Approach Speed Research Articles

Factors Affecting Driver Yielding at Uncontrolled Crosswalks

This study evaluated driver yielding behavior at basic- versus high-visibility crosswalk (HVC) markings on undivided major street approach legs of two-way stop-controlled intersections. The research team measured and compared driver yielding rates using staged pedestrian crossings at sites with HVCs and at sites with basic markings. Study sites were undivided two-lane roadways with relatively low speeds (25 to 35 mph speed limits) and volumes (up to 616 two-way vehicles per hour during crossings)—half with basic crosswalk markings, half with HVCs. In addition to site characteristic data (crossing distance, presence of bike lane, presence of on-street parking, location context), the study included the collection of vehicle volumes and approach speeds, which enabled statistical modeling of yielding in relation to site characteristics and driver speeds. In total, the study collected 1,188 individual observations at 32 sites in four states. The results showed that, under certain conditions, HVCs were associated with increased driver yielding relative to crossings with basic markings. The study further showed that an approaching driver’s speed and prevailing site speeds had a strong and consistent negative effect on driver yielding. Site context and other treatments present at the site had an impact, but the relationship between driver speed and yielding was robust and statistically significant across all tests. Drivers exceeding 40 mph were unlikely to yield to a pedestrian waiting at a marked crosswalk, suggesting that an agency should consider supplemental treatments to increase driver yielding at such locations.

Ontologies for Supporting Traffic Behaviour, Critical Gap, and Conflict Models at Unsignalized Intersection Routes

Background: The challenge arises from management of big data in transportation can be identified by ontological approach. Road accidents are regarded as a significant cause of both harm to humans and financial damage in many countries. In Malaysia, there were 567,516 accidents in 2020 alone. This translates into a daily average of 13 people killed due to traffic accidents in the country. Here, our work focuses on gap patterns, critical gap analysis, gap acceptance of right-turning motorists (RTMs) and serious conflict model on Malaysia Rural Roadways at three-leg unsignalized intersections. Methods: In early stage, traffic volume, motorist turning manoeuvre and speed study are implemented to identify the traffic behaviour at selected intersection. Three unsignalized intersection (UI) was involved namely (UI2), (UI 9), and (UI10). In the development of logistic regression models, five different datasets were used in this study: right-turning motorist (RTM) at unsignalized intersection UI2 (259 dataset), right-turning motorist at UI 9 (239 dataset), right turning motorist at UI 10 (314 dataset), right turning motorist combined model (812 dataset) and serious conflict lane change (351 dataset). Determination of critical gap was carried out at each unsignalized intersection 2, 9 and 10. Meanwhile gap-pattern analysis at each intersection used visualization spatial plot. In addition, this work investigated logistic regression method, artificial neuron network and structural equation modelling. Results: Gap pattern three was discovered to be a vulnerable gap pattern. Furthermore, this research reveals that the attributes of the gap three pattern, motorcycles rider, speed limit exceeding 50 kph and RTMs where motorcycles stop near passenger cars in minor roads encourage serious conflict. Moreover, this study proposes novelty tool for identifying hazardous unsignalized intersection by using visualization spatial plot between approach speed and gap.

Rapid road weather hazard forecasting using machine learning

For more than 35 years, the Met Office has been generating and delivering forecasts of road weather hazards, using a physics-based surface-exchange-scheme model. Currently producing forecasts at a new location requires a long initialisation period. However, this can be reduced by providing the model with accurate estimates of initial road surface temperatures. In this paper, we describe a neural network model we have developed to quickly translate readily available atmospheric forecast information into initial road surface temperature estimates. In this way, we combine the advantages of a traditional physics-based approach with the speed of a machine learning approach.

Contextually aware roadside radiation measurement testbed

We demonstrate a contextually aware multimodal roadside radiation measurement detection testbed for traffic monitoring applications in nuclear nonproliferation. Many variables in traffic such as vehicle or cargo size, mass, speed, shape, and distance of closest approach can have significant impacts on the radiation measured from a vehicle-transported radiation source. These factors can lead to uncertainties in the analysis of the radiation source, especially for lower-strength radiation sources of interest. Our testbed, known as the Multimodal Measurement System (MMS) uses non-radiation sensors including magnetometers, geophones, radiofrequency receivers, cameras, and LiDAR to extract contextual information about vehicles passing by the system. These contextual data can then be fused with data from radiation measurements to increase the system’s sensitivity and accuracy in nuclear threat detection applications. This work describes the instrumentation of the MMS and its data acquisition pipeline. Furthermore, we describe the pre-analysis performed on the raw multimodal data streams for data fusion, and the high-level machine learning analyses for detection and characterization. The variety of sensors within the MMS provides a valuable testbed that can be used to identify the combinations of contextual sensors that provide the greatest improvements to radiation source detection and characterization within the restrictions for various proliferation detection applications. The MMS is also modular so that additional combinations of sensors can be explored in the future.

