Loop Detectors Research Articles

A Lagrangian approach for variable speed limit implementation in C-ITS framework

With the development of Cooperative-Intelligent Transport System (C-ITS) technologies, new strategies based on embedded technologies have emerged to manage road networks. This paper focuses on adapting to this connectivity context a Variable Speed Limit (VSL) system to detect shockwaves and anticipate their propagation based on the kinematic wave theory to dampen them. We provide an alternative framework to adapt the VSL strategy, well-suited for Eulerian approaches, into a Lagrangian context. While the Eulerian approach is based on Loop Detector (LD) and macroscopic traffic indicators (e.g. flow, density), our Lagrangian approach relies on Road Side Units (RSUs) that record the GPS traces shared by Connected Vehicles (CVs). Based on the combination of CV trajectories, Fundamental and Space-Time Diagrams theory, shockwave estimation and prediction processes directly operate on congestion waves, which release the estimation issue for traffic density. The simulation-based analysis reveals that the performance of the Lagrangian approach is comparable to the Eulerian configurations.

Traffic flow prediction for highway vehicle detectors through decomposition and machine learning

ABSTRACT Traffic flow prediction is of significant importance in traffic planning. Currently, traffic flow data are primarily collected through loop detectors. However, factors such as road conditions can affect the accuracy of these data. To address this issue, this paper proposes a traffic flow prediction method based on decomposition and machine learning. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method decomposes the sequence into multiple intrinsic mode functions (IMFs). The complexity of each IMF is calculated using the sample entropy (SE), and then the IMFs are reconstructed. Parameters of the variational mode decomposition (VMD) are optimized using the whale optimization algorithm (WOA) for the secondary decomposition, and predictions are made using gated recurrent units (GRU). Finally, the prediction results are reconstructed to obtain the final prediction values. In the case study section, experiments are conducted using datasets from three detectors to explore different decomposition forms and methods.

Traffic state estimation from vehicle trajectories with anisotropic Gaussian processes

Accurately monitoring road traffic state is crucial for various applications, including travel time prediction, traffic control, and traffic safety. However, the lack of sensors often results in incomplete traffic state data, making it challenging to obtain reliable information for decision-making. This paper proposes a novel method for imputing traffic state data using Gaussian processes (GP) to address this issue. We propose a kernel rotation re-parametrization scheme that transforms a standard isotropic GP kernel into an anisotropic kernel, which can better model the congestion propagation in traffic flow data. The model parameters can be estimated by statistical inference using data from sparse probe vehicles or loop detectors. Moreover, the rotated GP method provides statistical uncertainty quantification for the imputed traffic state, making it more reliable. We also extend our approach to a multi-output GP, which allows for simultaneously estimating the traffic state for multiple lanes. We evaluate our method using real-world traffic data from the Next Generation simulation (NGSIM) and HighD programs, along with simulated data representing a traffic bottleneck scenario. Considering current and future mixed traffic of connected vehicles (CVs) and human-driven vehicles (HVs), we experiment with the traffic state estimation (TSE) scheme from 5% to 50% available trajectories, mimicking different CV penetration rates in a mixed traffic environment. We also test the traffic state estimation when traffic flow information is obtained from loop detectors. The results demonstrate the adaptability of our TSE method across different CV penetration rates and types of detectors, achieving state-of-the-art accuracy in scenarios with sparse observations.

Observation of negative capacitance in silicon heterojunction solar cells: role of front contact in carrier depopulation

