Global Positioning System Loggers Research Articles

Dead-reckoning facilitates determination of activity and habitat use: a case study with European badgers (Meles meles)

BackgroundStudies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited.MethodsCollars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type.ResultsNightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively).Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances.ConclusionsThis study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

Where Are Czech Adolescents Active? The Patterns of Movement and Transport Behavior in Different Active Living Domains.

To understand the environmental determinants of physical activity (PA), precise spatial localization is crucial. This cross-sectional study focuses on the spatiotemporal distribution of PA among Czech adolescents (n = 171) using Global Positioning System loggers and accelerometers. The results showed that adolescents spent most of their time in sedentary behavior, with 57.2% and 58.5% of monitored time at home and school, respectively. The park and playground had the lowest proportion of sedentary behavior but also the lowest amount of moderate to vigorous PA (MVPA). However, when considering the time spent in each domain, the highest proportion of MVPA was seen in publicly accessible playgrounds (13.3% of the time). Chi-square analysis showed that the relative distribution of different PA intensities did not differ across spatial domains. Based on these results, the authors propose 2 key strategies for increasing MVPA in adolescents: Increase the time spent in activity-supportive environments, such as parks and playgrounds, and design techniques to increase MVPA at home and school settings.

Linear and landscape disturbances alter Mojave desert tortoise movement behavior

IntroductionAnimal movements are influenced by landscape features; disturbances to the landscape can alter movements, dispersal, and ultimately connectivity among populations. Faster or longer movements adjacent to a localized disturbance or within disturbed areas could indicate reduced habitat quality whereas slower or shorter movements and reduced movements may indicate greater availability of resources. The Mojave desert tortoise (Gopherus agassizii) is a threatened species that is challenged by anthropogenic disturbances.MethodsWe studied tortoise movements using Global Positioning System (GPS) loggers at multiple sites in the Mojave Desert of Nevada and California. Tortoises at our sites encountered localized, linear human infrastructure, including paved roads, dirt roads, and fences, as well as landscape-scale disturbances [wildfire, off highway vehicle use (OHV), livestock grazing area]. We fit two-state (moving and encamped) Hidden Markov models to GPS logger data to infer how tortoise movement behavior relates to anthropogenic and natural features.ResultsWe found that temporal covariates, individual-level random effects (intercepts), and sex best explained state transition probability in all sites. We compared relationships between tortoise movement and linear disturbances, which varied depending on site and context. Tortoises made longer movements within the OHV recreation area, near most dirt roads, and near a low-traffic paved road, indicating that tortoises avoid these habitat disturbances. Conversely, tortoises made shorter movements in areas of higher slope and near highways, suggesting that these features may restrict movement or provide resources that result in prolonged use (e.g., forage or drinking locations). Tortoises that encountered fences around utility-scale solar installations were more active and made longer movements near fences, indicative of pacing behavior.DiscussionThese results provide insight into how different disturbances alter tortoise movement behavior and modify tortoise habitat use, providing information that can be used to manage tortoise habitat.

Impact of test, vaccinate and remove protocol on home ranges and nightly movements of badgers in a medium density population

