Real-time Monitoring Data Research Articles

Risk assessment of complex footbridge based on Dempster–Shafer evidence theory using Fuzzy matter–element method

Owing to the landscapes of cities and the requirements of subchannels, the construction of large-span footbridges is complex and extremely risky. These risks may lead to the collapse of a footbridge during construction, resulting in casualties and other catastrophic consequences, delays in projects and significant property losses, and potential safety hazards during follow-up maintenance. At present, China’s urban footbridge construction has not been included in the overall urban construction category, and its risk assessment is static and temporary, mainly relying on the subjective judgment of experts and construction personnel, and the different cases are difficult to learn from each other. To address this issue, a new risk assessment method and a novel system quality management framework are proposed herein. In this system, real-time engineering quality data under 4M1E (Man, Method, Material, Machine, Environment) framework is used to replace traditional risk factors. An improved Dempster–Shafer theory is adopted to effectively combine heterogeneous data from the 4M1E framework and calculate the dynamic total risk index. Finally, the proposed method is verified. The research results show that the new system can predict the risk level of each stage of bridge construction objectively and effectively by using real-time monitoring data, and has high robustness in different project testing.

A Secure Long-Range Transceiver for Monitoring and Storing IoT Data in the Cloud: Design and Performance Study.

Due to the high demand for Internet of Things (IoT) and real-time data monitoring and control applications in recent years, the long-range (LoRa) communication protocols leverage technology to provide inter-cluster communications in an effective manner. A secure LoRa system is required to monitor and store IoT data in the cloud. This paper aims to report on the design, analysis, and performance evaluation of a low-cost LoRa transceiver interface unit (433 MHz band) for the real-time monitoring and storing of IoT sensor data in the cloud. We designed and analyzed a low-cost LoRa transceiver interface unit consisting of a LoRa communication module and Wi-Fi module in the laboratory. The system was built (prototype) using radially available hardware devices from the local electronics shops at about USD 150. The transmitter can securely exchange IoT sensor data to the receiver node at about 10 km using a LoRa Wi-Fi module. The receiver node accumulates the sensor data and stores it in the cloud for processing. The performance of the proposed LoRa transceiver was evaluated by field experiments in which two transmitter nodes were deployed on the rooftop of Auckland University of Technology's Tower building on city campus (New Zealand), and the receiver node was deployed in Liston Park, which was located 10 km away from the University Tower building. The manual incident field tests examined the accuracy of the sensor data, and the system achieved a data accuracy of about 99%. The reaction time of the transmitter nodes was determined by the data accumulation of sensor nodes within 2-20 s. Results show that the system is robust and can be used to effectively link city and suburban park communities.

A Continuous Data Acquisition System for Three-Component Surface Microseismic Real-Time Monitoring

Surface microseismic monitoring is one of the most critical technologies for monitoring the dynamic subsurface reservoir processes. Due to the weak signals and poor signal-to-noise ratio (SNR), there are two major challenges to monitor the reservoir processes in an accurate and efficient way. One challenge is concerned with weak signals acquisition and how to evaluate the instrument’s abilities to record the microseismic events. The other one is how to realize real-time data monitoring during dynamic processes. Taking these challenges into consideration, a design approach to develop buried sensors consisting of three-component sensors and a low-cost, low-power consumption embedded device for microseismic monitoring data logger with the acquisition, synchronization, record, and transmission functions are presented. The circuits are divided into two printed circuit boards (PCBs). One is for a signal processing circuit, which consists of signal conditioning and analog-to-digital converter (ADC), and the other is for a digital circuit, which consists of the microcontroller and peripherals, including the global navigation satellite system (GNSS), Micro Secure Digital Memory Card (SD card), and Ethernet transceivers. The circuit for signal conditioning and ADC is a combination of a preamplifier, low-pass filter, programmable gain amplifier, and ADC. It has three modes: real-time monitoring, continuous recording, and combination. A software is designed to work in cooperation with the hardware to adjust the working parameters and modes. The self-noise of the data logger is tested in the laboratory, and the monitoring abilities of the data logger are evaluated. Finally, the surface microseismic monitoring system has been tested in the field to evaluate its performance for academic purposes.