The origin of the low-frequency inductive loop in the Nyquist plot of the Ag/indium tin oxide (ITO)/p-a-Si:H/intrinsic hydrogenated amorphous silicon (i-a-Si:H)/c-Si/i-a-Si:H/n-a-Si:H/ITO/Al heterojunction (SHJ) solar cells and their effect on the device performance are investigated by adopting impedance spectroscopy under dark and light. The negative capacitance/low-frequency inductive loop originates from the depopulation of injected charge carriers due to a transport barrier at the p-a-Si:H/ITO interface. The p-a-Si:H hole-selective SHJ device with a low-frequency inductive loop also has shown an S-shape and associated performance degradation in the light current density–voltage characteristics due to the opposing field type transport barrier present at the p-a-Si:H/ITO interface, which was overcome after vacuum annealing at ∼200 °C. However, the NiO x -based hole-selective contact Ag/ITO/NiO x /i-a-Si:H/c-Si/i-a-Si:H/n-a-Si:H/ITO/Al SHJ cells have not shown any low-frequency inductive loop or corresponding S-shape and associated performance degradation due to the optimised contact (minimum resistance) between the NiOx and ITO layers.

Traffic Signal Controller System and Vehicle Count-Based Road Congestion Detection

Abstract: Traffic congestion is one of the major modern-day crisis in every big city in the world. Recent study of World Bank has shown that average vehicle speed has been reduced from 21 km to 7 km per hour in the last 10 years in Dhaka. Inter metropolitan area studies suggest that traffic congestion reduces regional competitiveness and redistributes economic activity by slowing growth in county gross output or slowing metropolitan area employment growth. As more and more vehicles are commissioning in an already congested traffic system, there is an urgent need for a whole new traffic control system using advanced technologies to utilize the already existent infrastructures to its full extent. Since building new roads, flyovers, elevated expressway etc. needs extensive planning, huge capital and lots of time; focus should be directed upon availing existing infrastructures more efficiently and diligently. Previously different techniques had been proposed, such as infra-red light sensor, induction loop etc. to acquire traffic date which had their fair share of demerits. In recent years, image processing has shown promising outcomes in acquiring real time traffic information using CCTV footage installed along the traffic light. Different approaches have been proposed to glean traffic data. Some of them count total number of pixels , some of the work calculate number of vehicles .These methods have shown promising results in collecting traffic data.

Advanced Vehicle Detection and Tracking on Roads Using Deep Learning

Abstract: The world has been overtaken by artificial intelligence (AI), machine learning, and deep learning, which are affecting every aspect of our lives, including our need for companionship and the growth of smart cities and communities. Though tremendous progress has been made in fields like healthcare and education, transportation. Transportation is a significant barrier that has a huge financial and human cost on a worldwide scale. Traditional methods of assessing traffic on roads, which rely on inductive loops, don't give much information and can't control traffic in real-time. This study describes how the transportation sector could undergo a deep learning revolution. Deep learning techniques, including image and video analysis, have the potential to revolutionize road traffic monitoring by enabling computers to recognize traffic congestion, enhance road safety, and address various other concerns. The integration of deep learning technologies promises a new era of data-driven solutions for transportation-related issues, which could ultimately result in safer, more effective, and economically sustainable societies in the face of over a million annual fatalities and trillions of dollars in damages. In order to address these problems, this paper shows how well YOLOv3 detects automobiles and how well Darknet53 tracks these vehicles while they are on the road.

Design, Fabrication, and Characterization of a Planar Three-Electrode Trigger Switch Based on Flexible Printed Circuit Process.

An exploding foil initiator system (EFIs) is essential in modern weaponry for its safety and reliability. As the main component of EFIs, the performance of the switch is critical to EFIs. In this study, a planar three-electrode trigger switch was designed and fabricated using the Flexible Printed Circuits (FPC) process. Subsequently, the performance of the FPC switch was tested. The results show that the self-breakdown voltage of the FPC switch is stable. In addition, an FPF switch with a 0.6 mm main electrode gap demonstrated consistency, with delay times below 31.75 ns, and a jitter ranging from 1.7 ns to 10.94 ns at 900 V to 1200 V, evidencing the FPC switches' reliability and uniform performance across various voltages. Compared to the Micro-Electro-Mechanical Systems (MEMS) switches of similar dimensions, the FPC switches achieved a faster high-current attainment with less inductance, showing a 5% reduction in loop inductance. The repetitive testing results demonstrate that the FPC switch maintains consistent output performance, with stable peak currents, peak current time, and delay time over 50 action cycles, highlighting its repeatability. The FPC switch was assembled with an EFI chip and capacitor into an integrated system, which was subsequently able to successfully detonate HNS-IV at 1000 V/0.22 μF, proving the FPC switch's potential in low inductance applications.

