Low Penetration Rate Research Articles

Development status and application prospect of semi-active vibration reduction technology for down-hole drilling tools

Domestic and foreign oil companies have been constantly increasing the research and development investment in underground shock absorber, to reduce the vibration and shock of drilling tool under hard rock drilling environments, enhance the rate of penetration and decrease drilling cost under the premise of ensuring the normal life of drill string components and stable bit weight. Based on this, this paper has focused on the working principal of magneto-rheological fluid (MRF) semi-active damper, the composition of MRF and its internal parameters’ impact on the damping characteristics, combining with the status quo at home and abroad of down-hole vibration technology. Meanwhile, the test analysis of semi-active damping technology was conducted. And the results showed that semi-active vibration technology had greatly weakened the vibration level while improved the rate of penetration, guaranteed the service life of bit and other drill-string components, reduced additional tripping times and effectively solved the problems of bit weight instability and low penetration rate etc., caused by drill vibration. Therefore, in the current situation of worldwide oil price downturn, it has become increasingly prominent to reduce cost and increase efficiency, and it’s particularly necessary to research on the shock absorbers of down-hole drilling tools in China.

An Empirical Study on Consumer Behavior of Life Insurance Purchasing Decision

The Indian economy is one of the fastest growing economies in the world with GDP per capita growing at a rate of 7.1% per annum1. The country is also experiencing a demographic shift towards a younger population with about 35% of the population being between 15 and 34 years of age2 in 2017. In the next few decades, unprecedented numbers of young people are expected to enter the workforce, earn and save part of their earnings. India’s household financial savings were estimated to be about 8.1% of the Gross National Disposable Income (GNDI), or about $26 trillion in the financial year 2016-17. About a fourth of these savings are invested in insurance3. Thus, the insurance sector is large and will grow further in the coming years. Understanding consumer behavior and what influences purchase decisions is important for different players in this industry including regulators and insurance companies. Despite recent growth, the life insurance market in India has low penetration rates compared to many other countries. Financial inclusion is one of the primary concerns of policy makers across the world. Now the author has described several aspects about consumer behavior about Life Insurance at the event on purchasing.

Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study

<div class="section abstract"><div class="htmlview paragraph">Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.</div></div>

Right-of-way reallocation for mixed flow of autonomous vehicles and human driven vehicles

Autonomous Vehicles (AVs) are bringing challenges and opportunities to urban traffic systems. One of the crucial challenges for traffic managers and local authorities is to understand the nonlinear change in road capacity with increasing AV penetration rate, and to efficiently reallocate the Right-of-Way (RoW) for the mixed flow of AVs and Human Driven Vehicles (HDVs). Most of the existing research suggests that road capacity will significantly increase at high AV penetration rates or an all-AV scenario, when AVs are able to drive with smaller headways to the leading vehicle. However, this increase in road capacity might not be significant at a lower AV penetration rate due to the heterogeneity between AVs and HDVs. In order to investigate the impacts of mixed flow conditions (AVs and HDVs), this paper firstly proposes a theoretical model to demonstrate that road capacity can be increased with proper RoW reallocation. Secondly, four different RoW reallocation strategies are compared using a SUMO simulation to cross-validate the results in a numerical analysis. A range of scenarios with different AV penetration rates and traffic demands are used. The results show that road capacity on a two-lane road can be significantly improved with appropriate RoW reallocation strategies at low or medium AV penetration rates, compared with the do-nothing RoW strategy.

Optimizing Drill Bits Performance in Highly Unconfined Compressive Strength Formation, Sinai Oil Field, Egypt

In Belayim Oil field, Sidri concession, 8 1/2" section is composed of conglomerates with overall Unconfined Compressive Strength (UCS) varies from 25,000 to 40,000 psi. This section was mainly drilled with Polycrystalline Diamond Compact (PDC) bits and Tungsten Carbide Insert (TCI) drill bits. Drilling 1000 meter in 8 1/2" section needed minimum 6 to 8 drill bits. The average rate of penetration (ROP) was 2.8 (meter per hour) MPH and the average drilled interval was 135 m. The rock bit that had been used in offset wells achieved low ROP and short drilled intervals as well, meanwhile, PDC bit achieved moderate average ROP and relatively short run intervals The operator target was to reduce the drilling time by raising ROP and the drilled interval per drill bit; the hybrid bit was presented to achieve that target. The hybrid bit achieved the highest ROP record and the longest drilled interval in Sidri concession achieving 200 percent improvement from offsets’ average performance. Finally, the paper recommended using three hybrid bit in the first three runs and in the fourth run, where the unconfined compressive strength reached its maximum values, TCI drill bit could be used.