In the British Isles, the European badger (Meles meles) is thought to be the primary wildlife reservoir of bovine tuberculosis (bTB), an endemic disease in cattle. Test, vaccinate or remove (‘TVR’) of bTB test-positive badgers, has been suggested to be a potentially useful protocol to reduce bTB incidence in cattle. However, the practice of removing or culling badgers is controversial both for ethical reasons and because there is no consistent observed effect on bTB levels in cattle. While removing badgers reduces population density, it may also result in disruption of their social behaviour, increase their ranging, and lead to greater intra- and inter-species bTB transmission. This effect has been recorded in high badger density areas, such as in southwest England. However, little is known about how TVR affects the behaviour and movement of badgers within a medium density population, such as those that occur in Northern Ireland (NI), which the current study aimed to examine. During 2014–2017, badger ranging behaviours were examined prior to and during a TVR protocol in NI. Nightly distances travelled by 38 individuals were determined using Global Positioning System (GPS) measurements of animal tracks and GPS-enhanced dead-reckoned tracks. The latter was calculated using GPS, tri-axial accelerometer and tri-axial magnetometer data loggers attached to animals. Home range and core home range size were measured using 95% and 50% autocorrelated kernel density estimates, respectively, based on location fixes. TVR was not associated with measured increases in either distances travelled per night (mean = 3.31 ± 2.64 km) or home range size (95% mean = 1.56 ± 0.62 km2, 50% mean = 0.39 ± 0.62 km2) over the four years of study. However, following trapping, mean distances travelled per night increased by up to 44% eight days post capture. Findings differ from those observed in higher density badger populations in England, in which badger ranging increased following culling. Whilst we did not assess behaviours of individual badgers, possible reasons why no differences in home range size were observed include higher inherent ‘social fluidity’ in Irish populations whereby movements are less restricted by habitat saturation and/or that the numbers removed did not reach a threshold that might induce increases in ranging behaviour. Nevertheless, short-term behavioural disruption from trapping was observed, which led to significant increases in the movements of individual animals within their home range. Whether or not TVR may alter badger behaviours remains to be seen, but it would be better to utilise solutions such as oral vaccination of badgers and/or cattle as well as increased biosecurity to limit bTB transmission, which may be less likely to cause interference and thereby reduce the likelihood of bTB transmission.

Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.

A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.

Modelling the Energy Consumption of Driving Styles Based on Clustering of GPS Information

This paper presents a novel approach to distinguishing driving styles with respect to their energy efficiency. A distinct property of our method is that it relies exclusively on the global positioning system (GPS) logs of drivers. This setting is highly relevant in practice as these data can easily be acquired. Relying on positional data alone means that all features derived from them will be correlated, so we strive to find a single quantity that allows us to perform the driving style analysis. To this end we consider a robust variation of the so-called "jerk" of a movement. We give a detailed analysis that shows how the feature relates to a useful model of energy consumption when driving cars. We show that our feature of choice outperforms other more commonly used jerk-based formulations for automated processing. Furthermore, we discuss the handling of noisy, inconsistent, and incomplete data, as this is a notorious problem when dealing with real-world GPS logs. Our solving strategy relies on an agglomerative hierarchical clustering combined with an L-term heuristic to determine the relevant number of clusters. It can easily be implemented and delivers a quick performance, even on very large, real-world datasets. We analyse the clustering procedure, making use of established quality criteria. Experiments show that our approach is robust against noise and able to discern different driving styles.

Linking wild turkey hen movement data to nesting behavior

AbstractMonitoring of wild turkey (Meleagris gallopavo) females to obtain representative measures of apparent nesting and nest survival rates has long been an important component of wild turkey management. In the interest of improving estimates of apparent nesting and nest survival rates, we assessed the feasibility of inferring reproductive behavior of wild turkeys from remotely acquired movement data. We ascertained detailed nesting stage chronologies using Global Positioning System (GPS) data downloaded from 44 females fitted with transmitters that had both a very high frequency (VHF) beacon and a GPS logger. Then we tracked females using VHF to confirm nest locations and nest fate in north and north‐central Florida. Using twice hourly GPS derived locations, we evaluated 3 predictive machine learning models (Chi‐squared automatic interactive detection [CHAID], exhaustive CHAID, and multivariate adaptive regression splines [MARSpline]) for their ability to accurately classify nesting behaviors (laying and incubating stages) that were confirmed via visual inspection of all potential nest sites. Incubation stage was readily identifiable by the spatial signatures present in each female's GPS‐based movement track across all models (up to 92% true positive and as low as 8% false positives in exhaustive CHAID models), whereas the laying stage was more difficult to identify (up to 65% true positive and as low as 35% false positives in exhaustive CHAID models). Daily averages of movement metrics generally achieved better performance in predictive models than data sets with more frequent sampling (i.e., 30‐minute daytime locations). Our results indicated that exhaustive CHAID models performed best in identifying nesting stages, using movement metrics from GPS data. With continued development, these models may facilitate and improve large‐scale wild turkey monitoring.

Decision rules for determining terrestrial movement and the consequences for filtering high-resolution global positioning system tracks: a case study using the African lion (Panthera leo).