Application of Synchrotron Radiation-Based Fourier-Transform Infrared Microspectroscopy for Thermal Imaging of Polymer Thin Films.

The thermal imaging of surfaces with microscale spatial resolution over micro-sized areas remains a challenging and time-consuming task. Surface thermal imaging is a very important characterization tool in mechanical engineering, microelectronics, chemical process engineering, optics, microfluidics, and biochemistry processing, among others. Within the realm of electronic circuits, this technique has significant potential for investigating hot spots, power densities, and monitoring heat distributions in complementary metal-oxide-semiconductor (CMOS) platforms. We present a new technique for remote non-invasive, contactless thermal field mapping using synchrotron radiation-based Fourier-transform infrared microspectroscopy. We demonstrate a spatial resolution better than 10 um over areas on the order of 12,000 um2 measured in a polymeric thin film on top of CaF2 substrates. Thermal images were obtained from infrared spectra of poly(methyl methacrylate) thin films heated with a wire. The temperature dependence of the collected infrared spectra was analyzed via linear regression and machine learning algorithms, namely random forest and k-nearest neighbor algorithms. This approach speeds up signal analysis and allows for the generation of hyperspectral temperature maps. The results here highlight the potential of infrared absorbance to serve as a remote method for the quantitative determination of heat distribution, thermal properties, and the existence of hot spots, with implications in CMOS technologies and other electronic devices.

Algorithmic support of the adaptive system of controlled flight into terrain avoidance (CFITA)

The article considers the restrictions algorithms for the trajectory parameters of aircraft motion. Three groups of algorithms are under consideration. They are distinguished by the volume of airborne database about terrain, modes of aircraft operation. The first group (the full SFITA mode) uses the maximum digital cartographic information (DCI) about the terrain. In the SFITA algorithms, the terrain is approximated in the form of planes in space in some predictable, pre-emptive area in the flight path direction. Terrain following is carried out in space in the assigned direction. The equation of the adjacent plane is solved. The distance, the approach speed, and the time to reach the plane are enumerated. The net acceleration to reduce the approach speed to the plane of restriction is calculated under the specified constraint controls. The required time is calculated to reduce the approach speed towards the obstacle to zero. Having equated the expressions for the time of reaching and the required time, transition towards the distance to the plane of restriction, on which it is imperative to utilize constraint controls. The second group (the major SFITA mode) uses the DCI about the terrain in the direction of track in a pre-emptive area. The terrain is approximated by a line in the plane. The specified normal overload is used as a means of control. Terrain following is conducted in the vertical or slant planes. Subsequently, the same procedures are used as in the first group. The third group (the minimum SFITA mode) does not use DCI. The radio and barometric pressure altimeters are used as information systems about the terrain. The given SFITA mode is selected only for flights in a flat terrain. The algorithm includes the similar procedures as in the first and in the second groups. The analytical analysis, confirming the adaptive properties of algorithmic support based on the fundamental law of uniformly retarded motion, is given. The considered algorithms efficiency is confirmed by a comprehensive amount of simulation. The presented algorithms can become the foundation for developing Russian TAWS analogues.

RISING: A new framework for model-based few-view CT image reconstruction with deep learning.

Medical image reconstruction from low-dose tomographic data is an active research field, recently revolutionized by the advent of deep learning. In fact, deep learning typically yields superior results than classical optimization approaches, but unstable results have been reported both numerically and theoretically in the literature. This paper proposes RISING, a new framework for sparse-view tomographic image reconstruction combining an early-stopped Rapid Iterative Solver with a subsequent Iteration Network-based Gaining step. In our two-step approach, the first phase executes very few iterations of a regularized model-based algorithm, whereas the second step completes the missing iterations by means of a convolutional neural network. The proposed method is ground-truth free; it exploits the computational speed and flexibility of a data-driven approach, but it also imposes sparsity constraints to the solution as in the model-based setting. Experiments performed both on a digitally created and on a real abdomen data set confirm the numerical and visual accuracy of the reconstructed RISING images in short computational times. These features make the framework promising to be used on real systems for clinical exams.

