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Vehicle Tracking System Approaches: A Systematic Literature Review

2024, International Journal of Computer Science and Mobile Computing (IJCSMC)

The efficiency, security, and privacy of vehicle tracking systems can be improved through the development of a sophisticated real-time tracking system, the implementation of security measures to safeguard vehicles and their cargo, and the proposal of innovative solutions and best practices. This research adopts a comprehensive approach, which includes an extensive review of existing literature, the design and implementation of the tracking system, and systematic evaluations of its impact on transportation and logistics. The findings of this study demonstrate that the advanced tracking system provides accurate and up-to-date information on vehicle locations and performance, while the integration of security measures enhances the protection of vehicles and cargo. Furthermore, this research identifies the current state of vehicle tracking technology, identifies areas for improvement, and offers recommendations to advance the field. Overall, this study contributes to the progress of vehicle tracking technology by offering practical solutions, promoting data privacy, and optimizing compatibility, while also providing valuable insights into the performance and significance of these systems in the realm of logistics and transportation.

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Open Access

Peer-reviewed

Research Article

Performance of GPS/GPRS tracking devices improves with increased fix interval and is not affected by animal deployment

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom

Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Affiliation British Trust for Ornithology, The Nunnery, Thetford, United Kingdom

Affiliations CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Universidade do Porto, Vairão, Portugal, CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal, BIOPOLIS Program in Genomics, Biodiversity and Land Planning, Universidade do Porto, Vairão, Portugal

Roles Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

• Marta Acácio, 
• Philip W. Atkinson, 
• João Paulo Silva, 
• Aldina M. A. Franco

• Published: March 30, 2022
• https://doi.org/10.1371/journal.pone.0265541
• Reader Comments

The use of GPS tracking technologies has revolutionized the study of animal movement providing unprecedentedly detailed information. The characterization of GPS accuracy and precision under different conditions is essential to correctly identify the spatial and temporal resolution at which studies can be conducted. Here, we examined the influence of fix acquisition interval and device deployment on the performance of a new GPS/GSM solar powered device. Horizontal and vertical accuracy and precision of locations were obtained under different GPS fix acquisition intervals (1min, 20 min and 60 min) in a stationary test. The test devices were deployed on pre-fledgling white storks ( Ciconia ciconia ) and we quantified accuracy and precision after deployment while controlling for bias caused by variation in habitat, topography, and animal movement. We also assessed the performance of GPS-Error , a metric provided by the device, at identifying inaccurate locations (> 10 m). Average horizontal accuracy varied between 3.4 to 6.5 m, and vertical accuracy varied between 4.9 to 9.7 m, in high (1 min) and low frequency (60 min) GPS fix intervals. These values were similar after the deployment on white storks. Over 84% of GPS horizontal positions and 71% of vertical positions had less than 10m error in accuracy. Removing 3% of data with highest GPS-Error eliminated over 99% of inaccurate positions in high GPS frequency intervals, but this metric was not effective in the low frequency intervals. We confirmed the suitability of these devices for studies requiring horizontal and vertical accuracies of 5-10m. For higher accuracy data, intensive GPS fix intervals should be used, but this requires more sophisticated battery management, or larger batteries and devices.

Citation: Acácio M, Atkinson PW, Silva JP, Franco AMA (2022) Performance of GPS/GPRS tracking devices improves with increased fix interval and is not affected by animal deployment. PLoS ONE 17(3): e0265541. https://doi.org/10.1371/journal.pone.0265541

Editor: Laurentiu Rozylowicz, University of Bucharest, ROMANIA

Received: June 19, 2021; Accepted: March 3, 2022; Published: March 30, 2022

Copyright: © 2022 Acácio et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This research was funded by Natural Environment Research Council (NERC) and Engineering and Physical Sciences Research Council (EPSRC), via the NEXUSS CDT Training in the Smart and Autonomous Observation of the Environment (NE/N012070/1). Funding for this project was also provided by NERC via the EnvEast DTP (NE/ K006312), Norwich Research Park Translational Fund, University of East Anglia Innovation Funds and Earth and Life Systems Alliance funds. This research also benefited from FEDER Funds through the Operational Competitiveness Factors Program - COMPETE and by national funds through Fundação para a Ciência e Tecnologia (FCT) within the scope of the project POCI-01-0145-FEDER-028176. JPS was funded by the FCT project SFRH/BPD/111084/2015. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declare that this study was performed as an integral part of MA’s PhD research, to assess the extent to which deployment on animals and GPS fix interval reduces the accuracy and precision of the location data obtained by tracking devices. These devices were optimised for the author’s animal movement research studies through a partnership between the author’s institutions, but have also been made commercially available via a U.K. charity, the British Trust for Ornithology. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

The collection of animal movement data has substantially benefited from rapid technological advances. New tracking technologies enable researchers to unravel novel patterns of animal behavior and collect detailed spatial and temporal resolution data that can inform species conservation and management [ 1 ]. High accuracy Global Positioning System (GPS) technology devices are now commonly used to track wildlife across taxa and environments and have improved the study of animal movement and habitat use [ 2 , 3 ], revolutionizing the field of movement ecology [ 4 ].

The GPS location data can be archived or remotely transmitted by the tracking devices. The ability to remotely transmit data is particularly convenient and allows researchers to track animals that are difficult to recapture and to collect data at higher fix intervals, since data is recovered regardless of the animals’ movements or the device memory [ 3 ]. There are several transmission protocols, but Global System for Mobile communications / General Packet Radio Service (GSM/GPRS) has become widely used enabling worldwide transmission of large quantities of GPS data with reduced communications costs [ 1 ]. The combination of affordable remote transmission of data with solar power energy led to an improvement in device longevity and an exponential increase in tracking data collection [ 3 ].

The spatial resolution of the location data obtained by GPS tracking devices can vary due to environmental and technical reasons. The main environmental sources of variability in GPS device performance are topography [ 5 , 6 ] and habitat [ 7 – 10 ]. GPS accuracy tends to decrease in areas with closed canopy forests [ 11 – 17 ] (but see [ 18 ]), where GPS fix acquisition rates are reduced given the attenuation of GPS signal resulting from poor sky availability [ 6 , 7 , 17 , 19 ]. Moreover, in solar powered devices, fix acquisition rates can be reduced due to poor charging conditions [ 17 ]. Accuracy variability can also result from technical aspects related with signal acquisition, for example, the number [ 20 ] and geometry of satellites in the sky [ 19 ], which is quantified by the dilution of precision (DOP) metric. Higher DOP values indicate lower accuracy and can be due to poor satellite configuration, low number of satellites available, or increased triangulation errors due to clustering of satellites [ 16 , 19 , 20 ]. Both the number of satellites [ 5 , 13 , 21 ] and DOP [ 13 , 18 , 19 , 22 ] are metrics that can be used to identify and eliminate low accuracy locations, but these methods tend to be poor predictors of fix accuracy [ 7 , 17 , 23 ].

After attaching the GPS device to the animal (hereafter, device deployment), the morphology, movement and behavior of animals can also influence both accuracy [ 7 , 24 ] and fix acquisition success [ 13 , 25 – 27 ] (but see [ 28 ]), hence device accuracy should be quantified before and after deployment. Stationary tests can be used to quantify performance before deployment, by comparing the distance between the estimated location given by the tracking device and the true location obtained by an independent method. These tests can provide realistic assessments of location error and determine device accuracy [ 13 , 17 , 22 , 23 ]. However, it is difficult to assess device performance after deployment, as it requires knowing the exact positions of the animals after deployment [ 28 , 29 ], thus accuracy after deployment is normally assessed using pets [ 7 , 10 ].

It is important to quantify the spatial resolution of data obtained from tracking devices and provide accuracy estimates to the locations used in research applied to conservation and policy making [ 1 ]. Low horizontal GPS accuracy can detrimentally affect habitat selection studies, leading to poor model precision [ 30 – 33 ], while low vertical accuracy can be critical when determining flight altitude [ 34 , 35 ], collision risk with human infrastructures [ 36 – 38 ], and for determining 3D habitat utilization distributions of airborne animals [ 37 , 39 ]. Determining ways to identify low accuracy positions would enable researchers to increase the quality of the location datasets obtained and minimize the constraints caused by low accuracy GPS locations.

With an increasing use of GPS tracking technology, new devices are currently being designed and developed. Differences in hardware and software can influence the performance of tracking devices [ 6 , 15 , 30 , 35 ], therefore it is critical to assess their accuracy and precision in order to understand their applicability in ecological studies. Here, we describe a novel GPS/GPRS wildlife tracking device and quantify its horizontal and vertical accuracy and precision in stationary tests and after deployment on large birds. We examine device variability and assess if GPS-Error, a metric calculated by the GPS device, can be used to identify low-accuracy locations. We assess the performance of the devices in field conditions and discuss their use in ecological and conservation studies.

Materials and methods

Gps/gprs devices.

The Movetech Telemetry Flyways-50 is a compact Quad-band GPS/GPRS unit, a 22% efficiency solar cell, a Lithium-Ion battery, and a nylon plastic 3-D printed housing. The device weight starts at 23g. In this test we used the 50g model suitable for deployment on large birds, such as white storks [ 40 – 42 ] or Spanish imperial eagles [ 43 ]. The GPS/GPRS unit contains a GPS module with an on-board chip antenna. The GPS determines the 3D fix coordinates (horizontal and altitude above the ellipsoid) when 4 or more satellites are in view. The device can be programmed to log GPS data from 1 second to 24 hours, allowing for different day and night intervals. The intervals can be updated over the air, permitting an adaptation of the schedule to new environmental conditions.

The GPS unit provides an estimate of the positional error (hereafter GPS-Error ), which considers the maximum latitude/longitude position displacement in meters with a probability of 67% (i.e., ± 1 standard deviation). This metric is calculated directly by the GPS module and is more reliable than using single metrics of error (e.g. Horizontal Dilution of Precision or number of satellites used to obtain the fix) [ 44 ]. The data can be transferred to Movebank [ 45 ], and then visualized and downloaded for further analysis.

The GSM/GPRS unit, coupled with an agnostic SIM-card, provides global cellular connectivity and there is no external antenna, minimizing drag and interference with animal movements. These are archival devices with a memory for over 60,000 records, reducing the risk of data loss when the animal is in areas without GPRS network.