The combined use of global positioning system (GPS) technology and motion sensors within the discipline of movement ecology has increased over recent years. This is particularly the case for instrumented wildlife, with many studies now opting to record parameters at high (infra-second) sampling frequencies. However, the detail with which GPS loggers can elucidate fine-scale movement depends on the precision and accuracy of fixes, with accuracy being affected by signal reception. We hypothesized that animal behaviour was the main factor affecting fix inaccuracy, with inherent GPS positional noise (jitter) being most apparent during GPS fixes for non-moving locations, thereby producing disproportionate error during rest periods. A movement-verified filtering (MVF) protocol was constructed to compare GPS-derived speed data with dynamic body acceleration, to provide a computationally quick method for identifying genuine travelling movement. This method was tested on 11 free-ranging lions (Panthera leo) fitted with collar-mounted GPS units and tri-axial motion sensors recording at 1 and 40 Hz, respectively. The findings support the hypothesis and show that distance moved estimates were, on average, overestimated by greater than 80% prior to GPS screening. We present the conceptual and mathematical protocols for screening fix inaccuracy within high-resolution GPS datasets and demonstrate the importance that MVF has for avoiding inaccurate and biased estimates of movement.

Estimation of Surgical Resident Duty Hours and Workload in Real Time Using Electronic Health Record Data

To explore the use of electronic health record (EHR) data to estimate surgery resident duty hours and monitor real time workload. Retrospective analysis of resident duty hours logged using a voluntary global positioning system (GPS)-based smartphone application compared to duty hour estimates by an EHR-based algorithm. The algorithm estimated duty hours using EHR activity data and operating room logs. A dashboard was developed through Plan-Do-Study-Act cycles for real-time monitoring of workload. Single tertiary/quaternary medical center general surgery residency program with approximately 90 categorical, preliminary, and integrated residents at eight clinical sites. Categorical, preliminary, and integrated surgery residents of all clinical years who volunteered to pilot a GPS application to track duty hours. Of 2,623 work periods by 59 residents were logged with both methods. EHR-estimated work periods started later than GPS logs (median 0.3 hours, interquartile range [IQR] -0.1 - 0.3); EHR-estimated work periods ended earlier than GPS logs (median 0.1 hours, IQR -0.7 - 0.3); and EHR-estimated duty hour totals were less than totals logged by GPS (median -0.3 hours, IQR -0.8 - +0.1). Overall correlation between weekly duty hours logged by EHR and GPS was 0.79. Correlations between the 2 systems stratified from PGY-1 through PGY-5 were 0.76, 0.64, 0.82, 0.87, and 0.83, respectively. The algorithm identified six 80-hour workweek violations (averaged over 4 weeks), while GPS logs identified 8. EHR-based duty hours and operational data were integrated into a dashboard to enable real time monitoring of resident workloads. EHR-based estimation of surgical resident duty hours has good correlation with GPS-based assessment of duty hours and identifies most workweek duty hour violations. This approach allows for dynamic workload monitoring and may be combined with operational data to anticipate and prevent duty hour violations, thereby optimizing learning.

Recovery, body mass and buoyancy: a detailed analysis of foraging dive cycles in the European shag

Foraging dives in birds and mammals involve complex physiological and behavioural adaptations to cope with the breaks in normal respiration. Optimal dive strategies should maximize the proportion of time spent under water actively foraging versus the time spent on the surface. Oxygen loading and carbon dioxide dumping carried out on the surface could involve recovery from the consequences of the last dive and/or preparation in anticipation of the next dive depth and duration. However, few studies have properly explored the causal pattern of effects within such dive cycles, which is crucial prior to any assessment of optimal dive strategies. Using time depth recorders and global positioning system loggers, we recorded over 42 000 dives by 39 pairs of male and female European shags, Phalacrocorax aristotelis. Dives either involved a straight descent and ascent, presumably reflecting an unsuccessful search for prey, or a descent followed by horizontal movement followed by an ascent, presumably reflecting active hunting pursuit of pelagic prey. Males were larger than females, but we were unable to distinguish between sex effects and the nonlinear effects of body mass on dive behaviour. Path analysis showed that within-individual dive-to-dive variation in surface times can best be explained as recovery from the previous dive. As expected in a pelagic hunter with unpredictable dive durations, there was no evidence of anticipatory preparation of oxygen stores in predive surface durations. Among-individual variation in dives showed that body mass directly affected descent durations, but individual variation in all other dive and surface durations was driven by variation in descent duration, suggesting a critical role for dive depth in overcoming body mass-dependent effects of hydrodynamic/wave drag and buoyancy. Our analyses test for the first time certain critical assumptions for studies assessing optimal dive strategies in birds and mammals, thereby revealing new details and avenues for research concerning adaptive diving behaviour.