Photon number resolving detection with a single-photon detector and adaptive storage loop

Photon number resolving (PNR) measurements are beneficial or even necessary for many applications in quantum optics. Unfortunately, PNR detectors are usually large, slow, expensive, and difficult to operate. However, if the input signal is multiplexed, photon ‘click’ detectors, that lack an intrinsic PNR capability, can still be used to realize photon number resolution. Here, we investigate the operation of a single click detector, together with a storage line with tunable outcoupling. Using adaptive feedback to adjust the storage outcoupling rate, the dynamic range of the detector can in certain situations be extended by up to an order of magnitude relative to a purely passive setup. An adaptive approach can thus allow for photon number variance below the quantum shot noise limit under a wider range of conditions than using a passive multiplexing approach. This can enable applications in quantum enhanced metrology and quantum computing.

Graph Convolutional Gated Recurrent Unit Network for Traffic Prediction Using Loop Detector Data

Traffic prediction is challenging due to the stochastic nonlinear dependencies in spatiotemporal traffic characteristics. We introduce a Graph Convolutional Gated Recurrent Unit Network (GC-GRU-N) to capture the critical spatiotemporal dynamics. Using 15-min aggregated Seattle loop detector data, we recontextualize the prediction challenge across space and time. We benchmark our model against Historical Average, LSTM, and Transformers. While Transformers outperformed other models, our GC-GRU-N came in a close second with notably faster inference time — six times quicker than Transformers. We offer a comprehensive comparison of all models based on training and inference times, MAPE, MAE, and RMSE. Furthermore, we delve into the spatial and temporal characteristics of each model’s performance.

Changes to the Transport Behaviour of Inhabitants of a Large City Due the Pandemic

On 11 March 2020, the World Health Organisation (WHO) classified the COVID-19 outbreak as a global pandemic and, in consequence, many countries took steps to prevent the importation and subsequent local transmission of the SARS-CoV-2 virus, resulting in restrictions on economic activity, transport, travel, and daily mobility. Although the COVID-19 pandemic and its impact on daily mobility have been widely addressed in the literature, there is a limited number of studies that indicate to what extent these changes have become permanent. The purpose of this study was to determine the nature and scale of the impact of the COVID-19 pandemic on the local transport system of a large city in Poland (Łódź) and, above all, to identify the permanence of this impact. To accomplish these objectives, a questionnaire survey was conducted using the computer-assisted telephone interviewing (CATI) technique on a sample of 500 residents, which included questions on daily mobility in the period before (autumn 2019) and after (autumn 2022) the pandemic. In addition, the results of the questionnaire survey were juxtaposed with data from intelligent transport systems (ITS) (data from 20 induction loops, and data on the number of tickets validated in public transport vehicles). Not only did the pandemic change the frequency of spatial motivations, but it also affected trip durations and preferred modes of transport. The most unfavourable changes concern the modal division and the increase in the use of private transport at the expense of public transport. Understanding the durability of the impact of the pandemic on changes in the spatial mobility of the population may help to develop transport policies and increase the resilience of transport systems to possible future pandemics.

Design of Half-Bridge Switching Power Module Based on Parallel-Connected SiC MOSFETs for LLC Resonant Converter with Symmetrical Structure and Low Parasitic Inductance

