scholarly journals Evaluating the impact of new aircraft separation minima on available airspace capacity and arrival time delay

2020 ◽  
Vol 124 (1274) ◽  
pp. 447-471 ◽  
Author(s):  
E. Itoh ◽  
M. Mitici
Keyword(s):  
Delay Time ◽  
Arrival Time ◽  
Flight Time ◽  
Data Driven ◽  
Wake Vortex ◽  
Capacity Control ◽  
Queuing Model ◽  
Air Traffic Controllers ◽  
Arrival Time Delay ◽  
The Impact

AbstractAlthough the application of new, reduced aircraft separation minima can directly increase runway throughput, the impact thereof on the traffic flow of aircraft arriving at the destination airport has not been discussed yet. This paper proposes a data-driven and queue-based modeling approach and presents an analysis of the impact on the delay time of arriving aircraft in the airspace within a radius of 100 nautical miles around an airport. The parameters of our queuing model were estimated by analysing the data contained in the radar tracks and flight plans for flights that arrived at Tokyo International Airport during the 2 years of 2016 and 2017. The results clarified the best arrival strategy according to the distance from the arrival airport: The combination of airspace capacity control and reduction of the flight time and separation variance is the most powerful solution to mitigate delays experienced by arriving traffic while also allowing an increase in the amount of arrival traffic. The application of new wake vortex categories would enable us to increase the arrival traffic to 120%. In addition, the arrival delay time could be minimised by implementing the proposed arrival traffic strategies together with automation support for air traffic controllers.

scholarly journals Queue-Based Modeling of the Aircraft Arrival Process at a Single Airport

Aerospace ◽  
2019 ◽  
Vol 6 (10) ◽  
pp. 103 ◽  
Author(s):  
Eri Itoh ◽  
Mihaela Mitici
Keyword(s):  
Time Delays ◽  
Arrival Time ◽  
Air Traffic ◽  
Data Driven ◽  
Management Systems ◽  
Arrival Process ◽  
Operational Strategies ◽  
Queuing Models ◽  
Air Traffic Controllers ◽  
Transition Points

This paper proposes data-driven queuing models and solutions to reduce arrival time delays originating from aircraft arrival processing bottlenecks at Tokyo International Airport. A data-driven analysis was conducted using two years of radar tracks and flight plans from 2016 and 2017. This analysis helps not only to understand the bottlenecks and operational strategies of air traffic controllers, but also to develop mathematical models to predict arrival delays resulting from increased, future aircraft traffic. The queue-based modeling approach suggests that one potential solution is to expand the realization of time-based operations, efficiently shifting from traffic flow control to time-based arrival management. Furthermore, the proposed approach estimates the most effective range of transition points, which is a key requirement for designing extended arrival management systems while offering automation support to air traffic controllers.

Aspects of performance of truck weigh stations

1993 ◽  
Vol 20 (3) ◽  
pp. 380-385 ◽  
Author(s):  
Edward S. K. Fekpe ◽  
Alan M. Clayton ◽  
Attahiru Sule Alfa
Keyword(s):  
Service Time ◽  
Arrival Time ◽  
Probability Of Detection ◽  
Erlang Distribution ◽  
Significant Delay ◽  
Truck Traffic ◽  
Queuing Model ◽  
Time Service ◽  
The Impact ◽  
Perceived Probability

The performance characteristics of truck weigh stations are presented. Seasonally operated scales and random inspection sites are found to be at least 3 and 8 times respectively as effective in detecting violations as continuously operated permanent scales with no alternative bypass routes. The availability of alternative bypass routes reduces truckers' perceived probability of detection at permanent weigh scales by at least 5%. Higher perceived probabilities are associated with high inspection rates.Queuing theory is employed to study the impact of two alternative manpower levels on the performance of con tinuously operated permanent weigh scales. For the level of truck traffic volume considered, it is found that, theoretically, there is no significant delay to truck traffic through such weigh scale stations irrespective of the manning strategy. It is concluded that it is feasible to maintain minimal staff at the scales and redirect human resources to increase inspection capacities of other methods.The queuing model for the permanent scale operations represents a rare practical example of a classic M/Ek/1 model where the k stages in the service time Erlang distribution are real and not necessarily imaginary. Key words: permanent scale, effectiveness, enforcement, violation, inspection, arrival time, service time.

