Global Positioning System Data Research Articles

Quantifying time spent outdoors: A versatile method using any type of global positioning system (GPS) and accelerometer devices.

Spending time outdoors is associated with increased time spent in physical activity, lower chronic disease risk, and wellbeing. Many studies rely on self-reported measures, which are prone to recall bias. Other methods rely on features and functions only available in some GPS devices. Thus, a reliable and versatile method to objectively quantify time spent outdoors is needed. This study sought to develop a versatile method to classify indoor and outdoor (I/O) GPS data that can be widely applied using most types of GPS and accelerometer devices. To develop and test the method, five university students wore an accelerometer (ActiGraph wGT3X-BT) and a GPS device (Canmore GT-730FL-S) on an elastic belt at the right hip for two hours in June 2022 and logged their activity mode, setting, and start time via activity diaries. GPS trackers were set to collect data every 5 seconds. A rule-based point cluster-based method was developed to identify indoor, outdoor, and in-vehicle time. Point clusters were detected using an application called GPSAS_Destinations and classification were done in R using accelerometer lux, building footprint, and park location data. Classification results were compared with the submitted activity diaries for validation. A total of 7,006 points for all participants were used for I/O classification analyses. The overall I/O GPS classification accuracy rate was 89.58% (Kappa = 0.78), indicating good classification accuracy. This method provides reliable I/O clarification results and can be widely applied using most types of GPS and accelerometer devices.

ANTHROPOMETRIC AND PHYSICAL QUALITIES OF SEMI-PROFESSIONAL RUGBY LEAGUE PLAYERS

AIM The aim of the current study was to investigate the physiological and anthropometric characteristics of semi-professional rugby league players across a typical season, including pre- and post-season data. METHODS Thirty-five semi-professional rugby league players, mean age 25.38 ± 2.35 years were measured for height, body mass, sum of skinfolds, and speed (20m sprint). Global positioning system (GPS) data, sampling at 10 Hz, were collected, two pre-season and eighteen Queensland Cup rugby league matches in the 2023 season for 10 players (backs and forwards). Data was categorized into total distance (km), sprint distance (m), power plays, energy expenditure (kcal), player load, and top speed (m/s). RESULTS Compared with forwards the backs had significantly lower body mass (99.4kg vs 90.8kg), and significantly greater speed during 20m sprint (3.00 vs 3.1sec). Sum of skinfolds, no differences between backs and forwards across the three time points, 104.2 vs 106.2mm, 103.9 vs 110.9mm and 93.8 vs 108.1mm. Reviewing the GPS data across the trial and cup games revealed a number of significant differences between backs and forwards, backs greater overall distance (6.63km vs 5.26km), sprint distance (467.53 vs 264.63m), power plays (37 vs 25), energy expenditure (1002.42 vs 778.90 kcal), player load (311.50 vs 275.16), top speed (8.02 vs 7.02m/s). CONCLUSION Retrospective analysis of our data has allowed us to understand the activity demands between match types and differences in activity demands between seasonal phases of training in semi-professional male rugby league players and will permit further specificity of training and improve performance. Determining the fitness and physiological profiles and how they relate to training and match demands will allow the coaching staff to develop more targeted training strategies in order to optimally develop the fitness characteristics that predominantly underpin match demands.

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.

ATM CRIME PREVENTION USING WIRELESS SENSORS

Security and safety are a necessity for automated teller machines (ATM). The ATM security system is implemented using the Internet of things (IoT) and GPS (global positioning system). The main idea of this project is to develop an ATM surveillance and security system. In this project, when any physical attack against the ATM takes place, then information about the attack is sent using IoT and also alerts the surrounding area using a buzzer, at the same time the entire data from the sensors is sent to the developed mobile application and puts alert message to the bank officials. The officials who have control over the mobile application can control the Door through their mobile to lock from their location remotely. To prevent the escape of the thief chloroform connected to the controller through relay can also be sprayed inside the ATM by the officials remotely from their place using the mobile app. The Camera (ESP32) is used for live video coverage and to monitor the activity inside the ATM. The Camera will not only record the activity but also, transmit will live video taken inside the ATM and the ATM location as latitude and longitude are tracked using GPS. The system is connected to the Blynk mobile application. The sensor and GPS data are read by the microcontroller and these data are sent to the Blynk application. With the help of the Blynk application, the official who has access to it can control the relays and the respective devices connected to the relay to turn it ON or OFF.