The influence of microwave treatment on the compressive and tensile strength of igneous rocks

Cutting hard rocks has some problems such as low penetration rate and high wear rate of cutting tools. These problems can be solved by the microwave treatment of hard rocks during cutting. In this study, the effects of microwave treatment were investigated on the strength of nine different igneous rocks. First, the uniaxial compressive strength (UCS) and Brazilian tensile strength (BTS) tests were carried out on the untreated samples. Then, the strength tests were performed on the microwave treated specimens. The results show that the rock strength decreases generally with increasing microwave power and treatment duration. The strength losses and the heating degrees are different for each tested rock type irradiated by microwave power due to the different the mineral content. When treating the specimens with microwave energy, crackings and/or meltings occur depending on the applied power, the exposure duration, and the mineral content. Even a small amount of metallic mineral can increase too much the temperature of a specimen. The UCS and the BTS values of granites decrease generally with increasing surface temperature after 200 °C and 100 °C, respectively. The significant losses in the UCS and the BTS values of syenite and gabbro specimens are generally observed below 100 °C due to their metallic mineral contents or high amount of microwave absorber minerals. It can be concluded that microwave irradiation has important influences on the strength of igneous rocks depending on the applied energy, the exposure time, and mineralogy.

Real time adaptive cruise control strategy for motorways

An ACC (Adaptive Cruise Control)-based traffic control strategy is presented, which improves the motorway traffic flow efficiency by changing in real time the driving behaviour (specifically the employed time-gap and the acceleration strength) of ACC-equipped vehicles in motorway sections according to the corresponding traffic conditions. The control strategy comprises three distinct actions: (i) gradual decrease of ACC time-gaps at near-capacity traffic in order to increase capacity; (ii) minimum time-gaps and (iii) acceleration increase, both at the very vicinity of active bottlenecks in order to increase the discharge flow. The behaviour and impact of the control strategy, and of each of its parts separately, are demonstrated for different ACC penetration rates via microscopic simulation applied to a real motorway stretch. The simulation results show that, even for low penetration rates of ACC-vehicles, the proposed strategy leads to sensible improvements regarding the average vehicle delay and fuel consumption by delaying the onset of congestion (thanks to increased capacity); and by speeding up its dissolution (thanks to the mitigation of capacity drop).

Fuzing license plate recognition data and vehicle trajectory data for lane-based queue length estimation at signalized intersections

Queue length is one of the most important performance measures for signalized intersections. With recent advancements in connected vehicles and intelligent mobility technologies, utilizing vehicle trajectory data to estimate queue length has received considerable attentions. However, most of the existing methods are based on some assumptions, such as known arrival patterns and/or high penetration rates. Besides, existing models would probably be unstable or invalid under sparse trajectory environment. Hence, license plate recognition (LPR) data is introduced in this study to fuze with the vehicle trajectory data, and then, a lane-based queue length estimation method is proposed. First, by matching the LPR data with probe vehicle data, the two-dimensional probability density distribution of discharge headway and stop-line crossing time of various kinds of vehicles, i.e., queued and nonqueued vehicle for undersaturated condition and twice-queued and once-queued vehicle for oversaturated condition, can be calibrated. Then, the Bayesian theory is adopted to derive the lane-based queue length for undersaturated condition as well as the initial queue for oversaturated condition with the largest possibility, respectively. Where probe vehicle trajectories, if existed, will provide the boundaries for the estimated queue lengths. Finally, the performance of the proposed method is evaluated using both simulation and empirical data. Simulation results show that the proposed method could produce accurate estimates of queue lengths for both undersaturated and oversaturated conditions and can achieve reliable estimates even under low penetration rate (3%). Empirical results show that the proposed method outperforms an existing method using probe vehicle trajectories only.