scholarly journals Measurement of the Sea Surface Height with Airborne GNSS Reflectometry and Delay Bias Calibration

2021 ◽  
Vol 13 (15) ◽  
pp. 3014
Author(s):  
Feng Wang ◽  
Dongkai Yang ◽  
Guodong Zhang ◽  
Jin Xing ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Arrival Time ◽  
Elevation Angle ◽  
Satellite System ◽  
Sea Surface Height ◽  
Antenna Pattern ◽  
Sea Surface ◽  
Observation Method ◽  
Global Navigation Satellite ◽  
The Impact

Sea surface height can be measured with the delay between reflected and direct global navigation satellite system (GNSS) signals. The arrival time of a feature point, such as the waveform peak, the peak of the derivative waveform, and the fraction of the peak waveform is not the true arrival time of the specular signal; there is a bias between them. This paper aims to analyze and calibrate the bias to improve the accuracy of sea surface height measured by using the reflected signals of GPS CA, Galileo E1b and BeiDou B1I. First, the influencing factors of the delay bias, including the elevation angle, receiver height, wind speed, pseudorandom noise (PRN) code of GPS CA, Galileo E1b and BeiDou B1I, and the down-looking antenna pattern are explored based on the Z-V model. The results show that (1) with increasing elevation angle, receiver height, and wind speed, the delay bias tends to decrease; (2) the impact of the PRN code is uncoupled from the elevation angle, receiver height, and wind speed, so the delay biases of Galileo E1b and BeiDou B1I can be derived from that of GPS CA by multiplication by the constants 0.32 and 0.54, respectively; and (3) the influence of the down-looking antenna pattern on the delay bias is lower than 1 m, which is less than that of other factors; hence, the effect of the down-looking antenna pattern is ignored in this paper. Second, an analytical model and a neural network are proposed based on the assumption that the influence of all factors on the delay bias are uncoupled and coupled, respectively, to calibrate the delay bias. The results of the simulation and experiment show that compared to the meter-level bias before the calibration, the calibrated bias decreases the decimeter level. Based on the fact that the specular points of several satellites are visible to the down-looking antenna, the multi-observation method is proposed to calibrate the bias for the case of unknown wind speed, and the same calibration results can be obtained when the proper combination of satellites is selected.

The study of short orbital period of delta scuti pulsating variable stars

2019 ◽  
Vol 15 (S356) ◽  
pp. 407-407
Author(s):  
Abduselam Mohammed
Keyword(s):  
Time Delay ◽  
Orbital Period ◽  
Binary Stars ◽  
Arrival Time ◽  
Periodic Variation ◽  
Variable Stars ◽  
Orbital Elements ◽  
Pulsating Stars ◽  
Delta Scuti ◽  
Arrival Time Delay

AbstractAs a pulsating star moves in its binary orbit, the path length of the light between us and the star varies, leading to the periodic variation in the arrival time of the signal from the star to us (earth). With the consideration of pulsators light arrival time delay effects several new methods which allows using Kepler photometric data (light curves) alone to find binary stars have been recently developed. Among these modern techniques we used binarogram method and we identified that several δSct pulsating stars have companions. The application of these method on detecting long periods(i.e. longer than about 50 d) δSct pulsating stars is not new, but the uniqueness of this study is we verified that it is also applicable to detect and determine the orbital elements of short periods (i.e short orbital period) δSct pulsating stars. With this investigation, we identified the possible way to overcome effects of fictious peaks, even, on the maximum peaks helpful to verify weather the star has companion or not depend up on the existence of the time-delay. Then, we applied the technique on known binary stars and their orbital elements are previously published. Finally, we identified some new short orbital period δSct pulsating stars and obtained their orbital frequency and period with the same procedures. Because of with our attempts we succeeded and verified the applicability of the method (the Binarogram method) on these stars (i.e short orbital period) for the first time, we expect that our present study will play a great role for similar study and to improve our binary statistics.