Accuracy and precision in stationary test

We assessed the accuracy (closeness of the GPS locations to a known location, in meters) and precision (closeness of the GPS locations to each other, in meters) of the devices in a stationary test, using 11 GPS/GPRS tracking devices, fully sealed within a nylon plastic housing of medium thickness (between 1.5–2 mm) and ready for animal deployment. The stationary test was completed on a triangulation station located in Southern Portugal. The surrounding landscape is characterized by low altitude, slightly undulating plains, with large areas of non-irrigated agricultural land and low density of cork ( Quercus suber ) and holm-oak trees ( Quercus ilex ). We placed all the devices on top of a triangulation station simultaneously, at about 2 m above the ground, with a clear and uninterrupted view of the sky and programmed the devices over the air at three fix intervals: collecting GPS data every 1 min, 20 min and 60 min.

Horizontal accuracy was calculated as the distance between the coordinates obtained by the devices and the precise coordinates of the triangulation station, provided by the Direção-Geral do Território (DGT) [ 46 ]. Vertical accuracy was calculated as the difference between the altitude above the ellipsoid of the top of the triangulation station, and the altitude obtained by the devices. Negative vertical accuracy values are obtained when the GPS device’s altitude value is higher than the true altitude, and positive values result from a reading smaller than the true altitude. Hence, we quantified biases in under or overestimation of vertical locations.

The horizontal precision was determined using the mean and standard deviation of the geodesic distance between all locations obtained by the tracking devices, and the vertical precision as the mean absolute difference between all altitude readings of each device.

We performed a Kruskal-Wallis statistical analysis to assess differences in accuracy and precision between devices. We used data from all devices to compare the accuracy and precision of the positions collected at different fix intervals.

Identification of inaccurate positions

We examined if the GPS-Error metric, calculated by the device, could be used to identify horizontal and vertically inaccurate positions (horizontal and vertical locations with more than 10 m error). Location error was classified in three categories: 11–20 m, 21–30 m and larger than 30 m. For each tracking device, we excluded 1%, 3%, 5% and 10% of the positions with highest GPS-Error and determined the proportion of locations with above errors remaining in the dataset. We compared the reliability of GPS-Error at identifying the locations with the highest vertical and horizontal error for the three device schedules tested.

Accuracy and precision after deployment on birds

To assess the accuracy and precision of GPS devices before deployment, we performed a stationary test on 17 GPS-GPRS devices, programmed with 20 min fix interval, fully sealed within a reinforced housing (between 3–4 mm thickness) and ready for deployment on white storks. The GPS devices were left in the triangulation station for a minimum of 4 days and a maximum of 15 days. We calculated the horizontal and vertical position of the triangulation station by averaging 3 GPS positions and 3 altitude readings collected with a Ashtech ProMark 220 and an Ashtech 660 external antenna, on differential GPS mode (dGPS). The dGPS provided readings with a horizontal accuracy of 0.98 m (± 0.07 m) and vertical accuracy of 0.57 m (±0.42 m). By using the dGPS coordinates instead of the coordinates provided by DGT, we were able to replicate this protocol to calculate the precise location of the white stork nests and reliably compare the performance of the GPS devices before and after deployment.

After the stationary test, the 17 devices were deployed on white stork pre-fledging chicks ( Ciconia ciconia ) on nests located in the same region as the stationary test (approximately 50 km radius), in order to control for possible GPS sources error, such as different topography or habitat. The white stork chicks tagged were approximately 50 days old, had a minimum wing length of 400 mm and minimum weight 2.9 kg. The device and harness weighted less than 3% of the storks’ body weight. The loggers were back-mounted using a Teflon harness, with a weak link consisting of biodegradable cotton stitches below the sternum [ 40 , 42 ]. This study was carried out in accordance with the recommendations of Instituto da Conservação da Natureza e das Florestas and was approved by the Animal Welfare & Ethical Review Board from the School of Biological Sciences at the University of East Anglia. Licenses to deploy the loggers were granted by the Instituto da Conservação da Natureza e das Florestas (license number 364/2020/CAPT to 368/2020/CAPT).

The nests were located on top of trees providing the devices a clear and uninterrupted view of the sky. To calculate the precise horizontal and vertical position of the nest, we averaged 3 GPS coordinates collected with the dGPS on top of the nest. To calculate the tracking device accuracy and precision after deployment we considered the GPS positions collected during the first 7 days after deployment to guarantee the data was obtained prior to fledgling, as white stork juveniles do not fledge before 65 days. Horizontal and vertical accuracy, and horizontal and vertical precision of the devices before and after deployment were calculated as described above. We performed a Kruskal-Wallis statistical analysis to assess differences in accuracy and precision before and after deployment. All analysis were performed in R software [ 47 ], and distances calculated using package geosphere [ 48 ].

Stationary test

During the stationary test, we collected a variable number of GPS fixes per device using three GPS fix collection intervals (1 min, 20 min and 60 min), with a 100% fix acquisition rate ( Table 1 ).

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https://doi.org/10.1371/journal.pone.0265541.t001

There was a significant decrease in horizontal (χ2 = 508.07, df = 2, p-value <0.001) and vertical (χ2 = 168.23, df = 2, p-value <0.001) accuracy and horizontal (χ2 = 108.41, df = 2, p-value <0.001) and vertical (χ2 = 361.90, df = 2, p-value <0.001) precision with increasing GPS fix acquisition intervals ( Fig 1 ). The horizontal accuracy in the 1 min fix interval was 3.40 m (±3.10 m) and vertical accuracy was 4.95 m (±4.12 m) and decreased to 6.50 m (±8.34 m) and 9.69 m (±19.28 m) horizontal and vertical accuracy, respectively, in the 20 and 60 min fix interval. Vertical location error was approximately symmetric around zero during the longer fix intervals; during short intervals, the vertical errors were always positive, indicating a consistent underestimation of true altitude. Precision was also influenced by the fix collection interval. In the 1 min interval, the horizontal precision was 4.93 m (± 4.15 m) and vertical precision 3.60 m (± 5.90 m). In the 60 min interval, the horizontal precision was 9.15 m (± 9.46 m) and vertical precision 14.31 m (± 24.95 m), with intermediate values in the 20 min interval (horizontal precision = 6.14 m ± 5.46 m, vertical precision = 8.79 m ± 9.17 m) ( Table 1 ).

Horizontal (A) and vertical (B) accuracy, and horizontal (C) and vertical (D) precision in meters of devices programmed with fix intervals of 1 minute, 20 minutes and 60 minutes. The box represents 25, 50 and 75% of the data and the error bar represents 5% and 95% of the data.

https://doi.org/10.1371/journal.pone.0265541.g001

There was significant variability in accuracy between devices both in the 20 min (χ2 = 82.46, df = 9, p-value <0.001) and 60 min interval (χ2 = 22.62, df = 6, p-value <0.001), however, all devices consistently increased in accuracy in higher frequency GPS fix collection intervals ( Fig 2 ).

Horizontal (A) and vertical (B) accuracy of tracking devices programmed to collect GPS locations every 20 and 60 minutes. The error bars represent 95% confidence intervals.

https://doi.org/10.1371/journal.pone.0265541.g002

The proportion of horizontally accurate positions (location error < 10 m) varied according to the device ( Fig 3 ). Approximately 98% and 96% of the positions had a horizontal error below 10 m in the high frequency 1 min and 20 min intervals, respectively. The proportion of accurate locations declined to 83%, in the 60 min fix interval. Vertical accuracy also declined from 98% in the high frequency interval to approximately 71% in the 60 min fix intervals. GPS-Error provided a good metric to identify the locations that were less accurate in high intensity fix intervals. Eliminating 3% of the data with the highest GPS-Error obtained with the 1min fix interval, reduced 99% of positions with ≥10 m horizontal and vertical errors. GPS-Error was not effective at identifying the inaccurate locations in the less intensive schedules ( Fig 3 ).

Percentage of locations with horizontal and vertical error larger than 10 meters for each device, and after removing the points with largest GPS-Error and remaining with 99%, 97%, 93%, 95% and 90% of the original data. The dash line indicates 1% of locations with vertical and horizontal errors larger than 10 meters.

https://doi.org/10.1371/journal.pone.0265541.g003

Performance after deployment

In total, across all 16 devices we collected 7,333 positions during the stationary test and 5,204 GPS positions during the deployment test. Horizontal accuracy did not change after deployment of the devices on white storks (χ2 = 3.80, df = 1, p-value = 0.051), the mean accuracy was 4.21 m (± 18 m) before and 4.10 m (± 15 m) after deployment ( Fig 4 ). Vertical accuracy improved after deployment (χ2 = 43.72, df = 1, p-value <0.001), from 7 m (± 71 m) to 6 m (±56 m). Both horizontal and vertical precisions improved after deployment. Horizontal precision was 7.10 m (± 23 m) before and 6.72 m (± 19.7 m) after deployment (χ2 = 4543.2, df = 1, p-value <0.001), and vertical precision improved from 11m (± 85 m) to 10 m (± 67 m) after deployment (χ2 = 6824.0, df = 1, p-value <0.001) ( Table 1 ).

Cumulative frequency of (A) horizontal and (B) vertical errors (in m) of 16 GPS/GPRS devices before deployment (grey dashed line) and after deployment on white storks (black line). Shaded areas represent the standard deviation of the errors.

https://doi.org/10.1371/journal.pone.0265541.g004

In this study, we quantify the accuracy and precision of Flyway 50 Movetech Telemetry tracking devices and assess its suitability for studies that require high spatial resolution. Horizontal (3.40 m at 1 min fix interval) and vertical (4.95 m at 1 min fix interval) accuracies improved with decreasing fix intervals and were not negatively affected after deployment on birds. This accurate spatial resolution data enables ecological and behavioral studies that require highly accurate and precise information.