Modeling the effects of drillstring eccentricity, pipe rotation and annular blockage on cuttings transport in deviated wells

Transportation of drilled cuttings from the bit to the surface is one of the primary functions of a drilling fluid. This transport is facilitated primarily by pump rate and the rheology and density of the drilling fluid, which all contribute to cuttings lift. There are, however, other important factors that have a significant effect on cuttings transport, as shown by a large amount of academic and industry research. These include rotation and the eccentricity of the drillstring. These two parameters are interrelated when it comes to hole cleaning: the effect of pipe rotation on cuttings evacuation changes with varying eccentricity. This adds a level of complexity to the hole cleaning challenge, both from a modeling perspective as well as the practical management of cuttings evacuation from wells in the field.A novel cuttings transport model that includes essential effects such as pipe rotation, eccentricity and annulus blockage is presented in this study. Velocity profiles for the well annulus subdivided into small grids are calculated for any given fluid, which can be a Newtonian or a non-Newtonian fluid such as described by the Bingham Plastic, Power Law or Yield Power Law rheological models. These local velocities are compared against the concept of the critical velocity for cuttings transport. Then, the locations of the settled cuttings and the magnitude of annular blockage are calculated numerically. Continuity and momentum equations are solved for the blocked annulus to estimate the new local velocities. By doing so, a realistic representation of a wellbore's pressure and velocity profiles is obtained. The work aims to make hole cleaning modeling more comprehensive and representative in order to prevent hole cleaning-related non-productive time (NPT) or invisible lost time (ILT) events in the field. These include stuck pipe and lost circulation events, low rates of penetration, high torque and drag, failure to land the casing at the desired depth, and poor cement jobs leading in turn to poor zonal isolation.

Improving Coverage Rate for Urban Link Travel Time Prediction Using Probe Data in the Low Penetration Rate Environment.

Short-term travel time prediction is an important consideration in modern traffic control and management systems. As probe data technology has developed, research interest has moved from highways to urban roads. Most research has only focused on improving the prediction accuracy on urban roads because it is the key index of evaluating a model. However, the low penetration rate of probe vehicles at urban networks may result in the low coverage rate which restricts prediction models from practical applications. This research proposed a non-parametric model based on Bayes’ theorem and a resampling process to predict short-term urban link travel time, which can enhance the coverage rate while maintaining the prediction accuracy. The proposed model used data from vehicles in both the target link and its crossing direction, so its coverage rate can be expanded, especially when the data penetration rate is low. In addition, the utilization of relationships between vehicles in both directions can reflect the influence of signal timing. The proposed model was evaluated in a computer simulation to test its robustness and reliability under different data penetration rates. The results implied that the proposed model has a high coverage rate, demonstrating stable and acceptable performance at different penetration rates.

Spatial peculiarities of social networking services distribution in Rivnenska oblast