Programmable Meta‐Reflector for Multiple Tasks in Intelligent Connected Environments

AbstractIn the face of the challenges posed by the advent of 5G, Internet of Things (IoT) and requirements of future communication generations, the urgent need for cutting‐edge technologies capable of adapting their operation to a variety of dynamic tasks for efficient signal propagation management is clear. Connected intelligent metasurfaces, distinguished by their ability to dynamically and autonomously adapt their responses, and able to interact with an external uncontrollable and fast‐changing communication environment to benefit from collective intelligence so as to determine the required action, are emerging as an innovative solution in this field. Herein, an intelligent connected system based on a programmable metasurface reflector, is presented and assessed in two scenarios simulating dynamic uplink and downlink communications. For the uplink configuration, the metasurface is exploited for the estimation of the direction of arrival (DOA) of an incoming wave, while for the downlink scenario, it enables the automated tracking of mobile targets using global positioning system (GPS) data updated from a real‐time database. Experimental results highlight the responsiveness of the metasurface and demonstrate efficiency in wave focusing and mobile target tracking.

Integrated Investigation on Heterogeneous Lower Crust Rheology in Kyushu and Afterslip Behavior Following the 2016 Mw7.1 Kumamoto Earthquake

AbstractThe viscoelastic lower crust beneath Kyushu Island, influenced by the volcanic arc, interplays with active crustal faults in this region and helps to shape local tectonics. In this study, we employed a three‐dimensional viscoelastic finite element model to gain insights into the lithospheric rheology and crustal faulting kinematics, through modeling the postseismic deformation processes of the 2016 Mw 7.1 Kumamoto earthquake. Our model reveals a viscosity of 2 × 1020 Pa s for the lower crust and 2 × 1019 Pa s for the upper mantle. A reduced lower crust viscosity of 2 × 1019 Pa s in the volcanic arc area is required for better reproducing the Global Positioning System data. The stress‐driven afterslip decays rapidly over time and is up to 0.3 m within 5 years after the earthquake. We propose additional normal‐component afterslip to better explain the complex postseismic deformation in the near field, which may be due to the interaction between the fault and volcano Aso.

Variation in Total Electron Content Over Ethiopia During the Solar Eclipse Events

AbstractThis work studies variations of ionospheric total electron content (TEC) during four distinct solar eclipse events over the Ethiopia region. Dual‐frequency global positioning system (GPS) data obtained from UNAVCO over Addis Ababa (9.036°N, 38.76°E) and Bahir Dar (11.6°N, 37.34°E) stations are used to examine the ionospheric variability during two annular solar eclipses on 15 January 2010 and 1 September 2016, a partial solar eclipse on 4 January 2011, and a hybrid solar eclipse (the eclipse path starts out as annular but later changes to total) on 3 November 2013. The results show a significant decrease in TEC values during the occurrence of the solar eclipses. Specifically, the TEC values are reduced to −20% and −10% during the annular eclipse on 15 January 2010, −33% and −38% during the partial solar eclipse on 4 January 2011, −26% and −24% during the annular solar eclipse on 1 September 2016, over the Addis Ababa and Bahir Dar stations, respectively. There is only minimal change in TEC of −8% and −9% at Addis Ababa and Bahir stations, respectively, during the 3 November 2013 solar eclipse even if the obstruction rate is high over the study area. Furthermore, the study shows that the spatial gradient of TEC reduction varies at different locations, which is attributed to the distinct amount of reduction in solar radiation reaching the Earth's surface, resulting in reduced photo‐ionization. Overall, this study provides insightful information about the behavior of the ionospheric TEC during solar eclipses over Ethiopia and emphasizes the use of dual‐frequency GPS data in tracking the variations of the TEC.