GPS fix interval

The fix interval influenced the accuracy and precision of the devices, with a loss of 3.10 m and 4.74 m in horizontal and vertical accuracy, respectively, from the 1 min to 60 min interval (6.50 m horizontal and 9.69 m vertical accuracy at 60 min fix interval). While we found significant variability in device performance, all except one device, had lower accuracy in the 20 min and 60 min fix intervals compared to the device programmed with 1 min fix intervals. Thus, these results support previous findings that longer fix intervals have a negative effect on location accuracy [ 9 , 24 , 49 ]. The GPS units store information on the satellite constellation of the previous fix for a period of time (ephemeris retention), which increases the performance of the device when calculating a new location [ 9 ], by increasing GPS location acquisition success [ 6 ] and providing a fix in a shorter period of time [ 14 ], usually designated as a warm start. However, Cain III et al. [ 6 ] did not find an effect of fix interval on accuracy and Jiang et al. [ 14 ] found that the positions obtained with longer fix intervals (60 min) had lower DOP than in shorter intervals. Forin-Wiart et al. [ 10 ] found higher location errors in the 5 min interval, compared to 15 and 60 min intervals. They proposed that given the high temporal correlation between fixes, a low accuracy location would influence the following GPS position, decreasing the overall accuracy of the device. Our findings do not support this theory, even with similar fix intervals and in similar, open area habitat. This difference in results highlights the importance of testing the GPS units from different manufacturers, as they might produce different results [ 6 , 15 , 30 ]. Moreover, with newly developed loggers that collect data in different intervals according to the battery performance (e.g. dynamic fix transmitters [ 17 ]), the fix interval should be taken into consideration when accounting for device accuracy.

After deployment on white storks, we did not find a decrease in horizontal accuracy. In fact, the devices performed slightly better after deployment than before (increase in 1 m vertical accuracy and 0.38 m in horizontal precision and 1 m in vertical precision).

The performance of GPS devices can be influenced by environmental factors, such as topography [ 6 ] and habitat [ 7 , 23 ]. Vegetation structure and proximity to buildings might also decrease the sky availability and reflect the GPS signal, which increases location error [ 23 ]. In our study design, we avoided topography and habitat bias by performing the stationary tests geographically close to the deployment locations. However, the stationary test before deployment was performed with our tested devices in close proximity to each other (less than 2cm apart) and on the cement structure the triangulation station, which could have decreased the accuracy of the devices before deployment due to the reflection of the GPS signal.

Moreover, after deployment, the storks were on nests located on top of high trees with uninterrupted view of the sky, which could have slightly increased the accuracy and precision of the devices. Other studies have found that animal movement [ 7 , 16 , 26 ], behavior [ 25 , 27 ] morphology of the animal [ 26 ] and tag attachment method [ 50 ] can restrain the signal reception. The angle of the GPS antenna in relation to the sky has been found to influence the performance of the device, with lower fix acquisition success [ 8 , 28 ] and lower accuracy [ 10 , 22 , 51 ] when the antenna is not directly facing the sky. When estimating the post-deployment device accuracy and precision, we prevented animal movement and behavior bias by deploying the devices on birds before fledging. However, device position varied between 0°, when the bird is lying on the nest, and close to 80° when the bird is standing ( Fig 5 ). Despite this large variation in antenna position, white stork chicks spend a large proportion of the time lying on the nest (pers. obs.), therefore the influence of GPS antenna position on device performance was likely negligible.

https://doi.org/10.1371/journal.pone.0265541.g005

Finally, although GPS signal travels through leaves, tree trunks and animal’s bodies, there is a reduction of the signal strength and the degree of attenuation depends on the material and distance that the signal has to travel [ 52 ]. Our deployed devices were tested within a reinforced housing, with thicker nylon (between 3–4 mm thickness), which might have increased the number of low accuracy positions when compared to the initial stationary test ( Table 1 ). Nevertheless, after deployment the tested devices proved to be highly accurate, with over 84% and 71% GPS positions with less than 10 m horizontal and vertical error, respectively.

Elimination of inaccurate positions

Despite slight losses of accuracy with fix interval, the tested devices were still highly accurate. Combining all fix intervals during the stationary test, 95% of the positions were within 11 and 18 m horizontal and vertical error, respectively. Montgomery et al. [ 31 ] found that in small-scale ecological studies (<5 ha size patch), using a 10 m resolution categorical raster, a mean GPS accuracy of <5 m was needed to obtain 90% accurate inferences. An accuracy of <5m could be obtained with the tested devices tested if the fix interval was set between 1 and 20 min intervals. Using such high frequency fix intervals can thus help identify species’ fine-scale movement patterns and habitat requirements, critical for designing suitable conservation actions in local scales, though it can also compromise the device’s battery life and the duration of the study [ 53 ].

Despite the high accuracy of the devices, there was a small number of locations with errors above 250 m, both horizontally and vertically. These highly inaccurate positions can lead to a decrease in performance of habitat selection models [ 32 ]. For studies requiring very highly accurate GPS locations, such as studies in fragmented landscapes (e.g. urban areas, [ 23 ]), or studies of collision with human infrastructures (e.g. wind-farms [ 37 , 38 ]), it is important to be able to identify and eliminate outlier positions to increase GPS accuracy.

The most commonly used metrics to filter large error in GPS positions are the number of satellites [ 5 , 13 , 21 ] and DOP [ 13 , 14 , 18 , 19 , 22 , 23 ]. However, these can result in the elimination of a large proportion of the dataset, including accurate positions, while not eliminating all inaccurate positions [ 13 , 16 , 21 , 54 ]. Estimating the true altitude error and relate it to the horizontal error, produces acceptable results in eliminating poor quality fixes in comparison with single metric models [ 55 ]. This method however is only suitable for broad-scale habitat analysis, and since it relies on knowing the exact altitude of the animal, it is not appropriate for arboreal or flying species.

The devices tested in this study provide a GPS-Error estimate that proved to be effective at identifying low accuracy positions in short fix intervals (1 min), but it was not possible to replicate the results with longer fix intervals. Moreover, since the performance of the device is related to the habitat, by excluding locations with large positional errors there can be a bias in excluding data related to a single habitat [ 13 , 21 , 29 , 30 , 33 , 55 ]. Using species-specific GPS metadata, such as unrealistic speed, turning angles or distances travelled between consecutive fixes is effective in eliminating large positional errors [ 54 ]. However, this method is dependent on the mobility of the species, as well as the fix interval [ 55 ]. Other modelling techniques, such as using sensors (accelerometers and magnetometers) and GPS drift-corrected dead reckoning, have successfully increased the accuracy of animal movement estimates in low intensity GPS schedules [ 56 ]. This is particularly important in non-solar tags in which, in order to maximize the lifespan of the battery, longer fix intervals are used.

Supporting information

https://doi.org/10.1371/journal.pone.0265541.s001

Acknowledgments

The authors give special thanks to Dr. Inês Catry, Bruno Herlander Martins, Carlos Pacheco and Dr. Kate Rogerson for help during fieldwork and deployment of the devices on white storks, and to Liga para a Proteção da Natureza (LPN) for support during fieldwork.

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• 46. Direcção Geral do Território. Redes Geodésicas [Internet]. 2021. https://www.dgterritorio.gov.pt/geodesia/redes-geodesicas
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Original research

Systematic review of best practices for gps data usage, processing, and linkage in health, exposure science and environmental context research, amber l pearson.

1 CS Mott Department of Public Health, Michigan State University, Flint, MI, USA

Calvin Tribby

2 Department of Population Sciences, Beckman Research Institute of City of Hope, Duarte, California, USA

Catherine D Brown

3 Department of Geography, Environment and Spatial Sciences, Michigan State University, East Lansing, Michigan, USA

Jiue-An Yang

Karin pfeiffer.

4 Department of Kinesiology, Michigan State University, East Lansing, Michigan, USA

Marta M Jankowska

Associated data.

bmjopen-2023-077036supp001.pdf

bmjopen-2023-077036supp002.pdf

All data relevant to the study are included in the article or uploaded as supplementary information. All data are available in online supplemental file 2.

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 vary from study to study, making comparisons and reproducibility challenging.

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

A systematic review.

Data sources

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

Eligibility criteria

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

Data extraction and synthesis

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

Conclusions

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO registration number

CRD42022322166.

STRENGTHS AND LIMITATIONS OF THIS STUDY

• This systematic review used standard database search to find articles as well as citation assessment of review articles to encompass a comprehensive set of articles.
• Article types included association focused, methodological development, feasibility studies and tracking tool comparisons providing a broad scope of Global Positioning System (GPS) applications in human health research.
• We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines to report on results.
• We did not consider the necessity of differential reporting for certain health outcomes or subpopulations studied.
• A potential limitation of this review was the omission of studies using mobile phone apps to collect GPS data.

Introduction

Global Positioning System (GPS) devices are increasingly used in health research to quantify health risks or negative exposures (eg, air pollutants, exposure to fast-food restaurants) and positive exposures or outcomes (eg, green spaces, outdoor physical activity), often embedding GPS data in environmental and contextual features. Applications of using GPS to track participants in air pollution, physical activity, active living, drug and alcohol addiction, obesity and exposomics research are being developed. 1–4 Inclusion of mobility in exposure research offers the opportunity to expand our understanding of how movement through space and time contributes to healthy and unhealthy exposures. The use of GPS has emerged as a more accurate and specific measure of individual mobility and results in ‘dynamic’ exposures as compared with ‘static’ exposure measures. Static measures are commonly taken from a home or administrative unit at one point in time, 5 6 leading to a ‘stationary bias’. 7 GPS measurement of mobility should theoretically bring about a better alignment of dose and response relationships between contextual exposures and health outcomes. 8 However, increased application of GPS technology is ushering in new and varied study designs, data collection methods and analytical processing pipelines. This makes cross-study comparison and identification of emergent findings across the literature difficult.

In general, GPS-based health research aims to quantify risks and benefits of environmental and contextual features and typically involves deploying GPS devices to be worn for various lengths of time by participants. Crucial steps in this branch of research consist of the subsequent cleaning, processing and linking of GPS data to other measures, such as survey data, anthropometrics, physical activity data, neighbourhood characteristics or spatially explicit environmental exposures. Increasingly, multiple devices may be worn/used by participants and these data are linked to GPS data, including accelerometers, personal air pollution monitors and wearable cameras. At each stage in this research practice, decisions on data handling and processing are made which may influence the measurement of outcomes, risks or behaviours, and ultimately the study findings. Yet, little research discusses the impact of such decisions, how to report key decisions and/or evaluates best practices for these steps.