Social networking services are a modern phenomenon in the field of human relations. As differentiated from western science, the issue of SNS spatial distribution is not a matter of high concern in Ukrainian researchers because of a number of objective and subjective reasons. The main task of the research is to analyze the spatial distribution of the amount of users and the penetration level of social networking services such as Facebook and Instagram in administrative districts and cities of region subordination belonging to Rivnenska oblast. Facebook is the most popular social networking service in Rivnenska oblast. There is a trend towards concentration of users' accounts in big cities: Rivne City, Dubno City, Varash City. More than 70% of users of the oblast live in the cities of oblast subordination and 58.4% of accounts are concentrated in Rivne City which is also the regional center. T. Hägerstrand's Spatial Innovation Diffusion Theory has been proved – the largest cities of the oblast are regional innovation centers. Densely populated Sarnenskyi, Bereznivskyi, and Kostopilskyi districts are distinguished among the districts. Facebook is the least used in Dubenskyi, Rivnenskуі, Ostrozkyi, Zarichnenskyi, Demydivskyi, Hoshchanskyi districts. The penetration rate of the social networking service Facebook in the oblast is 32.7% (in Ukraine – 31.0%). The highest level is in cities of oblast subordination. Rivne City and Dubno City are particularly distinguished. High levels of penetration are achieved due to the high development of technical infrastructure, through the companies' and shops' accounts, as well as accounts of residents from surrounding villages. The relatively low penetration rate in Varash City, the second largest city of Rivne oblast due to population, can be explained by the weaker connections with the surrounding area. In administrative districts, the penetration rate is much lower. A relatively high percentage of users are in Radyvylivskyi, Bereznivskyi, Kostopilskyi districts. These districts are characterized by a high level of socio-economic development and convenient socio-geographical location. The lowest level of penetration of the social networking service is in Demydivskyi, Zarichnenskyi, Hoshchanskyi districts. These are little developed and depressed territories within the oblast. The second most popular social networking service in Rivnenska oblast is Instagram (26.3% penetration rate). In terms of development, it is inferior to Facebook, and its main users are mostly young people. In the spatial distribution of Instagram users, we can trace the same trends as the Facebook. In general, the distribution of social networking services such as Facebook and Instagram in Rivnenska oblast corresponds to a hierarchical model of spatial diffusion. There is a trend towards concentration of users in big cities.

VeMo: Enabling Transparent Vehicular Mobility Modeling at Individual Levels with Full Penetration

Understanding and predicting real-time vehicle mobility patterns on highways are essential to address traffic congestion and respond to the emergency. However, almost all existing works (e.g., based on cellphones, onboard devices, or traffic cameras) suffer from high costs, low penetration rates, or only aggregate results. To address these drawbacks, we utilize Electric Toll Collection systems (ETC) as a large-scale sensor network and design a system called VeMo to transparently model and predict vehicle mobility at the individual level with a full penetration rate. Our novelty is how we address uncertainty issues (i.e., unknown routes and speeds) due to sparse implicit ETC data based on a key data-driven insight, i.e., individual driving behaviors are strongly correlated with crowds of drivers under certain spatiotemporal contexts and can be predicted by combining both personal habits and context information. We evaluate VeMo with (i) a large-scale ETC system with tracking devices at 773 highway entrances and exits capturing more than 2 million vehicles every day; (ii) a fleet consisting of 114 thousand vehicles with GPS data as ground truth. Compared with state-of-the-art benchmark mobility models, the experimental results show that VeMo outperforms them by average 10% in terms of accuracy.

Traffic Flow Impacts of Converting an HOV Lane Into a Dedicated CACC Lane on a Freeway Corridor

Cooperative Adaptive Cruise Control (CACC) systems can increase roadway capacity, but the benefits are marginal at low market penetration rates (MPRs). Thus, a CACC dedicated lane is considered to group CACC vehicles for efficient traffic stream. Concepts of converting existing High Occupancy Vehicle (HOV) lanes into CACC lanes emerge, which leverages the infrastructural facilities and experience with HOV lanes. However, it is unclear to which extent changing HOV lanes to CACC lanes can influence freeway operations. This study examines the traffic flow impacts of converting HOV lanes into CACC lanes regarding CACC MPRs on a complex freeway corridor with multiple interacting bottlenecks in California. A simulation model capable of reproducing flow characteristics with HOV lane and CACC systems is employed for the assessment. Special attention is paid to macroscopic congestion patterns, CACC lane utilization, travel time reliability and CACC operation characteristics. The results show that converting to CACC lanes at low MPRs (130%) can exacerbate congestion in general purpose lanes, whereas at mediate CACC MPRs (40%-50%) the congestion is drastically alleviated due to a large share of traffic carried by CACC lanes.