Intelligent Campus System Design Based on Digital Twin

Amid the COVID-19 pandemic, prevention and control measures became normalized, prompting the development of campuses from digital to intelligent, eventually evolving to become wise. Current cutting-edge technologies include big data, Internet of Things, cloud computing, and artificial intelligence drive campus innovation, but there are still problems of unintuitive scenes, lagging monitoring information, untimely processing, and high operation and maintenance costs. Based on this, this study proposes the use of digital twin technology to digitally construct the physical campus scene, fully digitally represent it, accurately map the physical campus to the virtual campus with real-time sensing, and remotely control it to achieve the reverse control of the twin virtual campus to the physical campus. The research is guided by the theoretical model proposed by the digital twin technology, using UAV tilt photography and 3D modelling to collaboratively build the virtual campus scene. At the design stage, the interactive channel of the system is developed based on Unity3D to the realize real-time monitoring, decision making and prevention of dual spatial data. A design scheme of the spiral optimization system life cycle is formed. The modules of the smart campus system were evaluated using a system usability scale based on student experience. The experimental results show that the virtual-real campus system can enhance school management and teaching, providing important implications for promoting the development and application of campus intelligent systems.

Simulation and Construction of a Solar Powered Smart Irrigation System Using Internet of Things (IoT), Blynk Mobile App

Aims: To simulate and construct a solar powered smart irrigation system using Blynk Mobile App.
 Study Design: Experimental design through simulation studies and internet of things.
 Place and Duration of Study: Department of Physics, Nasarawa State University Keffi, Nigeria, between July 2021 and March 2022.
 Methodology: The system was simulated using Proteus ISIS Version 8.6. It consists of two main units that is transmitter and receiver. The transmitter consists of a sensor circuit that senses the soil moisture, humidity and temperature. Then the Node MCU microcontroller collects the data and sent to the Blynk Mobile App (Receiver). The circuit was tested at two different conditions of the soil; Wet soil and Dry soil.
 Results: Output performance analysis when the water level in soil (Moisture) was high, indicated Temperature value 28.3°C, Humidity 72%, and the Pump was OFF. When the water level in soil was mild, temperature was 60°C, Humidity 68%, and pump was turned ON. The pump is activated to switch ON as far as the water reservoir is having enough water and will switch OFF when the required level of moisture in the soil is achieved. The solar power ensured constant power supply, while the Blynk Mobile App ensured real time data monitoring by the Farmer even when he is far away from the farm.
 Conclusion: This system is an improve irrigation system that supports low water and electricity consumption as well as efficient monitoring for increase farmers’ output.

Correlation model of deflection, vehicle load, and temperature for in‐service bridge using deep learning and structural health monitoring

Deflection is an important issue in bridge structural health monitoring. An accurate deflection–vehicle load–temperature correlation model is critical to abnormal data identification, deflection prediction under extreme conditions, and bridge structural assessment. However, because of the discrete distribution in time domain of vehicle load and the extreme complexity of the deflection–vehicle load–temperature correlation, the correlation modeling method needs further studies. A novel deflection–vehicle load–temperature correlation modeling method is developed in this study. Based on the concept of deflection influence line (DIL), the raw vehicle load monitoring data are transformed into time-continuous vehicle influence coefficient (VIC). By using gated recurrent unit (GRU) neural network, a correlation model with inputs of VIC and environmental temperature data and output of deflection data is established. Taking a suspension bridge in China as an example, the prediction accuracy of short-, medium-, and long-term correlation models is tested. Moreover, based on the correlation model, a decomposition method of temperature- and vehicle-induced deflection components is proposed. The results show that the predicted deflection of the short-term correlation model is basically consistent with the real-time monitoring data, while the medium- and long-term correlation models have accurate prediction ability for the deflection extreme values in a certain time window. The temperature- and vehicle-induced deflection components separated by using the correlation model are in good agreement with the wavelet decomposition (WD) results, with clear physical meaning and independent of empirical judgment.