The significant variation in techniques used and methodological aspects reported in GPS-based health and exposure research makes building evidence consensus difficult. Few studies examine how differences in data collection methodology or data processing may affect relationships between health outcomes and exposure measures, although there are important exceptions (eg, ref 9 ). Still, through the process of collecting and analysing GPS data, researchers have several methodological choices which clearly impact the quality and completeness of data collected. For example, several aspects of GPS usage are directly controlled by the researcher, including the choice of GPS device, which has been shown to be important for measurement of location accuracy, consistency and duration, 10 11 although most research-grade devices have similar performance when unobstructed. 12 Similarly, participant instruction, compliance and length of GPS wear time have also been shown to be important factors in generating reliable and representative mobility data. 13–15 Other aspects of GPS usage relate to nuisances with the technology that affect completeness and accuracy of the data, including positional accuracy, uncertainty and missing data. For example, one aspect of GPS data collection involves characterising the amount of noise in the data (ie, error in calculation of the device location due to the low number of satellites available or multipath errors where GPS signals are reflected off buildings). Noise may then be filtered and removed from the dataset by researchers, based on some acceptable positional, altitude or speed error thresholds. Besides noise removal, missing data can also be the result of signal loss, which may occur in similar scenarios as noise or due to errors in operating the GPS device. In such cases, the resulting dataset includes gaps in the time series. Some studies fill these gaps using an imputation method (eg, last known location up to a specified time limit), which has been shown to affect the linkage process and ultimately data loss. 16 Yet, it is unknown how consistently spatial health and exposure research studies report these aspects of GPS usage and processing.

As the use of GPS devices in health and exposure research continues to increase, there is a considerable need to identify best methodological practices for data collection and processing. Without consistent methodological reporting, it will become impossible to gauge quality of studies and comparability of results. We define data collection and processing as the steps and procedures employed to collect GPS data, clean it and prepare it for analysis in human health research, but does not include applying data transformations for creation of new variables (eg, trip classification, time spent at home, etc). Identifying collection and processing best practices will aid authors in reporting, making amalgamation and meta-analysis of results easier, and will increase reproducibility across studies. In an effort to promote transparency, replicability and rigour in studies using GPS devices worn by individuals to study human health (mirroring recent advances in physical activity 17 and life course epidemiology 18 research) this systematic literature review aimed to: (1) identify and review best practices for GPS data collection and processing; (2) quantify reporting of GPS data best practice elements in published studies; and (3) discuss best practice applications with examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage. Importantly, this systematic review is the first step in ultimately a two-step process aimed at first understanding the current state of best practices and reporting, and second, building research community consensus on which practices should be reported in research using GPS for the measurement of human mobility and/or exposure assessment for the purposes of health-related research. The focus of this review is the first step.

Best practice manuscripts

First eight best practice manuscripts were specifically selected due to familiarity with the literature by the two senior authors (first and last). When reviewing best practice manuscripts, themes for relevant considerations and issues related to GPS data usage, processing and linkage were extracted and tallied across the articles (n=8). 9 12 13 19–23 Themes were discussed and agreed on by the senior authors based on their combined experience of 40+ years of GPS data collection and processing. Some best practice manuscripts included empirical data to showcase these issues, while others were primarily conceptual. For each theme, subelements or specific practices discussed in the best practice manuscripts were used as data extraction elements for the reviewed manuscripts. The subthemes were defined based on the stage at which they are employed in GPS data collection, processing or linkage. These practices then formed the basis of risk of bias assessment for the reviewed manuscripts.

We identified eight themes related to GPS usage and processing considerations among the best practice manuscripts ( table 1 ). Under each theme, multiple ways of reporting this issue or decision/practice were found. For example, some manuscripts reported methods of imputation while others reported the percentage of GPS coordinates that were imputed. The most common themes were GPS data missingness and noise considerations (in 88% of manuscripts), followed by participant compliance and sampling frequency.

Themes and specific best practices discussed as relevant for GPS data collection and processing, identified in best practice manuscripts (n=8)

GPS, Global Positioning System.

The first theme identified in the best practice manuscripts was the model/brand of GPS device used. While research-grade devices appear to perform similarly in terms of accuracy when unobstructed, battery life, satellite information and fix time may vary between units. 12 Thus, reporting the model/brand of the device may be useful to make comparisons across studies, and to evaluate the study protocol for wear time and participant compliance. The second theme was sampling frequency, or epoch, for capturing coordinates. The best practice manuscripts discussed the variety of sampling frequencies commonly observed (from 1 s to 5 s) and the influence this decision has on processing time and costs. 20 23 Consideration of the study population (eg, children) may influence the sampling frequency selected and may also depend on the importance of fine precision in location detection. 12 The third theme was wear time. Considerations include the research question of interest and the rarity of the behaviour or exposure under study. 12 Yet, some claim that studying behaviours that occur in specific places (eg, physical activity in a park) or seasonal variation in behaviour may require much longer wear time 20 than the typical GPS study (often 4 days which may or may not include a weekend day, 13 or 7 days 22 ). The fourth theme was GPS data missingness, which may be the result of signal loss, battery issues, non-compliance or memory storage capacity. One study reported missing data for around 70% of the total monitoring time 21 and another reported over 17% missing for signal loss alone. 19 The fifth theme was noise considerations, whereby signals are scattered by buildings of certain materials, 21 or when indoors. 13 Detection of noise in the GPS data may include filtering for unrealistic speed and acceleration values. 12 The sixth theme was imputation, which entails estimation of coordinates for times with missing GPS data. Most commonly, nearest neighbour or the last known valid point 13 19 20 methods were reported as imputation options. The seventh theme was linkage of GPS data to a variety of other data, including other sensor data (eg, personal air pollution monitor). 22 The linkage process itself may also result in data loss for the analytical phase. 19 The eighth theme was data inclusion, which may vary by subpopulation (eg, age) and lead to substantial data loss. 19 Importantly, one manuscript empirically tested the potential for non-compliance to protocols to bias results, whereby certain ethnic or socioeconomic groups may be more likely to have lower rates of compliance and thus not be included in analysis. 9

We did not tally the reporting of GPS device locational accuracy as an issue because there can be variation in performance among research-grade devices, though previous research indicated that most were able to detect location within metres when signal was unobstructed. 24 Smoothing is discussed in two best practice manuscripts, but its definition can be ambiguous ranging from upsampling to remove possible errors to using kernel density estimators after initial data processing. 12 20 The definition of smoothing was ambiguous depending on the researcher. Kerr et al define it as reduction of ‘random noise in complex datasets by focusing on the primary pattern in the data and replacing points outside that pattern with plausible points that match the pattern’ 12 (p 536). Jankowska et al 20 discuss smoothing of data through kernel density functions, which may be considered a postprocessing procedure to develop activity space metrics. The question of smoothing may better be understood in the context of noise processing decisions. While some manuscripts determined how noise was identified, few of those manuscripts disclosed how this was rectified. A better consensus on the definition, methods and reportable results of smoothing may be needed. Thus, due to lack of clear definitions, we elected to not include smoothing as a theme.

Information sources, search strategies and keywords used in systematic review

An extensive search of electronic databases was conducted, including PubMed, Scopus and Web of Science, for relevant studies in the English language that focus on GPS data cleaning, processing or linkage and human health. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for reporting. 25 The search terms used were (gps OR ‘geographic positioning system’ AND NOT ‘general practitioner*’ OR ‘general practice*’) AND (clean* OR imput* OR link* OR process* OR filter* OR join* OR stitch*) AND (human OR public) AND health’]. Exact search strategies, limits and filters for each database are provided in online supplemental file 1 . The initial search was intended to capture a broad range of studies across disciplines concerned with GPS data and human health (n=3182) (see figure 1 for a flow diagram of this process). Studies not published in English were excluded at the search stage. Studies that were not peer-reviewed original research were excluded at the screening stage. A protocol of this review strategy was registered with PROSPERO (ID: CRD42022322166). While we did not include reviews in this systematic review, we did conduct backward citation searching by checking the bibliographies of reviews that appeared in our search and cited methods in included manuscripts for additional eligible studies which met at least one of the inclusion criteria. The search was conducted on 24 October 2023 and included any publications prior to that date.

Flow chart of systematic review process. GPS, Global Positioning System.

Supplementary data

This systematic review focuses on the published best practices for GPS data usage, processing and linkage in public health research as related to environmental and contextual exposures. Few studies explicitly focus on best practices, and instead include analytical details in methodological sections of a study. We first compiled the few best practice manuscripts. These were identified as manuscripts focused on discussion of best practices in the usage and processing of GPS data (with or without empirical data included). These manuscripts differ from review manuscripts in that the focus was not on systematically reviewing existing literature. Then, to cast a wide net to obtain as many studies as possible that may inform this review, included studies were required to meet at least one of the following criteria: (1) feasibility/pilot studies or protocols involving GPS in populations for exposure/context and health research purposes and containing empirical data; (2) the development of a novel linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health outcomes; (4) derived variable methods (including algorithms) using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary). We permitted all manuscripts using the same cohort to be included because different research questions might yield different processing protocols. Existing literature reviews and commentaries on existing research were excluded.

Not included in the scope of this review were: (1) studies on GPS devices not worn by humans (eg, ref 26 ); (2) exclusive environmental measurement without any health component (eg, ref 27 ); (3) anonymised GPS data not linked to individuals (eg, ref 28 ); (4) the use of GPS to monitor people for healthcare or emergency services (eg, dementia patient tracking) (eg, ref 29 ); (5) comparisons of geocoding techniques or the spatial accuracy of GPS devices (eg, ref 30 ); (6) studies not containing empirical GPS data (eg, ref 31 ); and (7) studies that used a mobile phone or smartwatch for GPS tracking (eg, ref 32 ) because of the heterogeneity in these devices/apps and their unknown calibration, and source(s) of locational data (ie, potential reliance on cell towers rather than satellites).

Data extracted from reviewed manuscripts

Using the themes and subelements found in the best practice manuscripts ( table 1 , P1–P8), we extracted information about adherence to or reporting of each subelement from all other studies included in our review. The full dataset can be found in online supplemental file 2 .

P1–P4 (GPS device, sampling frequency, wear time, GPS missing data)

For P1, GPS brand and model were identified. For non-commonly used brands, custom devices or those that were inclusive of other monitoring devices (eg, air pollution devices), we classified brand and model as ‘other’. For studies noting more than one type of GPS device, we noted both devices’ brands and models. For P2, the sampling frequency in seconds was extracted. P3 wear time was coded by days of wear time as specified in the protocol or study guidelines, which does not necessarily indicate adherence by participants. Some protocols had unique wear periods by subgroups within a study or indicated a range of days. In these cases, the average wear time was reported. For protocols requesting less than a day of wear time, we calculated the proportion of a 24-hour day. P4 was coded as the per cent of GPS signal loss or missing data before any imputation was performed.

