, inspection of traffic event and emergency treatment. However, as the fast explosive growth of monitored vehicle number, massive GPS data proposes overwhelming challenges for map matching. Consequently, traditional map matching algorithms can hardly satisfy high demands for matching speed and accuracy. Therefore, a real time map matching algorithm for numerous GPS data is proposed to guarantee high matching accuracy and matching efficiency. Meanwhile, it can meet demands of GPS data processing required by the monitor of numerous vehicles within the city. Main contributions of the method are: (1) A Kalman filter based correcting algorithm is proposed to improve the matching accuracy of the traditional topological algorithm on the complicated road sections such as intersections and parallel roads. (2) Based on the Spark streaming framework, the serial map-matching algorithm is converted into a parallelized map-matching algorithm, which significantly improves the processing efficiency of the map matching. (3) A gridding method being applicable to the parallelized algorithm was proposed by the paper. The GPS data in the same grid were allocated to the same computing unit to improve the efficiency of the parallelized computation. Experimental results show that the matching accuracy of the algorithm demonstrated by the paper is increased by 10%; the matching efficiency is 25% higher than same amount of stand-alone computers. A cluster of 15 computers that operates the proposed algorithm is capable for the real time map matching for GPS data produced by 800 thousand vehicles, which can effectively and extensively support the lastingly increased demand for processing numerous GPS data.

Investigation into the Effect of Atmospheric Particulate Matter (PM2.5 and PM10) Concentrations on GPS Signals.

The Global Positioning System (GPS) has been widely used in navigation, surveying, geophysical and geodynamic studies, machine guidance, etc. High-precision GPS applications such as geodetic surveying need millimeter and centimeter level accuracy. Since GPS signals are affected by atmospheric effects, methods of correcting or eliminating ionospheric and tropospheric bias are needed in GPS data processing. Relative positioning can be used to mitigate the atmospheric effect, but its efficiency depends on the baseline lengths. Air pollution is a serious problem globally, especially in developing countries that causes health problems to humans and damage to the ecosystem. Respirable suspended particles are coarse particles with a diameter of 10 micrometers or less, also known as PM10. Moreover, fine particles with a diameter of 2.5 micrometers or less are known as PM2.5. GPS signals travel through the atmosphere before arriving at receivers on the Earth’s surface, and the research question posed in this paper is: are GPS signals affected by the increased concentration of the PM2.5/PM10 particles? There is no standard model of the effect of PM2.5/PM10 particles on GPS signals in GPS data processing, although an approximate generic model of non-gaseous atmospheric constituents (<1 mm) can be found in the literature. This paper investigates the effect of the concentration of PM2.5/PM10 particles on GPS signals and validates the aforementioned approximate model with a carrier-to-noise ratio (CNR)-based empirical method. Both the approximate model and the empirical results show that the atmospheric PM2.5/PM10 particles and their concentrations have a negligible effect on GPS signals and the effect is comparable with the noise level of GPS measurements.

Addressing location uncertainties in GPS-based activity monitoring: A methodological framework.

Location uncertainty has been a major barrier in information mining from location data. Although the development of electronic and telecommunication equipment has led to an increased amount and refined resolution of data about individuals' spatio-temporal trajectories, the potential of such data, especially in the context of environmental health studies, has not been fully realized due to the lack of methodology that addresses location uncertainties. This article describes a methodological framework for deriving information about people's continuous activities from individual-collected Global Positioning System (GPS) data, which is vital for a variety of environmental health studies. This framework is composed of two major methods that address critical issues at different stages of GPS data processing: (1) a fuzzy classification method for distinguishing activity patterns; and (2) a scale-adaptive method for refining activity locations and outdoor/indoor environments. Evaluation of this framework based on smartphone-collected GPS data indicates that it is robust to location errors and is able to generate useful information about individuals' life trajectories.