A climatology-based wind speed map for NBR 6123

abstract: Updating the basic wind speed map of NBR 6123 –-Wind loading on buildings – is one of the duties of the committee responsible for the revision of this code. Traditionally such maps are elaborated by means of extreme value wind speed data collected at meteorological stations, use of statistical methods for data characterization, and application of mathematical regression to elaborate the territorial maps. However, the spatial distribution of the atmospheric phenomena responsible for strong winds cannot be disregarded. This work presents a proposal for a new wind speed map for NBR 6123 combining a climatological approach and wind speed data recently compiled from hundreds of meteorological stations. A climatological wind map was first produced considering the phenomena which cause strong winds, and used as a basis to draw the isopleths of the basic wind speed map, considering the measured wind speed data. The resulting map shows basic wind speeds ranging from 30 to 48m/s.

On the speed of approach of objects during space flights

On the speed of approach of objects during space flights

Object neophilia in wild herring gulls in urban and rural locations

Living with increasing urbanisation and human populations requires resourcefulness and flexibility in wild animals' behaviour. Animals have to adapt to anthropogenic novelty in habitat structure and resources that may not resemble, or be as beneficial as, natural resources. Herring gulls Larus argentatus increasingly reside in towns and cities to breed and forage, yet how gulls are adjusting their behaviour to life in urban areas is not yet fully understood. This study investigated wild herring gulls' responses to novel and common anthropogenic objects in urban and rural locations. We also examined whether gulls' age influenced their object response behaviour. We found that, out of the 126 individual gulls presented with objects, 34% approached them. This suggests that the majority of targeted gulls were wary or lacked interest in the experimental set‐up. Of the 43 gulls that approached the objects, we found that those tested in urban locations approached more slowly than their rural counterparts. Overall, gulls showed no preference for either novel or common anthropogenic objects, and age did not influence likelihood of approach, approach speed or object choice. Individuals paid most attention to the object they approached first, potentially indicative of individual preferences. Our findings indicated that most herring gulls are not as attracted to anthropogenic objects as anecdotal reports have suggested. Covering up obvious food rewards may thus help mitigate human–gull conflict over anthropogenic food sources.

Fabrication of a large scale metasurface with high resolution and enhanced absorption

Plasmonic metasurface nanostructures have the potential to enable nonlinear optical functionality in metasurfaces by reducing power operating thresholds and enabling ultra-thin subwavelength devices. However, low absorption caused by resistive losses of unwanted metallic appearance and irregular corners in the fabrication process significantly reduces this promise, leading the metasurface community toward the new approaches to fabricate large area metasurfaces with Electron Beam lithography (EBL). In this article, with controlled proximity effect and high dose exposure rate in EBL setup, large area (2 cm2) metasurfaces are fabricated with high resolution of structure. The effect of absorption resonance in Infrared (LWIR) is experimentally studied through Fourier Transform Infrared Spectroscopy (FTIR). The results signify that the metasurface with high resolution and fine metallic corners outperforms the fabricated prototype with metal residue and non-uniform corners. When compared to conventional EBL, our nanofabrication approach speeds the patterning time by three times. The experimental measurements reveal enhanced absorption performance at 8 µm wavelength. Whereas, the developed metasurface is numerically studied to explain the absorption performance with plasmonic field distributions. This approach could be used in optoelectronic devices involving plasmonic applications, such as biosensing and infrared imaging.

Assessment of different pedestrian communication strategies for improving driver behavior at marked crosswalks on free channelized right turns

Introduction: Previous studies have indicated low driver yielding rates to pedestrians in various countries. This study analyzed four different strategies to improve driver yielding rates at marked crosswalks on channelized right turn lanes at signalized intersections. Method: A sample of 5,419 drivers was collected for four gestures using field experiments for males and females in the State of Qatar. The experiments were conducted in daytime and nighttime on weekends at three different locations; two sites are located in an urban area and the third is located in non-urban area. The effect of pedestrians’ and drivers’ demographic characteristics, gestures, approach speed, time of the day, location of the intersection, car type, and driver distractions on yielding behavior is investigated using logistic regression analysis. Results: It was found that for the base gesture, only 2.00% of drivers yielded to the pedestrians, while for hand, attempt, and vest-attempt gestures the yielding percentages were considerably higher, 12.81%, 19.59%, and 24.60%, respectively. The results also showed that females received significantly higher yielding rates compared to males. In addition, the probability of a driver yielding increased 2.8 times when drivers approached at slower speed compared to a higher speed. Further, drivers’ age group, accompanied, and distractions were not significant in determining drivers’ probability of yielding.