P5 and P6 (noise and imputation)

As there is no overarching definition of ‘noise’ in the GPS and health literature, we define noise generally as GPS data that is not missing but is likely erroneous due to signal issues from interference in the environment or satellite connectivity. The method of noise detection was included if the study specified how noise was identified (eg, rapid speed changes, satellite inaccuracy readings, rapid elevation changes). Some studies reported visually assessing the data and removing points they considered erroneous. For these studies the method of noise detection was coded as ‘manual’. If no specific discussion of noise was included, the subsequent subcategories in P5 were coded as ‘not applicable’ (n/a). Noise removal or correction thresholds (P5a) were extracted if specified (eg, altitude >800 m), as well as the per cent of points identified as noise (P5b). If a specific tool (P5c) was indicated in the manuscript to handle noise, it was coded as the tool name or the custom software or toolbox. For some studies the authors also indicated additional manual noise cleaning, which was also indicated under P5c. For manuscripts that cited commonly used tools, we included default noise parameters if they were findable and unless otherwise specified by the manuscript authors.

P6 (imputation performed) indicated if the manuscript specified its imputation choices (yes or none) or did not mention imputation. If a manuscript indicated no imputation was performed, or did not specify if imputation was performed, all other P6 columns were coded as n/a. P6a indicated the method used (eg, last known point), P6b identified the imputation threshold used to impute missing data, P6c identified the specific tool or algorithm used for imputation and, finally, P6d indicated the per cent of GPS points imputed.

P7 (linkage)

Data considered as ‘linked’ to GPS data were defined as data collected concurrently with the GPS device. This excluded postprocessing linkage with Geographic Information System (GIS) data as well as survey responses or biometrics collected and then appended to individual datasets. For linkage we focused on if data were linked to GPS and the type of data linked, as well as the epoch or interval of linkage (P7a). If more than one data type was linked to GPS, each data type and epoch were recorded in seconds and averaged. When linkage was reported at the trip or location level, those epochs were not used to calculate median values. We also identified the tool used for data linkage (P7b) and the percentage of data lost due to linkage if specified (P7c). For studies that did not link any other data to GPS data, we indicated ‘none’, and all following P7 categories were coded as n/a.

For themes P5 (noise), P6 (imputation) and P7 (linkage), many studies cite other manuscripts for details on processing procedures or decisions. If details on the themes and subelements could be found in those cited manuscripts, they were included as data for the original manuscript.

P8 (data inclusion)

Data inclusion was coded as ‘specified’ or ‘not specified’ (P8) to indicate if the manuscript had indicated how authors deemed a data point, wear period/day or participant’s compliance as valid for study inclusion. If specified, P8a and P8b provide further detail for noted wear periods/days or participant compliance required. For some types of studies, especially feasibility studies, the nature of the research did not require a data inclusion criterion (eg, all available data were used). For such studies we indicated ‘n/a’. For many studies, the authors did not indicate if inclusion criteria were specific to GPS data or more generally applied to linked data (eg, accelerometer non-wear periods). We chose to include any noted data inclusion criteria. Other studies collected data over several periods and we noted the minimum requirements for each data collection period. Similarly, for P8c—per cent of participants lost—many studies did not indicate why participants were lost. Thus, for studies that reported participants lost to GPS data issues, that number is reported, while for studies that did not differentiate, the total number of participants lost is reported.

In addition to practices identified through the best practice manuscript themes, we also extracted year of publication, name of the journal, focal health outcome, risk or behaviour, and type of data linked with GPS data. The best practices were divided into GPS usage practices, or those related to the collection of GPS data in a study, and GPS processing practices, or those related to preparing GPS data for analysis after collection. For each manuscript included, we calculated the total number of practices reported separately for GPS usage and GPS processing practices.

Study selection process

Two reviewers (lead and senior author) conducted title and abstract screening of the articles using the program Covidence. Inclusion/exclusion conflicts between reviewers were identified in Covidence and were resolved in a meeting whereby inclusion criteria were reviewed, and reasons for not meeting criteria were discussed. A total of 255 publications progressed to full-text screening by reviewers ( figure 1 ). Next, bibliographies of excluded review manuscripts 33–46 were checked and methods cited in included manuscripts were checked for possible inclusion. This process yielded an additional 36 manuscripts that were reviewed in full. Finally, within included manuscripts, if additional methods manuscripts were cited as key information sources for GPS data processing, we reviewed those manuscripts (n=21 additional manuscripts). We excluded manuscripts based on the following exclusion criteria: GPS data collection was planned but not yet carried out (eg, protocol without empirical data) (n=10), GPS devices were not used for exposure/contextual measures (eg, the GPS was only used for identifying the coordinates of an individual) (n=52), a mobile phone app was used for collecting coordinates (n=49), GPS device was not used for human mobility (n=7), only anonymised GPS data collected (n=1), a sole focus on GPS device comparison (n=3), a review paper or commentary (n=18), abstract only retrieved (n=11) or not available in English (n=2). Ultimately, 157 total manuscripts were selected for inclusion in this review.

Characteristics of studies included in the systematic review

Table 2 identifies characteristics of studies included. Out of the 157 publications included in this review, 107 were associations between GPS-measured mobility or exposures and health outcomes, 33 47–152 11 were comparisons of GPS tracking with other methods (eg, travel diary), 153–162 22 were feasibility/pilot studies or protocols involving GPS in populations for exposure/context and health research purposes and containing empirical data, 41 163–183 5 were focused on the development of a novel linkage of GPS data to other data intended for research on contextual influences on health 16 184–187 and 12 were focused on derived variable methods (including algorithms) using GPS data in health research. 14 188–198 All papers were published from 2007 onwards. The most common journals of publication were Health & Place and International Journal of Environmental Research and Public Health , followed by American Journal of Preventive Medicine and International Journal of Behavioral Nutrition and Physical Activity (data not shown in tabular form).

Characteristics of studies included in analyses (n=157)

*Studies could include more than one response.

BMI, body mass index; GIS, Geographic Information System; GPS, Global Positioning System.

Of the focal health outcomes and risks, almost half (45.9%) evaluated physical activity, followed by mobility (34.4%), neighbourhood-built environment exposures (29.9%) and other outcomes (eg, therapeutic experience, asthma, community participation) (17.8%) ( table 2 ). The most commonly linked data were accelerometry (54.1%), followed by GIS data (49.0%) and travel diary/log (29.9%).

Consistency in reporting of best practices

Results of evaluating the selected four GPS usage practices and five GPS processing practices are shown in table 3 . In evaluating practices reported for GPS usage, 93.6% reported brand of GPS device (most commonly Qstarz, followed by GlobalSat and Garmin), 91.7% reported model (most commonly Qstarz BT-1000XT) and 88.5% reported GPS sampling frequency (median=15 s). All but one study reported GPS days of wear time (median=7).

Practices reported in included manuscripts (n=157)

*The denominator excludes studies that explicitly state they do not report inclusion criteria due to exploratory, feasibility or other study-specific reasons.

†The denominator excludes previously excluded studies from P8, or cannot define a set number of days to be included in analysis (P8b).

GPS, Global Positioning System; PALMS, personal activity location measurement system.

In evaluating practices reported for GPS processing, only 52.9% of studies reported identifying noise and more specifically their method for noise detection (most commonly speed, elevation and satellite accuracy). Of those that include noise identification, most reported the threshold for noise detection (75.9%) and the tool used for detection (83.1%, most commonly personal activity location measurement system (PALMS)). The noise threshold was most commonly speed >130 km/hour or delta elevation >1000 m (PALMS default parameters). Only 15.7% of these studies reported the per cent of GPS points considered to be noise (median=0.4%). About 31% of studies reported whether they employed imputation. Of those who reported imputing missing GPS data, all of them reported the imputation method, and 81.6% reported the tool. Almost 70% reported the imputation threshold, but only 15.7% reported the number of imputed points (median 15.5%). Of studies that conducted data linkage with GPS data (n=132), all reported which data were linked. However, only 87.9% reported the linkage epoch (median=60 s), 61.4% reported the tool for linkage (most commonly PALMS) and 20.5% reported the data loss through the linkage process (median=11%). About 68% of studies reported the criteria for GPS data inclusion. Yet only 47.7% reported the minutes of data required to be a valid day (median=480) and 58.1% reported the number of valid days required (median=2). Over 90% reported participants lost by compliance criteria (median=3.6%). Of the included studies in this review, 81.5% reported all four GPS usage practices while <5% reported all general GPS processing practices (not including subthemes a–c/d—which were rarely reported).

When evaluating trends in reporting of GPS usage practices over time, there does not appear to be a clear pattern ( figure 2A ). However, all studies conducted prior to 2015, and in 2020 reported all practices. There does not appear to be a trend towards increased reporting over time. Likewise, temporal trends in reporting of GPS processing practices did not show a clear trend ( figure 2B ). But, overall, average reporting scores for GPS processing practices were much lower than those for GPS usage practices.

Average scores and 95% CIs for Global Positioning System (GPS) usage (high=4, low=0; A) and processing (high=5, low=0; B) practices reported, by year.

The aim of this review was to identify best practices of GPS data usage, processing and linkage in spatial health and exposure research, and assess the current state of reporting those practices. We explored the recommendations for reporting methods from best practice literature and then quantified reporting of GPS data best practice elements in published studies. To our knowledge, this is the first systematic review focused on the current state of GPS data usage, processing and linkage reporting, mirroring efforts in allied sciences to promote scientific transparency and replicability.

The themes identified in best practice manuscripts included the model/brand of GPS device used, sampling frequency, wear time, GPS data missingness, noise considerations, imputation, linkage of GPS data to a variety of other data and data inclusion criteria. These themes were each identified in at least 50% of the best practice manuscripts and were then used to assess reporting practices in our systematic review manuscripts. Of all papers included in the review, 81.5% reported GPS usage practices (P1–P3), however, only five papers (3.2%) reported on all GPS processing practices (P4–P6, not including subcomponents).