scholarly journals Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 99 ◽  
Author(s):  
Yueqi Gu ◽  
Orhun Aydin ◽  
Jacqueline Sosa
Keyword(s):  
Los Angeles ◽  
A Priori ◽  
Safety Data ◽  
Spatial Optimization ◽  
Optimal Number ◽  
Multidisciplinary Optimization ◽  
Data Driven ◽  
Fault System ◽  
Design Data ◽  
The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

Impact of UAS with Low Size, Weight, and Power Sensors on Air Traffic Controllers’ Performance and Acceptability Ratings

2020 ◽  
Vol 64 (1) ◽  
pp. 154-158
Author(s):  
Kim-Phuong L. Vu ◽  
Jonathan VanLuven ◽  
Timothy Diep ◽  
Vernol Battiste ◽  
Summer Brandt ◽  
...  
Keyword(s):  
Air Traffic ◽  
Unmanned Aircraft ◽  
Unmanned Aircraft Systems ◽  
Subjective Ratings ◽  
Detection Range ◽  
Human In The Loop ◽  
Air Traffic Controllers ◽  
Low Altitude ◽  
Sector Performance ◽  
The Impact

A human-in-the-loop simulation was conducted to evaluate the impact of Unmanned Aircraft Systems (UAS) with low size, weight, and power (SWaP) sensors operating in a busy, low-altitude sector. Use of low SWaP sensors allow for UAS to perform detect-and-avoid (DAA) maneuvers against non-transponding traffic in the sector. Depending upon the detection range of the low SWaP sensor, the UAS pilot may or may not have time to coordinate with air traffic controllers (ATCos) prior to performing the DAA maneuver. ATCo’s sector performance and subjective ratings of acceptability were obtained in four conditions that varied in UAS-ATCo coordination (all or none) prior to the DAA maneuver and workload (higher or lower). For performance, ATCos committed more losses of separation in high than low workload conditions. They also had to make more flight plan changes to manage the UAS when the UAS pilot did not coordinate DAA maneuvers compared to when they did coordinate the maneuvers prior to execution. Although the ATCos found the DAA procedures used by the UAS in the study to be acceptable, most preferred the UAS pilot to coordinate their DAA maneuvers with ATCos prior to executing them.

scholarly journals The INSESS-COVID19 Project. Evaluating the Impact of the COVID19 in Social Vulnerability While Preserving Privacy of Participants from Minority Subpopulations

2021 ◽  
Vol 11 (7) ◽  
pp. 3110
Author(s):  
Karina Gibert ◽  
Xavier Angerri
Keyword(s):  
Social Vulnerability ◽  
Data Science ◽  
Policy Design ◽  
Data Driven ◽  
Informed Decision Making ◽  
Technological Infrastructure ◽  
Moderate Number ◽  
Reliable Diagnosis ◽  
Multivariate Relationships ◽  
The Impact

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized.

Abstract P99: Impact of the Covid-19 Pandemic on Stroke Thrombolysis Rate and Delay to Thrombolysis Treatment in a Regional Stroke Centre in London, UK

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Brian Drumm ◽  
Paul Bentley ◽  
Zoe Brown ◽  
Lucio D’Anna ◽  
Tsering Dolkar ◽  
...  
Keyword(s):  
Arrival Time ◽  
Negative Impact ◽  
Public Awareness ◽  
Stroke Severity ◽  
Time Intervals ◽  
Neurological Outcomes ◽  
Stroke Thrombolysis ◽  
Prehospital Delays ◽  
The Impact ◽  
Stroke Centre