The Overall Design of the Comber Drive Control System

Objective: The cotton textile industry, as a competitive industry in China's international competition, is confronting new opportunities and challenges brought by the growing process of mechatronics. To further improve the traditional drive control of combing machines made in China and the automatic level of machines as a whole, some of our cotton textile enterprises have undertaken necessary technical transformations on the combing machines so as to raise the operational efficiency and production technology of domestic textile equipments. Methods: This paper focuses on the basic status and dynamic characteristics of the drive part of the domestic new comber, and analyzes the operation process of the comber and the prominent problems from the production practice. Results: The technically improved drive control system uses an industrial control computer (IPC) as the core of the system, which effectively improves the overall working efficiency of the comber, and improves the production accuracy and production efficiency. Conclusion: The combers that are textile machinery equipments with comprehensive application of machines, electricity, gases and instruments, play a vital role in enhancing product quality and production efficiency. Highly intelligent and integrated process control, real-time monitoring and accurate data acquisition and data analysis have become the mainstreams in the development of auto-control. Therefore, the commitment of high technology to transform the traditional production mode has also been an important research.

Feasibility of Using V2I Sensing Probe Data for Real-Time Monitoring of Multi-Class Vehicular Traffic Volumes in Unmeasured Road Locations

Portions of dynamic traffic volumes consisting of multiple vehicle classes are accurately monitored without vehicle detectors using vehicle-to-infrastructure (V2I) communication systems. This offers the feasibility of online monitoring of the total traffic volumes with multi-vehicle classes without any advanced vehicle detectors. To evaluate this prospect, this article presents a method of monitoring dynamic multi-class vehicular traffic volumes in a road location where road-side equipment (RSE) for V2I communication is in operation. The proposed method aims to estimate dynamic total traffic volume data for multiple vehicle classes using the V2I sensing probe volume (i.e. partial vehicular traffic volumes) collected through the RSE. An experimental study was conducted using real-world V2I sensing probe volume data. The results showed that traffic volumes for vehicle types I and II (i.e. cars and heavy vehicles, respectively) can be effectively monitored with average errors of 6.69% and 10.89%, respectively, when the penetration rates of the in-vehicle V2I device for the two vehicle types average 0.384 and 0.537, respectively. The performance of the method in terms of detection error is comparable to those of widely used vehicle detectors. Therefore, V2I sensing probe data for multi-vehicle classes can complement the functions of vehicle detectors because the penetration rate of in-vehicle V2I devices is currently high.

Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review

In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks.

Ocean Wind Observation Based on the Mean Square Slope Using a Self-Developed Miniature Wave Buoy.

Real-time, continuous, and long-term marine monitoring data benefits ocean research. This study developed a low-cost, multi-parameter, miniature wave buoy. High spatial and temporal resolution of sea surface parameters, including wind, waves, and current, can be obtained at low cost through the deployment of numerous buoys, thus forming an observation array. Tested in the laboratory water tank, the relative error of water surface slope measurement of the buoy was approximately 5.6% when the slope angle was less than 15°. For frequencies between 0.1 and 1.0 Hz, the measurement of slope spectrum was almost identical to that of the wave gauge. The buoy underestimated the slope spectrum between 1.0–1.56 Hz. A good relationship (r2 = 0.75) was obtained between wind speed at 10 m above sea surface (U10) and the low-pass-filtered mean square slope (LPMSS). After incorporating the wave age into the U10 inversion process, the root mean square error (RMSE) and BIAS were reduced to 1.15 m/s and 0.02 m/s, respectively. The 2D distribution of buoy-measured slope components was used to detect the wind direction, with an RMSE of 23.7°. The spectral tail slope steepened with increasing wind speed at low wind speeds (<7 m/s). A technical flow chart of the miniature wave buoy is proposed to observe the sea surface parameters. This miniature buoy will play an essential complementary role in the growing demand for sea state monitoring, especially in nearshore oceans.