MINIATURE GPS DATA LOGGERS REVEAL HABITAT AFFILIATIONS AND MOVEMENT OF VEERY (CATHARUS FUSCESCENS) DURING THEIR FIRST NON-TRANSIENT PERIOD IN SOUTH AMERICA

The ecology of Nearctic-Neotropical migrant songbirds in South America is largely unexplored. We used miniature global positioning system (GPS) data loggers to determine the broad habitat associations of nine Veeries (Catharus fuscescens) during their first non-transient period in South America. Because Veeries undertake an intratropical migration between two separate non-transient periods, the habitat used by settled birds in South America cannot not be determined from field observation or the capture of single unmarked individuals. Using satellite images, we examined coarse habitat characteristics at GPS positions from the tagged birds during their first non-transient period (December – February). We also examined habitat descriptions from existing records (e.g., published literature, museum records) of multiple birds from single sites that we consider settled individuals. All records we accepted as birds settled during their first non-transient period, including birds we tagged, were associated with stunted forest on nutrient poor soils primarily on elevated cerrado and white sand enclaves (~200 – 750 m) on the Brazilian Shield in southern Amazonia (cerrado, cerradão, savana metalófita – canga, campinarana, sartenejal). Notably, these uncommon forest communities are geographically limited and severely threatened due to anthropogenic conversion. Therefore, because of restricted habitat availability and the species’ recent population decline, we believe the Veery’s current global conservation status should be reconsidered. Following Nearctic-Neotropical migration, tagged individuals exhibited three behaviors prior to intratropical migration: (1) a prolonged stationary period at a single site, (2) shorter stationary periods with relocation events, (3) apparent continual movement. Our results have significant importance in terms of understanding the ecology and conservation needs of this declining species and demonstrate the utility of GPS loggers in tracking songbirds through dense tropical vegetation in remote and inaccessible regions of South America.

Fidelity and utility of GPS loggers as a tool for understanding community participation of older adults

Objective The purpose of this research was to examine the fidelity and utility of global positioning system (GPS) technology for studying community mobility indicators, and to explore implications for community participation for older adults. Method A longitudinal cohort design with community-dwelling older adults (n = 33) was employed. GPS spatial data and activity logs were collected at baseline and one year later. Data were analysed to evaluate the fidelity of the GPS protocol, the nature and frequency of destinations, and the stability of community mobility over time. Results Findings include evidence for high reliability of the GPS loggers to record out-of-home trips when compared to activity logs; utility of the technology for identifying frequency and type of occupational destinations; and the relative stability of two community mobility variables over time. GPS loggers generate key insights into how older adults move in and through their community as they engage in a range of occupations. Conclusion GPS technology is a robust and useful tool for gathering novel information, especially when combined with qualitative data. GPS loggers may be incorporated in evaluation or to tailor interventions. Community mobility enables social participation, and warrants further research using these methods to support processes of aging in place.

Solid Waste Collection System in Tourism Destination-The Status, Problems and Challenges

Solid waste collection system (SWCS) plays an extremely important role in solid waste management system (SWMS). Optimization on SWCS have always been a primary mission of SWM towards sustainability. Hoi An city (HAC) is a tourist attraction in the central of Vietnam with omnipresence of tourism activities especially in tourism destination (TD). As a result, optimization of SWCS towards sustainability is crucial for TD of HAC. This study aimed to offer understandings on the current SWCS. This study was conducted with GPS (Global Positioning System) logger and QGIS 3.4 to collect and analyze data of existing SWCS. Results showed that, four modes of transportation were used for solid waste collection activities (SWCA) with almost all of sources of WG in the morning. Largest figures for total number of route (29) and amount of waste collected by carts (19.6m3) were illustrated in the morning shift (MS). Illegal solid waste collection (SWC) accounted for about 10 % of total amount of waste collected by carts. Forklift trucks were the final disposal of all SWCA in TD. Current waste gathering (WG) practices indirectly damaged the tourism activities while the improper implementation of selective waste collection at source hindered the efficiency of SWCS in TD. This study made a contribution as a scientific base for optimization and administration of SWCS.