Impact of incorporating Spire CubeSat GPS observations in a global GPS network solution

Commonly, Global Positioning System (GPS)-based precise orbit determination (POD) of a low Earth orbiting (LEO) satellite is conducted by introducing fixed GPS orbits and clock corrections that were derived in a previous independent network solution using ground-based GPS receivers only. There have been a number of studies showing that the integration of LEO GPS observations can be advantageous in global network solutions, particularly when it comes to estimating geodetic parameters, such as the Earth’s center-of-mass. There has been an increase in GPS data available from LEO CubeSats over the past few years, such as from the Spire satellites, which are equipped with dual-frequency GPS receivers. The goal of the present study is to determine how GPS observations collected by specific Spire satellites affect global network solutions when combined with GPS observations from ground stations of the International GNSS Service. To obtain a combined GPS-LEO solution, GPS observations collected from receivers of selected Spire satellites and additional scientific LEOs are used to obtain combined GPS-LEO solutions by performing one joint least-squares adjustment process. Using this method, LEO satellite orbit parameters are determined along with GPS orbit parameters and geodetic parameters, namely GNSS station coordinates, Earth rotation parameters (ERPs), and the Earth’s center-of-mass. The influence of the Spire LEOs is assessed by analyzing different solutions that use GPS observations from different Spire and LEO satellites. Besides comparing the different global network solutions with one another, a comparison to a solution using only terrestrial GPS data is performed as well. As part of the quality assessment of the GPS-LEO solution, quality characteristics are analyzed, which are the geodetic parameters, orbit overlaps at arc boundaries, and formal errors determined for the estimated parameters. The results show that the estimation of the Earth’s center-of-mass coordinates benefits from including GPS-LEO observations. The solution is more improved by integrating GPS-LEO observations from scientific LEOs than from GPS-Spire-included solutions. The analysis reveals that GPS orbits are improved using the LEO-integrated approach, while orbit misclosures of LEO and Spire trajectories currently indicate a decrease in LEO orbit precision due to LEO orbit modeling deficiencies.

QWLCPM: A Method for QoS-Aware Forwarding and Caching Using Simple Weighted Linear Combination and Proximity for Named Data Vehicular Sensor Network

The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM’s efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM’s proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM’s substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions.

Intersection movements delay modelling based on crowd-sensed global positioning system trajectory data

Developing accurate large-scale transportation models, used to guide policy adoption and evaluate infrastructure alternatives or changes in sociodemographic conditions, is data and resource intensive. This research proposes a novel method for modeling intersection movement delay using crowd-sensed Global Positioning System (GPS) data. This is achieved by providing a general definition of turning movements and extracting travel times thought GPS trajectory data analysis. Additionally, a straightforward method is proposed to integrate the observed delays per movement type into volume-delay functions. The spatial definition provided for turning movements captured distinct speed profiles per turn type. The significant differences in mean speeds for different turn types highlights the importance of integrating turn penalty functions based on real observations and underscore the importance of crowd-sensed GPS data. A simple technique is also proposed to integrate the proposed method into the volume-delay functions used in large scale transport models.

7-day patterns in Black-White segregation in 49 metropolitan areas

While residential segregation is a persistent attribute of metropolitan areas, recent studies find segregation levels fluctuate throughout the day, reaching their lowest levels during daytime hours. This paper shows hourly variations in Black-White segregation from Monday through Sunday for the top 49 most populated metropolitan areas with Global Positioning System (GPS) data collected from mobile phones from October 2018. I find that segregation levels are higher on average over weekends compared to that of weekdays. I use models to identify the characteristics of neighborhoods with higher levels of segregation on weekends, which include all demographic variables and nearly a third of 35 sectors of businesses and organizations, such as retail, personal care, and religious organizations. I also find more than a third of the sectors are associated with higher levels of segregation during business hours on weekdays, including academic institutions, health care, manufacturing, and financial institutions. Findings from this paper display the significance in the distinction between weekdays and weekends with where people spend their time and how this relates to racial segregation. Specifically, Black people, on average, stay in their home census tracts and visit non-White neighborhoods for organizational resources more so than White people. Significant patterns of associations between racial segregation and the majority of businesses demonstrate the salience of race for more industries than previously understood.

Understanding the route choice preferences of private and dock-based public bike users using GPS data in Seoul, South Korea