Environmental Monitoring and Restoration of Ecological Cycle Index System Based on IoT Agriculture

Environmental factors have a direct impact on the development of agriculture, so it is particularly important to detect the environment of the agroecological cycle index system. Although there are some plans for environmental monitoring, most of them are environmental monitoring for a larger concept, but this study is specific to the actual object. This article takes farmland as the research object and comprehensively uses embedded technology, information monitoring technology, and network technology based on the analysis of the research status and system application of the farmland environmental monitoring system. This article studies and designs a real-time online remote monitoring system for farmland ecological environment based on embedded architecture and GPRS technology. By using sensors to obtain farmland information, it is displayed on the monitoring center and mobile client, and data information is obtained in real time. It manages and protects the farmland environment in advance. This article measures and analyzes the data from the final experiment. The experimental results show that the monitoring system can accurately collect farmland data in real time. And through the embedded server and the Internet, the data can be remotely transmitted in real time and displayed on the monitoring center and mobile client software. The results showed that PM2.5 was 30 μg/m3 at 20:00. The experimental data have the characteristics of real time and stability, which can meet the requirements of real-time network remote monitoring and transmission of data.

Artificial Ecosystem-Based Optimization with an Improved Deep Learning Model for IoT-Assisted Sustainable Waste Management

Increasing waste generation has become a key challenge around the world due to the dramatic expansion in industrialization and urbanization. This study focuses on providing effective solutions for real-time monitoring garbage collection systems via the Internet of things (IoT). It is limited to controlling the bad odor of blowout gases and the spreading of overspills by using an IoT-based solution. The inadequate and poor dumping of waste produces radiation and toxic gases in the environment, creating an adversarial effect on global warming, human health, and the greenhouse system. The IoT and deep learning (DL) confer active solutions for real-time data monitoring and classification, correspondingly. Therefore, this paper presents an artificial ecosystem-based optimization with an improved deep learning model for IoT-assisted sustainable waste management, called the AEOIDL-SWM technique. The presented AEOIDL-SWM technique exploits IoT-based camera sensors for collecting information and a microcontroller for processing the data. For waste classification, the presented AEOIDL-SWM technique applies an improved residual network (ResNet) model-based feature extractor with an AEO-based hyperparameter optimizer. Finally, the sparse autoencoder (SAE) algorithm is exploited for waste classification. To depict the enhancements of the AEOIDL-SWM system, a widespread simulation investigation is performed. The comparative analysis shows the enhanced outcomes of the AEOIDL-SWM technique over other DL models.

Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning

The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process.

Combining knowledge graph with deep adversarial network for water quality prediction.

Water quality prediction is an important research focus in smart water and can provide the support to control and reduce water pollution. However, existing water quality prediction models are mainly data-driven and only rely on various sensor data. This paper proposes a new water quality prediction modeling approach integrating data and knowledge. We develop a water quality prediction framework that combines knowledge graph and deep adversarial networks. The knowledge extraction and management compound extracts the water quality knowledge graph from different knowledge sources by using the deep adversarial joint model. The fusing parameter importance learning compound calculates the contribution of parameters in water quality prediction by taking into account both knowledge and data levels of correlation. Finally, a water quality prediction model combining weighted CNN-LSTM with adversarial learning predicts the values of total nitrogen based on real-time monitoring data. The experimental results on monitoring data from the Juhe River of China show that the proposed model can greatly improve the effect of water quality prediction.

Development and evaluation of an efficient and real-time monitoring system for the vector mosquitoes, Aedes albopictus and Culex quinquefasciatus.