For our first practice—reporting GPS brand and model—8% of the studies in this review did not disclose the GPS device model used, while 6% did not report brand. This limits understanding of the capability or comparability of devices across studies, as devices have different levels of locational precision and varying lengths of time to acquire a signal. 20 Still, most research-related devices yield similar accuracy when unobstructed. 12 If researchers deploy previously unvalidated devices, this might be an important limitation or weakness of the study’s ability to measure relationships between location and health outcomes. Similarly, reporting GPS device wear protocols is important, as they may affect the reliability and generalisability of findings. We found that all but one study reported the amount of requested wear time, but only 68.2% reported the inclusion criteria for their data, whether that be at the data point, day or person level. Because so much processing must occur to raw GPS data, specificity in what is considered a ‘valid’ point can clarify the quality of the data, as well as assist the researcher in reporting thresholds and other aspects of data cleaning within a manuscript. While some studies operated on a subday level, understanding the parameters for inclusion of observation days is relevant because a study that requires 3 days of GPS wear time may find stronger or weaker associations between exposures and health outcomes than a study that requires 7 days. The 3-day study may overestimate or underestimate a given exposure if, for example, the observation period does not include a representative sample of participants’ extent of activity spaces (eg, weekdays only). Previous research has found that at least 14 days of valid GPS data are required to obtain a representative sample of participants’ activity spaces. 14 However, this may differ depending on the risks or outcomes of interest or the population under study. Future research is needed into GPS wear protocols and processing steps which may affect associations with specific health outcomes or exposures.

Assessment, processing and reporting of missing data via signal loss, noise or linkage was highly inconsistent among studies. Review and reporting of missing data are important aspects of assessing possible bias in a study, especially if GPS or linked data missingness leads to removal of participants from a study. Additional sources of missing data can occur when using GPS models that automatically turn off when they do not sense activity or lose satellite signal, or when a researcher decides to exclude data outside of a specific study area. We found that only 12.1% of studies reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and none reported how much data were removed when rectifying data to a specific study area (not tallied or shown in tables). Of studies reporting amount of data loss from missing GPS data, numbers ranged from 0.1% to 70% of data missing. Larger amounts of missing data may indicate a poorer estimate of GPS-derived metrics, effecting quality of a study and ability to compare results to other studies. Delineation of GPS errors due to device or satellite reception issues and methods for either removing for correcting such errors are important to report in studies because they may affect the strength of associations of environmental exposures or behavioural contexts with health outcomes. For example, noisy GPS data or missing GPS data may occur in dense urban areas, where heat island effects or air pollution may be the strongest. Further, specific participant characteristics may more often take them outside of a study area. Missing GPS data may underestimate participants’ environmental exposures and may bias the associations with the health outcome of interest. Further research is needed into the spatial variation in GPS positional errors and how they relate to specific exposures, 199 which was beyond the scope of this review. Closely related to missing data is the decision of whether to impute missing or noisy data or not. While many studies chose to ignore missing data, some research has found that this can bias results particularly when linking GPS to other data resources like accelerometers. At the minimum, reporting if imputation was performed or not (only 30.6% of studies reported this) will help in identifying if a study may be prone to potential biases. 16

Additionally, we noted that a fair amount of GPS data was lost (median 11%) through linkage to other devices or GIS data. The potential implications of these lost data varied by study type. For example, some studies were only interested in physical activity monitored by accelerometers and therefore used the accelerometer epoch as the standard for linkage. The lost GPS data (which were not matched to accelerometer data) were then likely to have minimal impact on the quantification of physical activity and potentially minimal impact on study results. However, if the focus was to assess where physical activity or in what types of environments it was occurring, the data lost due to linkage could bias the results. In an existing review of studies using GPS units with accelerometers or travel diaries, 17 had missing or unusable data ranging from 2.5% to 95% after linkage. 15 We also noted that very few studies specified how data were kept after linkage, for example, if only data that included both GPS and the linked data resource as kept, or if all GPS data were kept no matter if it had a linkage. Although beyond the scope of this review, we further note that simplistic linkage of GPS data to other sources may lead to uncertainty in estimates of exposure. For example, some studies used a simple intersection between GPS and GIS layers to determine exposure, which assumes that there is no positional error in the GPS or GIS data. This may lead to misclassification of exposure. For example, in a study evaluating time spent in a park, if the GPS points fall outside of the GIS park perimeter, linkage may misrepresent exposure time. Sensitivity analyses based on varying distance thresholds may help determine how variations in distance between GPS data and GIS layers may bias exposure estimates. 200 201 It is possible that missing data which results in removal or participants may bias the results of studies (although this was not formally assessed here). Very few manuscripts performed analysis comparing characteristics of their retained sample compared with participants who had to be removed due to either GPS missing/noisy data or linkage issues (eg, missing at random analysis), with notable exceptions (eg, ref 9 ). In fact, few manuscripts reported how many participants were lost due to GPS data issues, instead amalgamating all lost participants together, regardless of reason for exclusion.

To promote reporting of practices and methods in this research area, we created an example table for best practice reporting ( online supplemental table S1 ). This table provides examples for ways to report practices, reviewers to evaluate and readers to identify GPS data considerations and potential biases. The table was designed based on real examples from the reviewed literature and makes use of the reporting themes identified in this review.

Limitations

While attempting to carry out an exhaustive review, there were certain aspects of GPS reporting and processing which we were not able to evaluate. For example, we did not consider the necessity of differential reporting for certain health outcomes. Future research may wish to provide guidance on an outcome-by-outcome basis (eg, for physical activity, depression, asthma). Moreover, our findings were not separated by subpopulations being studied (eg, child vs adult), though we do understand the need to modify methods should they not be appropriate for the population of interest. Thus, future research may wish to review best practices for each subpopulation and provide relevant guidance. Another potential limitation to our review was the omission of studies using mobile phone apps and smartwatches to collect GPS data. Although these devices are becoming commonplace, the decision was made to focus on ‘research grade’ GPS devices due to mobile phone apps often unknown calibration, source(s) of locational data and lack of homogeneity among these apps. However, by identifying best practices among research-related GPS devices, these practices can be transferred as applicable to mobile phone or smartwatch data collection and processing. Last, our restriction to evaluating studies published in English only is a limitation of this review and future studies pulling from non-English literature would be valuable.

Future research and policy implications

Though this review was focused on identifying best practices and assessing the current state of reporting on those practices, several ancillary areas of future research remain. For example, it is unknown how much uncertainty not correcting or removing locational noise may be introduced to the estimation of exposure or how GPS wear protocols and processing steps could affect the detected associations with health outcomes. Future research may usefully estimate the magnitude of each of these practices and/or data loss on overall uncertainty or bias using a meta-analysis or similar approach. Perhaps the most evident need in future research, based on our findings, is a consensus on which practices should be reported, regardless of study design or research focus, and which practices may be optional. As mentioned in the Introduction section, this second step in our research process will make use of the themes identified in the current systematic review in order to build consensus among experts. With such a consensus, future geospatial health and exposure research will be more comparable, reliable and reproducible.

Because studies using GPS data may be used to quantify harmful exposures, and thus inform policies aimed at protecting the public from those exposures, the designation of minimum reporting for comparisons across studies would allow us to ensure that policies are based on the best available science. Furthermore, enabling meta-analyses to pool findings and create best guidance for policy could be afforded by efforts to standardise reporting.

In summary, because there is currently no consensus for the optimal use or reporting of GPS data in spatial health and exposure research, studies tend to report what they feel is essential, yielding such variety that comparisons across studies are challenging. Throughout this review process, we found a lack of consistency in both reporting and methods. Some manuscripts were meticulous in identifying and reporting their process and procedures, either in the main text or appendix. For other manuscripts, we had considerable difficulty finding processing decisions, criteria or other critical information. This review underscores that the current state of GPS usage and processing practices reporting has significant room for improvement. Details pertaining to acquiring and processing of GPS data are vital so that future studies can fully assess the methods used, identify quality of data inclusion, compile findings in a meta-analysis or draw comparisons across studies.

Supplementary Material

Acknowledgments.

The authors thank Dr David Berrigan, Phil Hurvitz and Steve Mooney for help in conceptualising this research and providing critical feedback.

Correction notice: This article has been corrected since it was published. The funding grant number has been corrected.

Contributors: ALP: conceptualisation, data curation, investigation, methodology, formal analysis, resources, visualisation, writing—original draft, writing—review and editing. CDB, CT, J-AY: data curation, writing—review and editing. KP: conceptualisation, writing—review and editing. MMJ: conceptualisation, data curation, investigation, methodology, resources, visualisation, writing—original draft, writing—review and editing, guaranteed the content of the manuscript.

Funding: AP is funded by the National Cancer Institute (NCI) of the NationalInstitutes of Health (NIH) R01 CA239187.

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Use of Vehicle Probe and Cellular GPS Data by State Departments of Transportation (2021)

Chapter: chapter 2 - literature review.

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5 Probe and Cellular GPS Data Collection Methods Vehicle location information can be collected or extracted in several different ways: • In-vehicle embedded devices. • GPS devices embedded in cell phones. • Cell phone service providers’ networks. • Cell phone applications. In-Vehicle Embedded Devices In-vehicle embedded devices are part of vehicle manufacturer-built systems that collect vehicle telematics data including vehicle location, vehicle performance, and driver behavior. Unlike handheld devices, vehicles are not restrained by the small form factor and therefore necessary components can be distributed throughout the vehicle, while leveraging a vehicle’s power system for operation and vehicle velocity information for enhancing location information (Jagoe 2003). Location information is captured by fusing in-vehicle GPS receiver information and Dead Reckoning (DR) generated information. The DR method uses a previously determined position and the vehicle’s velocity to approximate the vehicle’s location (Cho and Choi 2006). GPS Devices Embedded in Cell Phones Most cell phone devices are equipped with a GPS receiver that can obtain navigation messages from at least three satellites to determine the device’s latitude and longitude (and altitude if using a fourth satellite signal). In addition to receiving a GPS signal, many devices utilize a method called assisted-GPS where wireless network information is used to supplement GPS information. This approach reduces the power consumed by the headset, optimizing start-up and acquisition time, and increasing the sensitivity of the GPS device (Barnes 2003). Cell Phone Service Providers’ Networks Cell phone service providers have the capability of tracking the location of individual devices as they move through the network. There are several network-level tracking methods generally used to locate a cell phone device. The Cell of Origin method approximates a user’s location based on the location of the base station serving the mobile device. This method has low accuracy of 150 to 10,000 meters based on individual cell size in the network (Barnes 2003). This method is supplemented by timing C H A P T E R 2 Literature Review