Experimental investigation of the effect of inward and outward propeller rotation on single blade and propeller loads

This paper is a sequel of the experimental investigation presented in Ortolani and Dubbioso (2019a) that addressed the single blade and propeller loads during the steady phase of the turning circle. In particular, the effect of inward and outward propeller sense of revolution on the propulsive performance has been investigated for a twin screw ship model equipped with a novel set-up pursued for the measurements of the single blade loads. On the basis of previous numerical investigations (Muscari et al., 2017; Dubbioso et al., 2022), the propeller sense of revolution should be evaluated as a passive mean to mitigate the amplification of the mean or fluctuating component of the blade loads and associated side effects (i.e., noise) caused by blade–wake interactions that occur during maneuvering conditions. The understanding and the accurate quantification of propeller loads, in these realistic operative scenario, is pivotal to design low emission and comfortable ships, fulfilling the requirements of safety and continuity of operations at sea. The analysis is carried out revisiting the investigation in Ortolani and Dubbioso (2019a) for the approach speed at FN=0.26 and a large set of rudder angles that span from moderate to tight maneuvers.

Design and Implementation of LIDAR System for Distance Measurements

LIDAR (Light Detection and Ranging) is an optical remote sensing system that can measure the distance to a target by shining light on the target. LIDAR technology is used in robotics to recognize the environment and classify objects. The ability of LIDAR technology to provide a 2D elevation map of the terrain, a highly accurate distance to the ground, and an approach speed allows robots and manned vehicles to land accurately and safely. LIDAR consists of a transmitter that illuminates the target with a laser beam and a receiver that can detect light components that are substantially coaxial with the transmitted beam. The receiver sensor calculates the distance based on the time it takes for the light to reach and return to its destination. A mechanical mechanism with a mirror uses a rotating nodding mirror to pan the light beam and cover the desired scene in one plane or even in 3D. LIDAR can measure distances up to 40 meters, making this system suitable for urban environments. The rider was mounted on a stepper motor and covered the entire area. This paper describes the design and implementation of LIDAR for distance measurement. Keywords: Arduino Nano, LIDAR, VL53L0X sensor, Servomotor, Processing Software.

Re-entry high speed commercial air vehicles: Improving the environmental eligibility at take-off and approach accounting the flight safety restrictions

In addition to the flight safety of re-entry high speed commercial air vehicles their environmental eligibility come into particular important issue due to environmental requirements for commercial subsonic aviation are becoming more stringent. During the re-entry take-off and approach cycles the vehicles are a subject to almost the same type of environmental requirements as the commercial subsonic aviation. The requirements are primarily concern the limitations of community noise and engine exhaust emissions levels, as well as the sonic boom level (for commercial supersonic aviation). Meeting the requirements may significantly complicate the development, certification and further operation of the vehicles. The paper discusses one of the potential ways of increasing the acoustic eligibility of the vehicles during take-off and approach cycles at using the some number of turbofan engines as part of the propulsion system. The way is based on the creation of a special control of turbofan engines thrust/power (so called low noise thrust management) accounting the provision of flight safety restrictions. The assessment of the acoustic impact (community noise) of the vehicle is carried out taking into account the contribution of major noise sources of turbofan engines, i.e. the fan and exhaust jet. The impact of two parameters of turbofan engines such as the bypass ratio and the number of turbofan engines was evaluated. The variation of these parameters was performed under the condition meeting the specified mission requirements in term of the required field (runway) length and the approach speed. The estimations shown that using the created low noise turbofan engine thrust management at the take-off and approach may significantly increase the acoustic eligibility of the vehicles using turbofan engines.