Trajectory reconstruction for freeway traffic mixed with human-driven vehicles and connected and automated vehicles

The development of technologies related to connected and automated vehicles (CAVs) allows for a new approach to collect vehicle trajectory. However, trajectory data collected in this way represent only sampled traffic flow owing to low penetration rates (PRs) of CAVs and privacy concerns, and fail to provide a comprehensive picture of traffic flow. This study proposes a method to reconstruct vehicle trajectories in fully sampled traffic flow on freeways that consists of human-driven vehicles (HVs) and CAVs by using the mobile sensing data acquired from CAVs. The expected behavior of vehicles within the detection range of CAVs is determined based on the driving state classified by the Wiedemann model, i.e., free driving, emergency, closing, and following. If the actual behavior is different from the expected, it is deemed to be influenced by the undetected HVs. Then, new HVs are inserted based on the estimated local traffic density and speed of the freeway. The trajectories of the inserted HVs are further reconstructed by using the established update rules of cellular automation, i.e., uniform motion, acceleration, deceleration, randomization and position update. Last, the proposed method was examined by simulation experiments under different traffic densities and PRs of CAVs. The results show that the trajectories of fully sampled mixed traffic flow can be reconstructed reasonably well, not only under traffic conditions without explicit congestion but in congested environments.

On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment

The new era of sharing information and “big data” has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the existing traditional ways of collecting traffic data that are based either on fixed-location sensors or GPS devices with low spatial coverage or penetration rates and significant measurement errors, especially in congested urban areas. Unmanned Aerial Systems (UAS) or simply “drones” have been proposed as a pioneering tool of the Intelligent Transportation Systems (ITS) infrastructure due to their unique characteristics, but various challenges have kept these efforts only at a small size. This paper describes the system architecture and preliminary results of a first-of-its-kind experiment, nicknamed pNEUMA, to create the most complete urban dataset to study congestion. A swarm of 10 drones hovering over the central business district of Athens over multiple days to record traffic streams in a congested area of a 1.3 km2 area with more than 100 km-lanes of road network, around 100 busy intersections (signalized or not), many bus stops and close to half a million trajectories. The aim of the experiment is to record traffic streams in a multi-modal congested environment over an urban setting using UAS that can allow the deep investigation of critical traffic phenomena. The pNEUMA experiment develops a prototype system that offers immense opportunities for researchers many of which are beyond the interests and expertise of the authors. This open science initiative creates a unique observatory of traffic congestion, a scale an-order-of-magnitude higher than what was available till now, that researchers from different disciplines around the globe can use to develop and test their own models.

The joint effects of driving hedonism and trialability on the choice between internal combustion engine, hybrid, and electric vehicles

The low penetration rate of electric vehicles (EVs) is raising concern among policy makers and car designers who face risky decisions whether to invest in EV technology and promotion. Traditionally, battery electric vehicles (BEV) and hybrid electric vehicles (HEV) were considered successive technologies. Yet, it is becoming apparent that in the next few decades these technologies will co-exist, which revives the interest in the choice amongst them. This study focuses on normative and hedonic goals to understand the impact of innovativeness and driving hedonism and their interaction with user experience and pro-environmental attitudes of consumers choosing between conventional, hybrid and electric cars. The behavioral model challenges utility-based models of auto propulsion choices by integrating hedonic goal-framing and its interaction with product experience into Rogers’ diffusion of innovation model. In a discrete choice experiment informed by stated-preference Bayesian efficient design among 309 participants, we find that the interaction between driving hedonism and BEV 'trialability' is positively related to the adoption of HEVs rather than of BEVs. Compared to environmental consumers who lack driving hedonism, the segment of innovative-environmentalists act as BEV adoption pioneers and the segment of innovative-environmentalist-hedonists are HEV adoption pioneers.