BackgroundThe surveillance of vector mosquitoes is essential for prevention and control of mosquito-borne diseases. In this study, we developed an internet-based vector mosquito monitor, MS-300, and evaluated its efficiency for the capture of the important vector mosquitoes, Aedes albopictus and Culex quinquefasciatus, in laboratory and field trials.Methodology/Principal findingsThe linear sizes of adult Ae. albopictus and Cx. quinquefasciatus were measured and an infrared window was designed based on these data. A device to specifically attract these two species and automatically transmit the number of captured mosquitoes to the internet was developed. The efficiency of the device in capturing the two species was tested in laboratory, semi-field and open field trials. The efficiency results for MS-300 for catching and identifying Ae. albopictus in laboratory mosquito-net cages were 98.5% and 99.3%, and 95.8% and 98.6%, respectively, for Cx. quinquefasciatus. In a wire-gauze screened house in semi-field trials, the efficiencies of MS-300 baited with a lure in catching Ae. albopictus and Cx. quinquefasciatus were 54.2% and 51.3%, respectively, which were significantly higher than 4% and 4.2% without the lure. The real-time monitoring data revealed two daily activity peaks for Ae. albopictus (8:00–10:00 and 17:00–19:00), and one peak for Cx. quinquefasciatus (20:00–24:00). During a 98-day surveillance trial in the field, totals of 1,118 Ae. albopictus and 2,302 Cx. quinquefasciatus were captured by MS-300. There is a close correlation between the number of captured mosquitoes and the temperature in the field, and a positive correlation in the species composition of the captured samples among the mosquitoes using MS-300, BioGents Sentinel traps and human landing catches.Conclusions/SignificanceThe data support the conclusion that MS-300 can specifically and efficiently capture Ae. albopictus and Cx. quinquefasciatus, and monitor their density automatically in real-time. Therefore, MS-300 has potential for use as a surveillance tool for prevention and control of vector mosquitoes.

Integrated data-driven framework for anomaly detection and early warning in water distribution system

Data-driven anomaly detection and early warning have been extensively used in water distribution systems (WDS). Events such as pipe bursts and sensor failure cause abnormal monitoring data. Anomaly detection during real-time data monitoring and identification of various events are crucial in WDS. This study proposes a framework for anomaly detection and early warning in WDS. This framework comprises four anomaly detection modules—single-point anomaly identification, sensor sequence, inter-sensor sequence, qualitative module. A case study is conducted using the Net3 pipe network model. The results indicate that the proposed method can accurately identify pipe bursts and detect situations causing abnormal sensor data.

Digital twin real time monitoring method of turbine blade performance based on numerical simulation

Due to the limited number of sensor arrangements, the hydrodynamic performance test and detection of marine equipment cannot achieve real time monitoring and complete coverage of the flow field. The digital twin (DT) technology can solve this problem and help achieve real time monitoring and performance evaluation at sea. This paper takes the horizontal axis tidal turbine (HATT) as the research object, studies the applicability of the simulation-based DT real time monitoring method. Firstly, the feasibility of computational fluid dynamics (CFD) simulation of HATT is verified by experiments. Secondly, the simulation database is established under various working conditions. Then the DT method is used to reduce the three-dimensional numerical model to a first-order digital model and the simulation result data can be quickly loaded into the digital model for real time data monitoring. If the monitoring data is not calculated in the database, the Kriging interpolation method is used to reconstruct the database for flow field display quickly. For the data with large deviation of comparison result curves, the optimization algorithm is used along with machine learning. A real time monitoring engineering reference is provided for flow fields distribution and hydrodynamic performance assessment of blades.