Modification and Validation of Low‐Cost Recreational GPS Loggers for Tortoises

ABSTRACTSome chelonians, such as the gopher tortoise (Gopherus polyphemus), spend much of their time underground in burrows. Often, they will retreat into these burrows when approached. This predator avoidance behavior may potentially influence the spatial use patterns that researchers directly observe via manual telemetry. In an effort to record the movements of gopher tortoises using affordable methods that reduce behavioral effects, we devised a novel application for Global Positioning System (GPS) loggers, specifically that of a low‐cost recreational unit that could be modified for extended deployment (~6 months) under gopher tortoise‐conditions. We conducted a stationary testing study of these GPS loggers whereby they were deployed at specific depths (0.5 m, 1.0 m, and 2.0 m) inside inactive burrows and on the surface at our study site in southeastern Georgia, USA, during summer 2017. This stationary trial allowed us to characterize the effect of burrow depth on GPS fix success rate (FSR), mean location error (µLE), and root mean square of the location error (LERMS). We found that µLE and LERMS increased with increasing depth underground; µLE ranged from 17.34 m on the surface to 69.98 m at the 2.0‐m depth. However, following exclusion of LE outliers >3 standard deviations from each treatment mean, LERMS decreased to 8.6 m at the surface to 40.46 m at the 2.0‐m depth. The FSR varied by burrow depth from 0.51 (2.0‐m depth) to 0.99 (surface). Additionally, we provide our attachment and deployment methods as used for studying the spatial ecology of the gopher tortoise with modified recreational GPS loggers. Overall, the modified GPS loggers performed well, obtaining 146,118 successful GPS fixes on 38 individual gopher tortoises during 84 unique deployments, each approximately 3.5–6 months in duration. © 2020 The Wildlife Society.

Tree cover density attracts rare bird of prey specialist to nest in urban forest

Urban forests are important hotspots of biodiversity in the environment of cities and their surroundings but may also stand out from neighbouring forests by greater tree species diversity, higher tree age and greater share of deciduous species. However, they are usually not connected with nesting of rare birds of prey, especially dietary specialists. Unexpectedly, I have found Honey Buzzard Pernis apivorus nesting in such a forest in a 300,000-inhabitant city in North-Eastern Poland. The male of this pair was caught and fitted with a global positioning system logger. Using a tree cover density raster, I show that both for nesting and foraging, Honey Buzzards preferred dense canopies. Even with anthropogenic disturbance, the urban forest still offered the best conditions (greatest tree cover) for this specialist species in a 10 km radius. This finding shows that the advantages of some features of urban forests (deciduous character, dense canopy) can overcome disadvantages of the urban environment even for rare, large raptors. Also tree cover density seems a promising indicator of biodiversity in urban forests and should be tested in larger studies.

The development and validation of a prototype mobility tracker for assessing the life space mobility and activity participation of older adults

BackgroundThere is increasing interest in examining the life space mobility and activity participation of older adults in the community using sensor technology. Objective data from these technologies may overcome the limitations of self-reported surveys especially in older adults with age-associated cognitive impairment. This paper describes the development and validation of a prototype hybrid mobility tracker for assessing life space mobility and out-of-home activities amongst 33 community-ambulant older adults in Singapore.MethodsA hybrid mobility tracker was developed by combining a passive Global Positioning System logger, tri-axial accelerometer and radio-frequency identification. Objective measures of life space, derived from 1 week of tracking data using Geographic Information Systems, were the maximum Euclidean distance from home (Max Euclid) and the area of the minimum convex polygon surrounding all GPS waypoints (MCP area). Out-of-home activities were quantified by visually identifying the total number of activity nodes, or places where participants spent ≥5 min, from mobility tracks. Self-reported measure of life space in 4 weeks was obtained using the University of Alabama at Birmingham Study of Life Space Assessment (UAB-LSA) questionnaire. Self-reported out-of-home activities were recorded daily in a travel diary for 1 week. Bivariate correlations were used to examine convergent validity between objective and subjective measures of life space and out-of-home activities.ResultsThe mean age of participants was 69.2 ± 7.1 years. The mean UAB-LSA total score was 79.1 ± 17.4. The median (range) Max Euclid was 2.44 km (0.26–7.50) per day, and the median (range) MCP area was 3.31 km2 (0.03–34.23) per day. The UAB-LSA total score had good correlation with Max Euclid (r = 0.51, p = 0.002), and moderate correlation with MCP area (r = 0.46, p = 0.007). The median (range) total number of activity nodes measured by tracker of 20 (8–47) per week had a good correlation with the total activity count recorded in the travel diaries of 15 (6–40) per week (r = 0.52, p = 0.002).ConclusionsThe tracking system developed to understand out-of-home travel was feasible and reliable. Comparisons with the UAB-LSA and travel diaries showed that it provided reliable and valid spatiotemporal data to assess the life space mobility and activity participation of older adults.