Introduction: There are reports of changes in the numbers of stroke admissions and time intervals to receiving emergency treatments during the COVID-19 pandemic. We examined the impact of the COVID-19 pandemic on the stroke thrombolysis rate and delay to thrombolysis treatment in a regional stroke centre in London, UK. Methods: COVID-19 testing began at our hospital on 3 March 2020. Clinical data for all acute stroke admissions were routinely collected as part of a national Sentinel Stroke National Audit Programme (SSNAP) and all thrombolysis data were entered into our local thrombolysis database. We retrospectively extracted the relevant patient data for the period of March to May 2020 (COVID group) and compared to the same period in 2019 (pre-COVID group). Results: Compared with pre-COVID, there was a 17.5% fall in total stroke admissions (from 315 to 260) during COVID; but there were no significant differences in the demographics, stroke severity, proportions with known time of onset, or median onset-to-arrival time. The thrombolysis rates amongst ischemic strokes were not significantly different between the two groups (59/260=23% pre-COVID vs. 41/228=18% COVID, p=.19). For thrombolysis patients, their stroke severity and demographics were similar between the two both groups. Median onset-to-needle time was significantly longer by 22 minutes during COVID [127 (IQR 94-160) vs. 149 (IQR 110-124) minutes, p=.045]; this delay to treatment was almost entirely due to a longer median onset-to-arrival time by 16 minutes during COVID (p=.029). Favorable early neurological outcomes post-thrombolysis (defined as an improvement in NIHSS by ≥4 points at 24 hours) were similar (45% vs. 46%, p=.86). Conclusion: COVID-19 pandemic had a negative impact on prehospital delays which in turn significantly increased onset-to-needle time, but without affecting the chance of a favorable early neurological outcome. Our data highlight the need to maintain public awareness of taking immediate action when stroke symptoms occur during the COVID-19 pandemic.

AI Assisted High Fidelity Multi-Physics Digital Twin of Industrial Gas Turbines

2021 ◽  
Author(s):  
Senthil Krishnababu ◽  
Omar Valero ◽  
Roger Wells
Keyword(s):  
Gas Turbine ◽  
Data Storage ◽  
Gas Turbines ◽  
Building Blocks ◽  
Data Driven ◽  
High Fidelity ◽  
Test Field ◽  
Operating Characteristics ◽  
Digital Twin ◽  
The Impact

Abstract Data driven technologies are revolutionising the engineering sector by providing new ways of performing day to day tasks through the life cycle of a product as it progresses through manufacture, to build, qualification test, field operation and maintenance. Significant increase in data transfer speeds combined with cost effective data storage, and ever-increasing computational power provide the building blocks that enable companies to adopt data driven technologies such as data analytics, IOT and machine learning. Improved business operational efficiency and more responsive customer support provide the incentives for business investment. Digital twins, that leverages these technologies in their various forms to converge physics and data driven models, are therefore being widely adopted. A high-fidelity multi-physics digital twin, HFDT, that digitally replicates a gas turbine as it is built based on part and build data using advanced component and assembly models is introduced. The HFDT, among other benefits enables data driven assessments to be carried out during manufacture and assembly for each turbine allowing these processes to be optimised and the impact of variability or process change to be readily evaluated. On delivery of the turbine and its associated HFDT to the service support team the HFDT supports the evaluation of in-service performance deteriorations, the impact of field interventions and repair and the changes in operating characteristics resulting from overhaul and turbine upgrade. Thus, creating a cradle to grave physics and data driven twin of the gas turbine asset. In this paper, one branch of HFDT using a power turbine module is firstly presented. This involves simultaneous modelling of gas path and solid using high fidelity CFD and FEA which converts the cold geometry to hot running conditions to assess the impact of various manufacturing and build variabilities. It is shown this process can be executed within reasonable time frames enabling creation of HFDT for each turbine during manufacture and assembly and for this to be transferred to the service team for deployment during field operations. Following this, it is shown how data driven technologies are used in conjunction with the HFDT to improve predictions of engine performance from early build information. The example shown, shows how a higher degree of confidence is achieved through the development of an artificial neural network of the compressor tip gap feature and its effect on overall compressor efficiency.

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