6 Use of Vehicle Probe and Cellular GPS Data by State Departments of Transportation advance information which calculates the time difference between the start of a radio frame and a data burst, which is used to further approximate a user’s location within a cell (Jagoe 2003). The Estimated Time of Arrival and Angle of Arrival methods use the time necessary for a device’s signal to reach multiple base stations and the direction from which the signal arrives, respectively. This information is then sent to a mobile switch where it is analyzed to generate the approximate location of the mobile device (Barnes 2003, Jagoe 2003). Cell Phone Applications Cell phone applications or apps often utilize device location (collected using a device’s GPS receiver and network information) to enhance user experience. When users install social media, weather, or similar apps that use location information for other purposes, their location informa- tion is captured and used to provide additional services and often resold to third parties (Huang et al. 2018). History of Probe Data for Transportation Applications Probe-Based Speed Data In the early 2000s, transportation agencies identified a need to supplement data from the traditional static sensor technology to better evaluate real-time and historical performance of the transportation system. Early efforts included generating floating car data using a single vehicle equipped with a GPS unit traversing corridors at specific times. The resulting GPS points were snapped to an underlying map to generate metrics such as average speed, travel time, and congestion index (Tong et al. 2005). The use of this floating car method was intermittent, tedious, and time-consuming, and the method proved to be only marginally useful (Pack et al. 2019). Radio camera positioning of cellular phones was also being developed at this same time. This technology used the signal strength of the cell phone at the antenna site and various forms of triangulation to identify the position and speeds of vehicles for use in traveler information and traffic control (Smith et al. 2001). Other methods relied on the cooperation of cellular carriers to mine the signal timing and handoff data emanating from a cellular tower-switching network (Myr 2003). Some agencies have been generating their own probe vehicle data using toll tags. For example, the Florida DOT (FDOT), the Texas DOT, and toll authorities around the country use toll tags to identify and re-identify vehicles as they traverse the toll facility and use that information to calculate speed and travel time between re-identification points (Pack et al. 2019). In 2006, new vehicle probe technology was emerging as a means of continuously monitoring traffic as commercial firms were offering traffic data services based on a variety of methods, the most common of which were based on either cellular geo-location or fleet GPS-based telematics reporting technologies. (Center for Advanced Transportation Technology, University of Maryland and KMJ Consulting Inc. 2011). The use of probe vehicle data for real time operations and planning among agencies was expanded to a larger number of agencies when The Eastern Transportation Coalition (formerly the I-95 Corridor Coalition) created a probe vehicle data marketplace in 2007 (University of Maryland 2007). This request for proposal (RFP) outlined desired requirements for both data and quality that propelled use of probe vehicle data among coalition member agencies and later other agencies across the country. It also resulted in a model data use agreement that has been cited in many publications and RFPs in the years following this initial RFP.

Literature Review 7 Commercial speed data providers have moved away from using a single method, technology, or source to generate probe data, to combining probe data from multiple sources and technolo- gies to create a comprehensive traffic information service (Center for Advanced Transportation Technology, University of Maryland and KMJ Consulting Inc. 2011). These offerings have largely replaced the need for fixed roadside infrastructure to support speed data collection. Origin-Destination Data O-D studies have been a core activity in the world of transportation to better understand travel patterns, identify traffic congestion sources and potential traffic control strategies, better plan transit services, and plan urban development for almost 100 years (McClintock 1927, Mickle 1944, Braff 1948, Blucher 1950). Traditionally, these studies relied on data collected through spotter observations, as well as paper-based and web surveys of travelers. As a result, they repre- sented a small sample of the population and required significant time and effort to administer and process, often producing results many years after commencing the study (Williams 1986, Hartgen 1992, Richardson 2003). For example, even the surveys themselves could be time-consuming because to accurately capture demand variability, multi-day surveys and, more specifically, a 2-week survey duration are necessary (Senbil and Kitamura 2009). Vehicle Telematics-Based Origin-Destination Data With the emergence of probe speed data in the mid-2000s, commercial firms such as AirSage, HERE, and INRIX began capturing O-D data and delivering that data as a product to agencies (Allos et al. 2014). The cost and latency of these products were lower than those of traditional surveys, and sampling rates were higher. Location-Based Services Data In the late 1990s and early 2000s, telecommunications companies, application developers, and content providers were looking to leverage the emerging LBS capabilities aggregated from multiple sensors in mobile devices (Myllymaki and Edlund 2002). Additional technology and algorithm advancement led to more accurate and easier ways to locate mobile devices and track device paths (Barbeau et al. 2008). These developments led to further refinements of the LBS, and produced data to not only identify locations and paths of users and their devices, but also to infer the mode of transportation to differentiate between cars, buses, and other modes (Byon, Abdulhai, and Shalaby 2009). In recent years, the proliferation of mobile apps (like social media apps and weather apps) that capture device location information enhanced the volume of available location data. For exam- ple, location-based social networks enable uploading geotagged content (e.g., photos, videos, and recorded routes), sharing the present location (e.g., “check-in” at Foursquare), commenting on an event at the place where it is happening (e.g., via Twitter), or leaving ratings/tips/reviews for a location (e.g., a restaurant) (Huang et al. 2018). Today, location information extracted from social media and other mobile device applications allows agencies and the private sector to analyze a variety of transportation problems, such as parking supply and demand implications (Mondschein et al. 2020), trip purpose, the reliability of bike share programs (Svartzman et al. 2020), communications during natural disasters affecting the transportation system (Lovari and Bowen 2020, Roy et al. 2020), the analysis of transit services and ride hailing platforms (Kim et al. 2019), safety and mobility performance in smart cities (Oh et al. 2019), and others. Agencies began getting access to O-D and trajectory data based on LBS. Third-party data providers such as StreetLight collect and aggregate LBS data and package it for use by the agencies (Lee and Sener 2017).

8 Use of Vehicle Probe and Cellular GPS Data by State Departments of Transportation Trajectory Data In the last several years, some of the vehicle telematics-based O-D data providers began adding value to the traditional O-D data sets by including associated trajectory data. The new products consist of origins and destinations, as well as waypoints captured between those origins and destinations (Petrone and Franz 2018, Fan et al. 2019). Combining O-D and trajec- tory information provides a comprehensive view of “trips” taken by individual vehicles. These trips provide valuable information previously unavailable, such as routes taken, time to traverse those routes, and variation in travel time and speed along the route. Most of these O-D and trajectory data offerings are sourced directly from vehicle telematics available from integrated sensors and devices from vehicle original equipment manufacturers and vehicle fleets (Sharma et al. 2017). Types and Properties of Current Probe and Cellular GPS Data Available The literature review covered three main classes of probe and cellular GPS data: speed, O-D, and trajectory (sourced from telematics and LBS data). Speed Data Probe-based speed data are derived from vehicles and mobile devices equipped with embedded GPS devices. Speeds and travel times provide real-time or historic average speeds on one or more road segments. To be useful, probe vehicle data must be conflated to a roadway network. Initial commercial offerings relied on Traffic Message Channel (TMC) codes, a reference system designed to give a unique alpha-numeric code to each road segment for purpose of assigning traffic information to that segment (HERE 2015). These TMC codes are typically assigned at significant decision points, interchanges or intersections in an unambiguous format, independent of map vendor. The North America Location Code Alliance created, owns, maintains, and expands the U.S./Canada TMC location code table that adheres to the international standard on location referencing (INRIX 2018). While being the only standardized coding method to uniquely identify roadway segments for the purpose of conveying traffic and other information, the TMC code segmentation reached its limitations in road coverage, the ability to cover new roads more quickly, segment overlap and gapping, and segment resolution, as probe data have become denser and more granular. INRIX introduced XD segments in 2013, a proprietary segmentation that addresses some of the TMC code limitations (Young et al. 2015, INRIX 2018). Similarly, other data providers are offering sub-TMC products to address TMC segmentation limitations (HERE 2015, TomTom 2015). The National Performance Management Research Data Set In July 2013, the Federal Highway Administration (FHWA) procured the NPMRDS to support the Freight Performance Measures and Urban Congestion Report programs, as well as the Moving Ahead for Progress in the 21st Century Act (MAP-21) performance management activ- ities. This data set consists of actual observed average travel times every 5 minutes, 24 hours a day, 7 days a week covering the National Highway System (NHS) as defined by MAP-21 and delivered monthly starting with October 2011. The average travel time data are sepa- rated for freight, passenger, and all traffic. The first version of the NPMRDS was provided by HERE Inc. and sourced from mobile phones, vehicles, and portable navigation devices for passenger vehicles, and from the American Transportation Research Institute’s leveraging of embedded fleet systems for freight vehicles (FHWA Office of Operations and Resource Center).

Literature Review 9 Since February 2017, data are provided by a team led by the University of Maryland Center for Advanced Transportation Technology (Center for Advanced Transportation Technology). The speed and travel time data are provided by INRIX, which leverages its existing source data, including millions of connected vehicles, trucks, and mobile devices that anonymously supply location and movement data. The data cover more than 400,000 segments provided in 5-minute intervals, 24 hours a day (NPMRDS FAQ 2020). Origin-Destination Data Probe-based O-D data contain basic information about trips between two geographic points. It does not contain information about specific routes between those two geographic endpoints. The geographic endpoints are reported as latitude/longitude pairs, but often generalized to zones. The O-D data set provides counts of trips between the origin zone and destination zone for a selected time period, such as workday a.m. peak period. The O-D data may also be broken down by travel mode and vehicle class type as well, such as passenger vehicles, heavy vehicles, medium vehicles, bikes, or pedestrians. (Pack et al. 2019, Southwest Washington Regional Transportation Council 2019, StreetLight 2020). Probe-based O-D data can be combined with traditionally available geodemographic infor- mation for origin and destination zones (Martin et al. 2018). Data may include standard census statistics as well as expanded data elements, such as population size and density, employment statistics, average income, and age, gender, race, and occupation statistics for a zone (Kim et al. 2012, Lawson 2018, StreetLight 2020). Trajectory Data Whereas O-D data provide two data points per trip, trajectory data provide information about the origin, destination, and routes taken between those endpoints. Trajectory data are timestamped location data from vehicles, cell phones, and other GPS-enabled devices throughout the network, often referred as “bread-crumb trail” data. Other data elements can include (Pack et al. 2019): • Unique device/vehicle ID, • Unique trip ID, • Departure time and location (trip origin), • Arrival time and location (trip destination), • Periodic “waypoints” during the trip, including latitude/longitude, where the period can be multiple times per second or once every X minutes, • Instantaneous speed/heading, • ID of road segment for the waypoint, and • Data that describe the path and routes of a trip from an origin to a destination. Trajectory data are available everywhere that probe speed data are collected, which means that it has ubiquitous coverage not otherwise available using traditional O-D surveying and floating car studies. The capture rate for trajectory data varies by region. For example, in Utah INRIX provides trajectory data that cover 3% of all trips in the state (Markovic et al. 2020). Trajectory data can be processed and made available within hours of collection at a higher price than that of data that are delivered weekly or monthly. Location-Based Services Data LBS use smartphones’ GPS technology (or control plane locating for older devices) to geolocate the device and integrate this location information with relevant services and content databases. The content databases provide supporting information such as the road network (digital maps),