Water Hammer Simulation Using Simplified Convolution-Based Unsteady Friction Model

Omission of frequency-dependent hydraulic resistance (skin friction) during modelling of the water hammer phenomenon is unacceptable. This resistance plays a major role when the transient liquid flow occurs in rigid-walled pipes (steel, copper, etc.). In the literature, there are at least two different modelling approaches to skin friction. The first group consists of models based on instantaneous changes in local and convective velocity derivatives, and the second group are models based on the convolution integral and full history of the flow. To date, more popular models are those from the first group, but their use requires empirical coefficients. The second group is still undervalued, even if based on good theoretical foundations and does not require any empirical coefficients. This is undoubtedly related to the calculation complexity of the convolution integral. In this work, a new improved effective solution of this integral is further validated, which is characterised with the use of a simplified weighting function consisting of just two exponential terms. This approach speeds the numerical calculations of the basic flow parameters (pressure and velocity) significantly. Presented comparisons of calculations using the new procedure with experimental pressure runs show the usefulness of the proposed solution and prove that it maintains sufficient accuracy.

Morphologically Adaptive Crash Landing on a Wall: Soft‐Bodied Models of Gliding Geckos with Varying Material Stiffnesses

Bioinspired Robots In article number 2200120, Ardian Jusufi and co-workers present a bio-inspired soft robotic lander inspired from geckos perching on trees. Hard landings are demonstrated for a range of approach angles and speeds, making the soft bodied lander robust to external perturbations resembling those experienced in real-world conditions. The bio-inspired passive perching mechanism can simplify the design and computational effort required to perform rapid landings on walls. Art work: Ella Maru Studio.

Analysis of change of direction performance with dominant and non-dominant leg using linear and nonlinear approaches

ABSTRACT This study was aimed to analyse the lower limb kinematics during the change of direction (COD) performance with the dominant (DL) and non-dominant (NDL) leg using linear (traditional kinematics) and nonlinear (Self Organising Map-based cluster analysis) approaches. Three 5-0-5 COD performances with the DL and three with the NDL were performed by 23 (aged 21.6 ± 2.3 years) collegiate athletes. No significant difference was observed between the COD duration, and approach speed of DL and NDL. Significantly greater ankle abductions, knee and hip external rotations were identified in COD with DL, compared to NDL (p < .001, d > 0.8). Self Organising Maps portrayed a completely different coordination pattern profile during change of direction performance with the DL and NDL. The cluster analysis illustrated similar inter-individual coordination patterning when participants turned with their DL or NDL. No visible relationship was observed in the cluster analysis of the lower limb joint angles and angular velocities. Outcomes of this study portrayed that coordination patterning (combination of joint angles and the rate of change of angles) could portray the movement patterning differences in different tasks, while a sole investigation on the joint angles or angular velocities may not reveal the underlying mechanisms of movement patterning.

A Second-Order Closed-Form J_2 Model for the Draper Semi-Analytical Satellite Theory

A second-order closed-form semi-analytical solution of the main problem of the artificial satellite theory (J_2 contribution) consistent with the Draper Semi-analytic Satellite Theory (DSST) is presented. This paper aims to improve the computational speed of the numerical-based approach, which is only available in the GTDS-DSST version. The short-period terms are removed by means of an extension of the Lie-Deprit method using Delaunay variables. The averaged equations of motion are given explicitly and transformed to the non-singular equinoctial elements. Finally, the second-order terms in the equations of motion are included in the C/C++ version of the DSST orbit propagator.

“Slow down. Rail crossing ahead. Look left and right at the crossing”: In-vehicle auditory alerts improve driver behavior at rail crossings

Even though the rail industry has made great strides in reducing accidents at crossings, train-vehicle collisions at Highway-Rail Grade Crossings (HRGCs) continue to be a major issue in the US and across the world. In this research, we conducted a driving simulator study (N = 35) to evaluate a hybrid in-vehicle auditory alert (IVAA), composed of both speech and non-speech components, that was selected after two rounds of subjective evaluation studies. Participants drove through a simulated scenario and reacted to HRGCs with and without the IVAA present and through different music conditions and crossing devices. Driver simulator testing results showed that the inclusion of the hybrid IVAA significantly improved driving behavior near HRGCs in terms of gaze behavior, braking reaction, and approach speed to the crossing. The driving simulator study also showed the effects of background music and warning device types on driving performance. The study contributes to the large-scale implementation of IVAAs at HRGCs, as well as the development of guidelines toward a more standardized approach for IVAAs at HRGCs.