Research on mechanical characteristics and energy dissipation of traditional ramming technology

The ramming technique is a traditional method used to improve the compactness of soil with impact and vibration energy. Based on the measured data of ramming force transmission and dissipation, this study reveals the mechanism of energy transfer and dissipation of single-layer ramming as well as the law of variation with ramming quality, laying thickness, and ramming times. It also establishes the ramming model and empirical formula of ramming quality, laying thickness, and ramming times, introduces the ramming layer changes associated with the constitutive relationship (elastic to elastoplastic parts) as a function of ramming times, and determines the influence depth and horizontal range based on real-time data monitoring. It was found that as the mass of the rammer increases, the range of influence of the impact stress inside the earth also increases proportionally, and this effect is more pronounced vertically. However, the dissipation rate of energy in horizontal direction is much greater than in the vertical direction. The traditional stacked ramming technique of “chong hai wo, hang yin ding” can effectively eliminate the defects of horizontal reversal upwelling impact and unevenness of the ramming layer during the ramming process. The research results provide a theoretical basis for evaluating the quality of the traditional ramming technique.

Development of a data-driven ensemble regressor and its applicability for identifying contextual and collective outliers in groundwater level time-series data

• A data-driven ensemble time-series data estimation model is developed. • Statistical variability and the exogenous factor are considered in the estimation. • The range of normal data is calculated based on the ensemble estimation result. • Contextual and collective outliers in time-series data were effectively identified. In this study, a method to estimate the normal range of groundwater level time-series data was developed to identify outliers in terms of the global, contextual, and collective sense. To evaluate the normal range of groundwater level time-series data, the statistical characteristics of the data and the patterns of the precipitation time-series data were incorporated into the LSTM (Long Short-Term Memory)-based ensemble regressor (i.e., the LER model). Based on the LER model, multiple possible trends of the groundwater level were generated, and the general rules of outlier identification methods (i.e., σ and Tukey’s fences (TF) rules) were applied to the LER ensemble estimation result to finally define the range of the normal data. For outlier identification performance validation, the actual groundwater level acquired from three groundwater monitoring stations in South Korea (i.e., the Pohang–Gibuk (PG), Namwon–Dotong (ND), and Jeju–Sangyae (JS) monitoring wells) and the corresponding precipitation data acquired from the nearest weather stations were applied to the study. As the reference method for comparative performance validation, simple applications of the σ and TF rules were used. For the monitoring data, the developed LER-based outlier identification method evaluates the range of the data that might be explained by the modelled influences of the interest (i.e., normal data range). The developed method showed an outlier identification performance of >70% in general while the performance of the σ and TF rules was mostly <50%. In particular, as the method effectively estimated the seasonal trend and the variability of the groundwater level with consideration of the precipitation patterns and statistics on the groundwater level variation, it is superior for identifying the contextual or collective outliers compared to the simple σ and TF rules. Through in-depth analysis, it can be concluded that the developed LER-based outlier identification method is effective for discriminating the abnormal data by considering the intrinsic statistical characteristics of the original data trend and the exogenous factors. In the aspect of the practical applicability, as the result can be automatically acquired based on real-time monitoring data, the developed method is expected to apply for more efficient maintenance of the monitoring devices by embedding the model as the management software into the monitoring network system.

A Revised Video Vision Transformer for Traffic Estimation With Fleet Trajectories

Real-time traffic monitoring represents a key component for transportation management. The increasing penetration rate of connected vehicles with positioning devices encourages the utilization of trajectory data for real-time traffic monitoring. The use of commercial fleet trajectory data could be seen as the first step towards mobile sensing networks. The main objective of this research is to estimate space occupancy of a single road segment with partially observed trajectories (commercial fleet trajectories in our case). We first formulate the trajectory-based traffic estimation as a video computing problem. Then, we reconstruct trajectory series into video-like data by performing spatial discretization. Following this, video input is embedded using a tubelet embedding strategy. Finally, a Revised Video Vision Transformer (RViViT) is proposed to estimate traffic state from video embeddings. The proposed RViViT is tested on a public dataset of naturalistic vehicle trajectories collected from German highways around Cologne during 2017 and 2018. The results witness the effectiveness of the proposed method in traffic estimation with partially observed trajectories.