Coupling Tracking Technologies to Maximize Efficiency in Avian Research

ABSTRACTDirect marking and tracking of wildlife using telemetry is widespread and critical to understanding many aspects of wildlife ecology. For most species, researchers must select between multiple tracking technologies that represent trade‐offs among data requirements, mass, and cost. Options tend to be more limited for smaller, volant species. We developed and tested a unique combination of a store‐on‐board Global Positioning System logger with an independent very‐high‐frequency (VHF) tag (hereafter, hybrid tag) fitted on the greater sage‐grouse (Centrocercus urophasianus) with a modified harness design in northeastern Wyoming, USA, 2017–2018. We compared hybrid tags with other tracking technologies commonly used in avian research, namely VHF and Argos satellite relay tags. Given our research objectives, that required both frequent location data and field‐based observational data, we found the hybrid tags were the most cost‐effective option and capable of collecting more location data compared with Argos tags because of power savings associated with data transmission. Cost savings allowed us to avoid sacrificing sample size while still obtaining high‐resolution location data in addition to field‐based observational data such as the presence of chicks. We believe our hybrid tags and harness design would be beneficial to research on other avian species of comparable size to the greater sage‐grouse and those that are relatively localized year‐round, including many other Galliformes. © 2020 The Wildlife Society.

An adaptive approach to vehicle trajectory prediction using multimodel Kalman filter

AbstractWith the aim to improve road safety services in critical situations, vehicle trajectory and future location prediction are important tasks. An infinite set of possible future trajectories can exit depending on the current state of vehicle motion. In this paper, we present a multimodel‐based Extended Kalman Filter (EKF), which is able to predict a set of possible scenarios for vehicle future location. Five different EKF models are proposed in which the current state of a vehicle exists, particularly, a vehicle at intersection or on a curve path. EKF with Interacting Multiple Model framework is explored combinedly for mathematical model creation and probability calculation for that model to be selected for prediction. Three different parameters are considered to create a state vector matrix, which includes vehicle position, velocity, and distance of the vehicle from the intersection. Future location of a vehicle is then used by the software‐defined networking controller to further enhance the safety and packet delivery services by the process of flow rule installation intelligently to that specific area only. This way of flow rule installation keeps the controller away from irrelevant areas to install rules, hence, reduces the network overhead exponentially. Proposed models are created and tested in MATLAB with real‐time global positioning system logs from Jeju, South Korea.

Effect of driving style on operating speed in crest vertical curves of two-lane highways

Models that predict the operating speed (OS) in terms of highway geometry assume that drivers are homogenous individuals. However, road users build the way they drive based on their behaviour and skills, thereby configuring a so-called driving style. The state-of-the-art literature defines at least eight driving styles that affect the OS of drivers, but OS–geometry models do not consider this aspect. The purpose of this work was to study the effect of driving styles on OS in crest vertical curves (CVCs) of two-way highways. To this end, 16 drivers were selected and classified according to their driving style, based on the multidimensional driving style inventory. Subsequently, the circulation speed of each driver was tested on 40 CVCs through a 10 Hz Global Positioning System logger installed in each vehicle. New OS–vertical geometry models were developed and classified according to driving style. It was concluded that models that do not consider driving styles conceal the sensitivity of the OS to the vertical geometry of highways. Additionally, among the different driving styles there is an OS difference of up to 8 km/h. Therefore, including the driving style variable in OS models of two-way roadways is a relevant factor.

Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.