SiC MOSFETs are used in many power conversion applications because of their superior characteristics, such as fast switching speed, low on-resistance, and high operating temperature. In certain high-power systems, SiC MOSFETs are connected in parallel to enhance their current capacity and power efficiency. However, compared with Si-based devices, the current imbalance caused by the parasitic inductance difference becomes more severe when driving SiC MOSFETs in parallel, owing to the fast switching speed. Furthermore, the power loop inductance imbalance that occurs when constructing a half-bridge with parallel SiC MOSFETs has rarely been addressed in previous studies. In this study, a half-bridge switching power module based on parallel-connected SiC MOSFETs is proposed to solve the current imbalance through a symmetric structure of the gate and power loops. The effects of the magnitude and imbalance of the gate and power loop inductances in the half-bridge structure based on parallel-connected devices are also explained. A detailed printed circuit board layout of the proposed switching power module is provided, and the inductance symmetry is verified through simulations. A double-pulse test is conducted to verify the current-balancing capability of the proposed switching power module. In addition, an LLC resonant converter is designed using the proposed switching power module, and the power loss between parallel SiC MOSFETs is compared. The experimental results indicate the total power loss error between the parallel-connected SiC MOSFETs of the proposed power module is only 1.94%.

Harnessing stochasticity for superconductive multi-layer spike-rate-coded neuromorphic networks

Conventional semiconductor-based integrated circuits are gradually approaching fundamental scaling limits. Many prospective solutions have recently emerged to supplement or replace both the technology on which basic devices are built and the architecture of data processing. Neuromorphic circuits are a promising approach to computing where techniques used by the brain to achieve high efficiency are exploited. Many existing neuromorphic circuits rely on unconventional and useful properties of novel technologies to better mimic the operation of the brain. One such technology is single flux quantum (SFQ) logic—a cryogenic superconductive technology in which the data are represented by quanta of magnetic flux (fluxons) produced and processed by Josephson junctions embedded within inductive loops. The movement of a fluxon within a circuit produces a quantized voltage pulse (SFQ pulse), resembling a neuronal spiking event. These circuits routinely operate at clock frequencies of tens to hundreds of gigahertz, making SFQ a natural technology for processing high frequency pulse trains. This work harnesses thermal stochasticity in superconducting synapses to emulate stochasticity in biological synapses in which the synapse probabilistically propagates or blocks incoming spikes. The authors also present neuronal, fan-in, and fan-out circuitry inspired by the literature that seamlessly cascade with the synapses for deep neural network construction. Synapse weights and neuron biases are set with bias current, and the authors propose multiple mechanisms for training the network and storing weights. The network primitives are successfully demonstrated in simulation in the context of a rate-coded multi-layer XOR neural network which achieves a wide classification margin. The proposed methodology is based solely on existing SFQ technology and does not employ unconventional superconductive devices or semiconductor transistors, making this proposed system an effective approach for scalable cryogenic neuromorphic computing.

The effects of additive agents on zinc and zinc–nickel alloys electrodeposited on steel substrates in acidic electrolytes

The influence of glycine, as a chelating agent, and triblock copolymer mixture of PPG-PEG-PPG and PEG1000, as a suppressor agent, on the deposition mechanisms of zinc and zinc-nickel alloys were studied by cyclic voltammetry (CV) and potentiometric electrochemical impedance spectroscopy (PEIS), using electrolyte solutions of chlorinated acid. Cyclic voltammetry measurements show that the composition of the electrolytic solution affects the voltammogram profiles. Complementary PEIS investigations allowed the electrodeposition mechanisms to be highlighted depending on the composition of the electrolytic solution. The electrodeposition of zinc, in the absence of additives, occurs in a single cathodic wave from the ZnCl3− species, whereas in the presence of glycine, a second more cathodic reduction wave is relative to the Zn(Gly)22+ species. The presence of the PPG-PEG-PPG and PEG1000 triblock copolymer induces a clear inhibition of the zinc reduction. Nevertheless, the zinc electrodeposition follows a three-dimensional instantaneous nucleation. The zinc electroplating mechanism involves a capacitive semi-circle at high frequency and two inductive loops at medium and low frequencies assigned to two adsorbed intermediates onto the substrate whatever the electrolyte composition. The presence of the polymer mixture affects the zinc reduction by increasing the charge-transfer resistance and the adsorption phenomena on the substrate. The electrodeposition of zinc-nickel alloys reveals a single reduction wave, from ZnCl3− and Ni(Gly)2+ species, and multiple anodic signals depending on the electrolytic composition, according to the presence of glycine to chelate the nickel species. The zinc-nickel electroplating was evidenced to be a multiple-step process with a three-dimensional instantaneous nucleation, including intermediate species adsorbed onto the substrate. The equivalent circuit proposed consists of two capacitive half-circles separated by an inductive loop for any electrolytic solution. However, the impedance spectra were affected by the presence of glycine by modifying the inductive loop reflecting the first adsorbed intermediate (Ni(I)ads). The morphology of zinc and zinc-nickel, with a nickel content between 14 and 19 wt%, is affected by the electrolyte composition, however with the same crystal lattice. The preferential orientation of the lattice was shown to depend on the additives present in the solution.