Analyzing route preferences is crucial for comprehending how individuals choose their travel paths, assessing road facilities, and anticipating upcoming traffic conditions. This study aimed to explore the factors influencing route selections made by private and public bike users and compare their decisions during peak and off-peak hours. Global positioning system (GPS) traces of 12,106 private bike users and 74,397 public bike users from a single weekday in Seoul, South Korea, were used. A map-matching process aligned the GPS data with a comprehensive cycling network encompassing bikeways, sidewalks, neighborhood roads, and their street attributes such as urban, park, and river streets. Choice sets composed of the shortest and 10th alternative routes were created to compare various road features with actual routes. By employing a path-size logit model to address overlapping issues among the routes, the results showed that buffered bike lanes separated from car flows positively influenced the route selection of bicyclists. Bike boulevards and shared sidewalk bikeways negatively impacted route decisions made by public bike users, with a more pronounced effect during peak hours. Furthermore, private bike users showed a decreased preference for routes in residential areas during peak hours, while an increased preference during off-peak hours. Moreover, public bike users preferred selecting routes in the presence of shopping-stores or amenities as well as public transit stations, with these effects being more pronounced during peak hours. These findings are expected to contribute to capturing valuable insights into the route preferences of bicyclists for the improvement of bicycle infrastructures.

Estimating the slip rate in the North Tabriz Fault using focal mechanism data and GPS velocity field

Abstract This study delves into slip distribution on the North Tabriz Fault (NTF), a critical aspect of seismic hazard analysis due to its proximity to the Tabriz metropolis. The study operates within a uniform elastic half-space, maintaining constant values for fault geometry and regional rheological parameters throughout the research. To calculate strain boundary conditions, permanent and periodic global positioning system (GPS) data from the northwest region were utilized. The fault was constrained perpendicularly while allowing tangential movement, facilitating the determination of its annual slip rate using the boundary element method, with the Okada analytical model serving as the fundamental solution. The findings underscore the intricate relationship between the fault’s slip rate and boundary conditions, revealing a predominant right-lateral strike-slip motion. The study offers two slip rate assessments, obtained through earthquake focal mechanisms and GPS velocity data, yielding values of 5 and 5.5 mm / year {\rm{mm}}/{\rm{year}} , respectively. Importantly, the alignment of these calculated slip rates with paleo-seismological data underscores the credibility of the results generated via the boundary element method, distinguishing it as a reliable approach when compared to other numerical and analytical techniques. This research provides valuable insights into the behavior and slip dynamics of the NTF, which is pivotal for assessing seismic risks.

Enhanced Deep Neural Networks for Traffic Speed Forecasting Regarding Sustainable Traffic Management Using Probe Data from Registered Transport Vehicles on Multilane Roads

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts.

Wireless vision-based digital media fixed-point DSP processor depending robots for natural calamities

Abstract Natural calamities are affecting many parts of the world. Natural disasters, terrorist attacks, earthquakes, wildfires, floods and all unpredicted phenomena. Disasters cause emergency conditions, so imperative to coordinate the prompt delivery of essential services to the sufferers. Often, disasters lead many people to perish by becoming trapped inside, but many more also perish as a result of individuals receiving rescue either too late or not at all. The implementation and design of a Receiver module utilizing Davinci code processor DVM6437, Wireless camera receiver, Zigbee Transceiver and Global Positioning System (GPS) is proposed in this manuscript for Wireless Vision-based Semi-Autonomous rescue robots that are employed in rough terrain. The receiver side’s Zigbee transceiver module eliminates the limitations of tele-operating rescue robots by enabling the control station to receive GPS data signals and aids in robot management by sending control signals wirelessly. Half and full-duplex communication are supported by the Davinci processor DVM6437, a digital media fixed-point DSP processor that relies on Very Long Instruction Words. It includes an extensive instruction set that is ideal for real-time salvage operations. DVM processor is coded utilizing MATLAB Simulink. MATLAB codes and Simulink blocks are employed under Embedded IDE link.

Examining the relationship between active transport and exposure to streetscape diversity during travel: A study using GPS data and street view imagery

Active transport (AT)—physical activity (PA) during travel—can promote human health. Among built environment factors related to travel research, design refers to the street network features encouraging AT. The advent of street view images (SVIs) presents the potential to measure design during travel by capturing the eye-level built environments. Benefited by SVIs, this study innovatively introduces streetscape diversity—the interconnection of street view-derived built environment factors—during travel as the proxy to measure design from the street view perspective. Exposures to higher streetscape diversity could provide increased access to potential destinations and therapeutic landscapes, thereby promoting AT. Through integrating SVIs and young adults’ Global Positioning System (GPS) trajectories, this study utilized negative binomial regression models to assess the relationship between streetscape diversity and time spent in AT. Results indicate that the inclusion of streetscape diversity improves the model performance, and there is a positive relationship between streetscape diversity and AT. This finding indicates that increased access to travel routes with diverse streetscapes could increase the probability of participating in AT. Furthermore, integrating individual GPS data and SVIs allows more precise space-time measurement of individual environmental exposures.