10 Use of Vehicle Probe and Cellular GPS Data by State Departments of Transportation business and landmark information (points of interest), and dynamic data such as traffic and weather reports. The user’s past, present, or future location is joined with other context infor- mation to deliver a service (Schiller and Voisard 2004). For use in transportation, LBS data are collected when smartphone apps include code that collects location information from users that opt-in to use the app and report location informa- tion (Cuebiq 2019). The collected data are then packaged as a product that provides anonymous timestamped location information, including dwell times associated with points of interest (Cuebiq 2020). LBS data can then be further enhanced using inferred information such as income, race, education, and family status at the neighborhood level (StreetLight 2020). Current Primary Uses of Probe and Cellular GPS Data According to Pack et al. 2019, probe vehicle data conflated to underlying roadway network and in combination with other data sources can be used to support many different activities including but not limited to the following: Probe-Based Speed Data • Monitoring real-time congestion – Detecting and identifying incidents. – Issuing traveler information. – Conducting work zone monitoring and impact analysis activities. – Detecting the end of the queue. – Comparing real-time speed information to historical trends. – Identifying recurring and non-recurring bottlenecks. – Evaluating regional operations and situational awareness, for example, Metropolitan Area Transportation Operations Coordination (MATOC) (Harrison et al. 2019). • Performance management – Evaluating performance metrics over time: travel time, buffer time, reliability, planning time, and associated indices. – Incorporating data into dynamic performance management dashboards. – Investigating user delay cost. – Meeting legislative requirements, for example, MAP-21 and FAST Act target setting (Pu and Meese 2013, Vandervalk 2018). – Evaluating the worst bottlenecks in a region for a period of time. – Studying trends, including special event, holiday, and seasonal movements. – Exploring the impacts of capital investments prior to, during, and after completion of the project. – Conducting after action reviews, for example, Maryland State Highway Administration (Harrison et al. 2019). – Evaluating winter performance management, for example, the Ohio DOT (Harrison et al. 2019). – Measuring freight performance (Habtemichael et al. 2015). – Measuring truck and auto performance (Eshragh et al. 2015). • Planning and research – Identifying problems. – Prioritizing projects. – Performing safety analyses. – Implementing public participation/information campaigns. – Conducting before-and-after studies.

Literature Review 11 – Reporting project assessments, for example, the New Jersey DOT Tactical-level Asset Management Plan (Harrison et al. 2019). – Evaluating at-grade railroad crossings (Hafeez and Kasemsarn 2017). • Traveler information – Providing real-time travel time information on dynamic message signs (DMS). – Delivering network performance information. – Distributing special event and holiday guidance. Origin-Destination and/or Trajectory Data • Real-time traffic pattern analysis – Evaluating corridor demands based on observed vehicle trips. – Evaluating the effectiveness of implemented detours around incidents and congestion and observing self-detouring patterns. – Identifying temporary but significant changes in origin-destination patterns and route utilization resulting from special events or closures. • Signals performance and turning movement analysis (Center for Advanced Transportation Technology, INRIX 2020) – Observed distribution of speeds through intersections. – Arrival on green metrics. – Turning movement metrics. – Intersection travel times. – Approach speeds. – Intersection delays. • Multi-modal system utilization – Discovering mode transitions and influence traveler decisions based on network utilization patterns. – Analyzing freight patterns, for example, “Quantifying Long-Haul Trucks on Florida’s Highways” (StreetLight 2020). – Assessing the economic impact of mode utilization, for example, “Virginia Bike Tourism: Measuring Cycling’s Economic Impact” (StreetLight 2020). • Planning and research – Traditional origin-destination analysis to identify trip origins and destinations, work versus leisure travel, and many other data points. – Waypoint analysis to determine if traffic in a specific area (e.g., state, county, traffic analysis zone, or business center) originated in the same area, neighboring area, or another more distant location. This can identify whether certain corridors are mainly local travel or pass- through corridors. – Trip cluster analysis to evaluate the effectiveness of existing transit service or identifying areas in need of new transit service. – Analysis of trip patterns that may affect critical freight corridors or ports. – Infrastructure investment decisions, for example, analyzing metrics to know where to place electric vehicle charging station locations (StreetLight 2020). Data Quality, Accuracy, and Reliability Probe vehicle data are available from only a sample of total vehicles flowing through the network but are generally considered sufficient enough to estimate travel time distributions through the network. Early studies have shown that even a small percentage (1%-2%) can be adequate for producing data appropriate for certain congestion-related performance measures (Hunter et al. 2009).

12 Use of Vehicle Probe and Cellular GPS Data by State Departments of Transportation Early studies attempted to quantify the quality of probe-based speed data and its ability to be used for certain applications on various classes of roadways based on AADTs or signal spacing. However, the density, granularity, coverage, and quality of data has improved to where it has become a key source of information to support agency operations and planning efforts on most roads for most use cases (Pack et al. 2019). Extensive validation efforts have shown that probe vehicle data are accurate and sufficient enough to support most traffic operations and planning efforts (Vander Laan and Zahedian 2019-2020), as well as policy research (van der Loop et al. 2019). The U.S. DOT and many agencies have performed their own analysis of the NPMRDS, which is based on probe speed data, to support target setting and performance management (Turner and Koeneman 2018, Refai et al. 2017). Validation efforts for LBS and trajectory-related data are still in the early stages, but initial independent validation efforts have shown that the data are extremely representative of actual conditions. For example, multiple aerial photo studies have validated INRIX Trips data (Jordan et al. 2016a, 2016b, 2017), and the Maryland Transportation Institute’s methodologies for computing trips by mode from LBS data have also been validated (Zhang et al. 2020). Gaps in Data Usage Information Many agencies consider travel time and probe-generated speed data as the top three data elements they would be interested in acquiring if available (Pack and Ivanov 2014). However, prior research has shown that agencies struggle to work with the data due to the size of the data. Data for even a small state like Rhode Island can include hundreds of millions of trajectory records and billions of waypoints. It is for these reasons that third-party analytics packages are starting to be adopted by agencies.

Over the last decade, state departments of transportation (DOTs) have begun to use vehicle probe and cellular GPS data for a variety of purposes, including real-time traffic and incident monitoring, highway condition, and travel demand management. DOTs are also using vehicle probe and cellular GPS data to inform system planning and investment decisions.

The TRB National Cooperative Highway Research Program's NCHRP Synthesis 561: Use of Vehicle Probe and Cellular GPS Data by State Departments of Transportation documents how DOTs are applying vehicle probe and cellular GPS data for planning and real-time traffic and incident monitoring and communication.

In December 2021, an erratum was issued.

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• Kapil Mundada 12 ,
• Sumedh Patti 12 ,
• Tejas Rajguru 12 ,
• Puskraj Savji 12 &
• Sayali Shambharkar 12  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 754))

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Real-time information regarding buses is made available to passengers via the GPS and GSM-based Real-Time Bus Monitoring system, which aims to enhance public transportation. The system uses GPS and GSM technologies to track the location and estimated arrival time of buses and transmit this information to commuters’ mobile devices. This technology addresses the common problem of long waiting times at bus stops and the uncertainty of bus arrival times. The system's benefits are numerous, including the ability to reduce waiting times, optimize bus routes, and reduce traffic congestion. Commuters can track the arrival times of their buses and make informed decisions about their travel plans, which can save them time and effort. The system can also assist transportation authorities in making decisions about bus routes and improving the overall efficiency of public transportation. Moreover, the system's effectiveness can be enhanced through the use of data analytics and artificial intelligence techniques. By analyzing data on bus ridership and travel patterns, transportation authorities can identify areas of high demand and adjust bus schedules and routes accordingly. This approach can help reduce overcrowding on buses, improve travel times, and enhance the overall user experience.

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Real Time Bus Monitoring System

Leveraging GPS and SMS-Based Bus Tracking Architecture for an Efficient Transportation

Customized Internet of Things-Based Bus Tracking and Management System

Lin W-H, Zeng J (1999) Experimental study on real-time bus arrival time prediction with GPS data. Transp Res Rec J Transp Res Board (1666):1019

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Chandurkar S, Mugade S, Sinha S, Borkar P (2013) Implementation of real-time bus monitoring and passenger information system. Int J Sci Res Publ 3(5)

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Acknowledgements

We express our gratitude to our supervisor, Prof. Kapil Mundada, for providing us with his invaluable guidance and critical evaluation throughout the duration of this project. Additionally, we extend our appreciation to the Department of Instrumentation Engineering at VIT Pune, for their generous support and provision of essential resources that enabled us to successfully undertake this endeavor.

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Department of Instrumentation and Control Engineering, Vishwakarma Institute of Technology, Pune, India

Kapil Mundada, Sumedh Patti, Tejas Rajguru, Puskraj Savji & Sayali Shambharkar

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Correspondence to Kapil Mundada .

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Singidunum University, Belgrade, Serbia

ITM University, Gwalior, Madhya Pradesh, India

Shyam Akashe

Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

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Mundada, K., Patti, S., Rajguru, T., Savji, P., Shambharkar, S. (2023). Smart Bus Real-Time Tracking System Using GSM and GPS Module. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Infrastructure and Computing. ICT4SD 2023. Lecture Notes in Networks and Systems, vol 754. Springer, Singapore. https://doi.org/10.1007/978-981-99-4932-8_46

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DOI : https://doi.org/10.1007/978-981-99-4932-8_46

Published : 26 September 2023

Publisher Name : Springer, Singapore

Print ISBN : 978-981-99-4931-1

Online ISBN : 978-981-99-4932-8

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• DOI: 10.5120/21107-3835
• Corpus ID: 45333173

Vehicle Tracking, Monitoring and Alerting System: A Review

• Sumit S. Dukare , D. A. Patil , K. Rane
• Published 18 June 2015
• Engineering, Computer Science
• International Journal of Computer Applications

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