High-density superconductive logic circuits utilizing 0 and π josephson junctions

Superconductor Electronics (SCE) is a fast and power-efficient technology with great potential for overcoming conventional CMOS electronics’ scaling limits. Nevertheless, the primary challenge confronting SCE today is its integration level, which lags several orders of magnitude behind CMOS circuits. In this study, we have innovated and simulated a novel logic family grounded in the principles of phase shifts occurring in 0 and π Josephson junctions. The fast phase logic (FPL) eliminates the need for large inductor loops and shunt resistances by combining the half-flux and phase logic. Therefore, the Josephson junction (JJ) area only limits the integration density. The cells designed with this paradigm are fast, and the clock-to-Q delay for logic cells is about 4ps. While maintaining over 50% parameter margins for wiring cells. This logic is power efficient and can increase the integration by at least 100×in the SCE chips.

Accessibility of Library Building for People With Disabilities in Jakarta Library

The lack of accessibility for people with disabilities in public facilities makes it difficult for people with disabilities to carry out activities and mobility easily, safely, comfortably, and independently, especially in terms of meeting information needs and accessing information needed in libraries. This study aims to find out the implementation of accessibility of library buildings for people with disabilities in the Jakarta-Cikini Library. The research method used is a qualitative approach with a type of case study research. Data collection through observation, interviews, and documentation. The selection of informants in this study used purposive sampling. The results showed that the implementation of accessibility of library buildings for people with disabilities in the Jakarta-Cikini Library can be said to be quite good based on IFLA standard guidelines. There are only a few accessibility that have not been implemented thoroughly such as disability-only parking lots far from the entrance and symbol colors in parking lots that have faded, doors and walls that use glass there are no stickers or warning signs to watch out for glass, the unavailability of an induction loop system at the circulation and information desk, the unavailability of handles to help disabled people standing on the toilet and the absence of the application of guiding blocks Inside the library.

Large second-order Josephson effect in planar superconductor-semiconductor junctions

We investigate the current-phase relations of Al/InAs-quantum well planar Josephson junctions fabricated using nanowire shadowing technique. Based on several experiments, we conclude that the junctions exhibit an unusually large second-order Josephson harmonic, the \sin(2\varphi)sin(2φ) term. First, superconducting quantum interference devices (dc-SQUIDs) show half-periodic oscillations, tunable by gate voltages as well as magnetic flux. Second, Josephson junction devices exhibit kinks near half-flux quantum in supercurrent diffraction patterns. Third, half-integer Shapiro steps are present in the junctions. Similar phenomena are observed in Sn/InAs quantum well devices. We perform data fitting to a numerical model with a two-component current phase relation. Analysis including a loop inductance suggests that the sign of the second harmonic term is negative. The microscopic origins of the observed effect remain to be understood. We consider alternative explanations which can account for some but not all of the evidence.