Research and application of the global positioning system (GPS) clustering algorithm based on multilevel functions

With the rapid development and widespread adoption of wearable technology, a new type of lifelog data is being collected and used in numerous studies. We refer to these data as informative lifelog which usually contain GPS, images, videos, text, etc. GPS trajectory data in lifelogs is typically categorized into continuous and discrete trajectories. Finding a point of interest (POI) from discrete trajectories is a challenging task to do and has caught little attention so far. This paper suggests an LP-DBSCAN model for mining personal trajectories from discrete GPS trajectory data. It makes use of the hierarchical structure information implied in GPS trajectory data and it is suggested a variable-levels, variable-parameters clustering method (LP-DBSCAN) based on the DBSCAN algorithm to increase the precision of finding POI information. Finally, the Liu lifelog dataset is subjected to a systematic evaluation. In terms of GPS data that are not evenly distributed geographically, the experimental results demonstrated that the proposed algorithm could more accurately identify POI information and address the adverse effects caused by the global parameters of the traditional DBSCAN algorithm.

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Improved Ionospheric Total Electron Content Maps over China Using Spatial Gridding Approach

Comparing malaria risk exposure in rural Cambodia population using GPS tracking and questionnaires

BackgroundThe Great Mekong Subregion has attained a major decline in malaria cases and fatalities over the last years, but residual transmission hotspots remain, supposedly fueled by forest workers and migrant populations. This study aimed to: (i) characterize the fine-scale mobility of forest-goers and understand links between their daily movement patterns and malaria transmission, using parasites detection via real time polymerase chain reaction (RT PCR) and the individual exposure to Anopheles bites by quantification of anti-Anopheles saliva antibodies via enzyme-linked immunosorbent assay; (ii) assess the concordance of questionnaires and Global Positioning System (GPS) data loggers for measuring mobility.MethodsTwo 28 day follow-ups during dry and rainy seasons, including a GPS tracking, questionnaires and health examinations, were performed on male forest goers representing the population at highest risk of infection. Their time spent in different land use categories and demographic data were analyzed in order to understand the risk factors driving malaria in the study area.ResultsMalaria risk varied with village forest cover and at a resolution of only a few kilometers: participants from villages outside the forest had the highest malaria prevalence compared to participants from forest fringe’s villages. The time spent in a specific environment did not modulate the risk of malaria, in particular the time spent in forest was not associated with a higher probability to detect malaria among forest-goers. The levels of antibody response to Anopheles salivary peptide among participants were significantly higher during the rainy season, in accordance with Anopheles mosquito density variation, but was not affected by sociodemographic and mobility factors. The agreement between GPS and self-reported data was only 61.9% in reporting each kind of visited environment.ConclusionsIn a context of residual malaria transmission which was mainly depicted by P. vivax asymptomatic infections, the implementation of questionnaires, GPS data-loggers and quantification of anti-saliva Anopheles antibodies on the high-risk group were not powerful enough to detect malaria risk factors associated with different mobility behaviours or time spent in various environments. The joint implementation of GPS trackers and questionnaires allowed to highlight the limitations of both methodologies and the benefits of using them together. New detection and follow-up strategies are still called for.

SEEK+: Securing vehicle GPS via a sequential dashcam-based vehicle localization framework

Nowadays, the Global Positioning System (GPS) plays an critical role in providing navigational services for transportation and a variety of other location-dependent applications. However, the emergent threat of GPS spoofing attacks compromises the safety and reliability of these systems. In response, this study introduces a cutting-edge computer vision-based methodology, the SEquential dashcam-based vEhicle localization frameworK Plus (SEEK+), designed to counteract GPS spoofing. By analyzing dashcam footage to ascertain a vehicle’s actual location, SEEK+ scrutinizes the authenticity of reported GPS data, effectively identifying spoofing incidents. The application of dashcam imagery for localization, however, presents inherent obstacles, such as adverse lighting and weather conditions, seasonal and temporal image variations, obstructions within the camera’s field of view, and fluctuating vehicle velocities. To overcome these issues, SEEK+ integrates innovative strategies within its framework, demonstrating superior efficacy over existing approaches with a notable detection accuracy rate of up to 94%.