Multi-objective coordinated scheduling of anomalous control domain with electric/thermal energy storage and translatable load

Difficulties in the consumption of new energy such as wind and light are one of the main reasons for the low penetration rate of distributed power. The multi-objective coordinated scheduling model of anomalous control domain with electric/thermal energy storage and translatable load is constructed from the aspects of energy utilization, system network loss and environmental pollution cost. A hybrid random black hole algorithm is proposed to improve the algorithm by using global search mechanism. Optimization ability. The example analysis is carried out by IEEE30 node power distribution system. The simulation results show that the coordinated optimization of energy storage and translatable load can effectively improve energy efficiency, reduce system network loss and environmental pollution cost, and verify the rationality of the proposed model

Combinatorial cell surface display system in Escherichia coli for noninvasive colorectal cancer detection

Abstract Navigation and recognition of disease lesions remain challenging during colorectal cancer diagnosis and treatment, given that the precision and capacity of using specific surface antigens as recognition sites are relatively lacking. Moreover, the low penetration rate of noninvasive detection methods has delayed disease diagnosis in developing countries. We developed a bacterial device targeting the Thomsen–Friedenreich antigen, which is abundant on lesion tissue, and produced vesicles that serve as markers for ultrasonic detection. The device uses a bacterial cell surface display system and acoustic reporter gene to function as a screening device for colorectal cancer detection. The diagnostic efficiency of this device was determined by flow cytometry, immunohistochemistry, microfluidic chip-based assay, and ultrasonic examinations on both cellular and tissue scales. In all scales and experiments, our device showed great feasibility upon differentiating disease lesion and normal tissue, while foundation of the usage of vesicle reporter system as noninvasive method has also been laid. The application of this device provides insight into the practicability and prospect of bacterial detecting agents in the field of diagnostics. All animal studies were approved by the Institutional Animal Care and Use Committee of Shanghai Jiao Tong University, China (approval No. 201801015) on February 23, 2018.

Short-term traffic flow prediction based on faded memory Kalman Filter fusing data from connected vehicles and Bluetooth sensors

This paper proposes a Kalman Filter (KF) technique to predict the short-term flow at urban arterials based on the information of connected and Bluetooth equipped vehicles. Online traffic flow prediction using real-time data derived from different sensors is still an open research subject. To this end, a Kalman Filter model is developed to predict traffic flow based on two sources of real-time data, i.e. Connected Vehicles (CVs) and Bluetooth data. We also apply a Faded Memory Kalman Filter (FMKF) by considering more weights for new measurements to overcome the issue of inaccuracy in the prediction model and to predict the traffic flow with more resolution. At first, based on training data from Vissim traffic simulator, parameters of the KF's equations are calibrated using a machine learning based and big data processing. Performance of the conventional and faded memory KF models are then validated and compared using some test data pertaining to different rates of connected vehicles and Bluetooth-equipped vehicles (BVs). We use a pilot study of the city of Melbourne, Australia for numerical tests. The results indicate significant superiority of the FMKF over the KF in various traffic situations, as such the prediction error in some cases has reduced up to 60%. This paper contributes to the literature in three folds: (i) It uses a computationally efficient flow prediction algorithm based on synthesizing data from CVs and BVs (ii) It proposes to use an adaptive form of KF (i.e. FMKF) to compensate for the prediction error originating from modelling error. Hence, the model can perform well for a range of traffic conditions (iii) The proposed model works well even with low penetration rates (PRs) of the CVs or BVs (say 20%).

Extended Observer for Urban Traffic Control Based on Limited Measurements From Connected Vehicles

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle traffic state estimation and prediction based on limited CV measurements is presented. Furthermore, the proposed formulation enables fusion of CV with other data sources and their integration in any UTC system. The developed Extended Observer (EO) is a discrete-time, variable-dimension implementation of the Extended Kalman Filter. It does not require loop detectors and is independent of the type of signal control. The evaluation focuses on the queue length estimation. Especially in oversaturation, the EO outperforms the CV measurements for all examined penetration rates. Moreover, the highest benefit is observed for the lowest penetration rates. The results show that for oversaturation and low penetration rates the EO improves the CV measurements 13-31%. In addition, the EO is tested with an adaptive UTC system by feeding the fused estimation from CV and camera measurements. The error in queue length estimation from the EO is significantly lower than the error based on stochastic arrivals. Additionally, the results show a reduction in delays at the examined signal and the complete intersection. Overall, this paper sheds light on the potential benefits from enhancing limited CV measurements in order to contribute immediately to current UTC systems.