Real-Time Unmanned Aerial Vehicle-Based Traffic State Estimation for Multi-Regional Traffic Networks

Traffic state estimation is a challenging task because of the collection of sparse and noisy measurements from fixed points in the traffic network. Induction loops, as they are non-intrusive, can observe any area of the traffic network on demand and provide accurate traffic density and speed measurements. Our main contribution is the development of an optimization framework where small parts of the traffic network are monitored by Unmanned Aerial Vehicles (UAVs) and accurate estimates of traffic density and mean speeds for every region in the traffic network are returned in real-time. Assuming regional-based traffic dynamics, a cyclical UAV flight path is defined for each region. One UAV is assigned to each flight path and monitors a small area of the region below. The UAV-based traffic measurements are expressed as moving averages to smooth out fluctuations in traffic density and mean speed. A moving horizon optimization problem is formulated, which minimizes the estimation and process errors over a moving time window. The problem is non-convex and challenging to solve, because of the presence of nonlinear traffic dynamics. By considering free-flow conditions, the optimization problem is recast to a quadratic program that returns density estimations for each region of the traffic network in real-time. Simulation results compare our UAV framework to an alternative, where the whole traffic network is monitored by UAVs. Both frameworks obtain similar results, despite the alternative framework using more UAVs than our framework.

The role of the oxide layer in the corrosion of aluminium in acidic solutions

AbstractThe role of the oxide layer in the corrosion of aluminium in hydrochloric, sulphuric(VI) and orthophosphoric acid is studied at pH 1. The oxide layer is stable in hydrochloric and sulphuric acids. The corrosion rate of aluminium in these solutions is 0.2 and 0.3 mm/year, respectively. The oxide layer is unstable in orthophosphoric acid and the corrosion rate is higher, 1.75 mm/year. The corrosion process is studied using scanning electron microscopy, immersion and electrochemical techniques. The mechanism of corrosion of aluminium in the orthophosphoric acid solution is proposed. It includes the adsorption of a reaction intermediate on the surface of the corroding metal and the formation of the oxide layer at anodic potentials with respect to the open‐circuit potential. It is also explained when the inductive loop appears on the electrochemical impedance spectra of aluminium corroding in orthophosphoric acid solution. The mechanism of aluminium corrosion in hydrochloric and sulphuric acid is more complex compared to orthophosphoric acid, and is not analysed in detail.

Traffic Density Estimation for Traffic Management Applications Using Neural Networks

Traffic density is one of the elemental variables used in molding road traffic kinetics. Current density estimation techniques include loop detectors and sensors which are dependent on the crowd-sourcing of traffic data, which suffers from limited coverage and high cost. This article proposes a unique method to estimate traffic density based on neural network and mathematical modelling which uses surveillance feed from cameras. The proposed method can save both transportation costs and journey time, thus helping in better traffic management. The result analysis shows that the proposed method works well for varying traffic flow conditions and dynamic conditions.

Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows

To develop the most appropriate control strategy and monitor, maintain, and evaluate the traffic performance of the freeway weaving areas, state and local Departments of Transportation need to have access to traffic flows at each pair of on-ramp and off-ramp. However, ramp flows are not always readily available to transportation agencies, and little effort has been made to estimate these missing traffic flows in locations where no physical sensors are installed. To bridge this research gap, a data-driven framework is proposed that can accurately estimate the missing ramp flows by solely using data collected from loop detectors on freeway mainlines. The proposed framework employs a transfer learning model. The transfer learning model relaxes the assumption that the underlying data distributions of the source and target domains must be the same. Therefore, the proposed framework can guarantee high-accuracy estimation of on-ramp and off-ramp flows on freeways with different traffic patterns, distributions, and characteristics. Based on the experimental results, the flow estimation mean absolute errors range between 23.90 veh/h to 40.85 veh/h for on-ramps and 31.58 veh/h to 45.31 veh/h for off-ramps; the flow estimation root mean square errors range between 34.55 veh/h to 57.77 veh/h for on-ramps, and 41.75 veh/h to 58.80 veh/h for off-ramps. Further, the comparison analysis shows that the proposed framework outperforms other conventional machine learning models. The estimated ramp flows based on the proposed method can help transportation agencies to enhance the operations of their ramp control strategies for locations where physical sensors are not installed.