Arrival Distribution Research Articles

Data‐driven flight time prediction for arrival aircraft within the terminal area

Predicting the arrival aircraft's flight time plays a critical role in effectively optimizing and scheduling spatial-temporal resources in the terminal airspace. This paper focuses on a data-driven method for predicting the arrival flight time. First, based on the existing research, a feature set is constructed from four aspects: initial state, arrival pressure, sequencing pressure, and wind information, which are believed to affect arrival flight time significantly. Second, eight widely used models are developed to predict flight time, including linear regression models, nonlinear regression models, and tree-based ensemble models. Furthermore, the stacking technique is adopted to improve the prediction performance. Finally, take Guangzhou Baiyun International Airport as a study case to verify the proposed method's effectiveness. The results indicate that the arrival pressure (describing the arrival traffic demand) and the sequencing pressure (sketching the arrival traffic distribution) could effectively improve the prediction accuracy. The mean absolute percentage error of the predicted flight time via ATAGA and IGONO can be increased by 1%. Besides, the proposed method of extracting wind data could also improve the prediction performance. The mean absolute error of the predicted flight time via GYA can be reduced by 4.85 s.

Third-order transport coefficients of ion swarms.

Mobility and diffusion coefficients are generally extracted from experimental measurements of ion arrival time distributions using tensors of ranks one and two, i.e., in terms of the diffusion equation that is equivalent to Fick's second law. The theory is extended here to tensors of rank three. It is shown that under customary circumstances, the generalized diffusion equation only contains a single third-order transport coefficient. This equation is used to generate synthetic data for ions moving through a pure gas. The mobility and diffusion coefficients and third-order transport coefficients inferred from these data are compared with values used to simulate the arrival time distribution. Finally, an existing computer program has been modified in order to compute one component of the third-order transport coefficient, and this program has been applied to Li+ in He.

Sharpness of the phase transition for parking on random trees

AbstractRecently, a phase transition phenomenon has been established for parking on random trees. We extend the results of Curien and Hénard on general Bienaymé–Galton–Watson trees and allow different car arrival distributions depending on the vertex outdegrees. We then prove that this phase transition is sharp by establishing a large deviations result for the flux of exiting cars. This has consequences on the offcritical geometry of clusters of parked spots which displays similarities with the classical Erdős–Renyi random graph model.

High-Resolution IMS-MS to Assign Additional Disulfide Bridge Pairing in Complementarity-Determining Regions of an IgG4 Monoclonal Antibody.

Monoclonal antibodies (mAbs) have taken on an increasing importance for the treatment of various diseases, including cancers and immunological disorders. Disulfide bonds play a pivotal role in therapeutic antibody structure and activity relationships. Disulfide connectivity and cysteine-related variants are considered as critical quality attributes that must be monitored during mAb manufacturing and storage, as non-native disulfide bridges and aggregates might be responsible for loss of biological function and immunogenicity. The presence of cysteine residues in the complementarity-determining regions (CDRs) is rare in human antibodies but may be critical for the antigen-binding or deleterious for therapeutic antibody development. Consequently, in-depth characterization of their disulfide network is a prerequisite for mAb developability assessment. Mass spectrometry (MS) techniques represent powerful tools for accurate identification of disulfide connectivity. We report here on the MS-based characterization of an IgG4 comprising two additional cysteine residues in the CDR of its light chain. Classical bottom-up approaches after trypsin digestion first allowed identification of a dipeptide containing two disulfide bridges. To further investigate the conformational heterogeneity of the disulfide-bridged dipeptide, we performed ion mobility spectrometry-mass spectrometry (IMS-MS) experiments. Our results highlight benefits of high resolution IMS-MS to tackle the conformational landscape of disulfide peptides generated after trypsin digestion of a humanized IgG4 mAb under development. By comparing arrival time distributions of the mAb-collected and synthetic peptides, cyclic IMS afforded unambiguous assessment of disulfide bonds. In addition to classical peptide mapping, qualitative high-resolution IMS-MS can be of great interest to identify disulfide bonds within therapeutic mAbs.

A muon-track reconstruction exploiting stochastic losses for large-scale Cherenkov detectors

IceCube is a cubic-kilometer Cherenkov telescope operating at the South Pole. The main goal of IceCube is the detection of astrophysical neutrinos and the identification of their sources. High-energy muon neutrinos are observed via the secondary muons produced in charge current interactions with nuclei in the ice. Currently, the best performing muon track directional reconstruction is based on a maximum likelihood method using the arrival time distribution of Cherenkov photons registered by the experiment's photomultipliers. A known systematic shortcoming of the prevailing method is to assume a continuous energy loss along the muon track. However at energies >1 TeV the light yield from muons is dominated by stochastic showers. This paper discusses a generalized ansatz where the expected arrival time distribution is parametrized by a stochastic muon energy loss pattern. This more realistic parametrization of the loss profile leads to an improvement of the muon angular resolution of up to 20% for through-going tracks and up to a factor 2 for starting tracks over existing algorithms. Additionally, the procedure to estimate the directional reconstruction uncertainty has been improved to be more robust against numerical errors.

Leveraging Hypothesis Testing for CSI Based Passive Human Intrusion Direction Detection

Due to the pervasive deployment of Wi-Fi devices, Wi-Fi-based human intrusion detection has attracted intensive attention in recent years. By analyzing temporal changes of signal caused by human movement, the existing solutions can achieve passive human intrusion detection. However, the detection of intrusion direction, from which the person intrudes the area of interests, is still a challenging task. Considering the influence of intrusion direction on the energy and spatial statistical distributions of multiple reflections, in this study, we propose WIDD, a Wi-Fi-based passive human intrusion direction detection system. Concretely, WIDD first extracts the angle of arrival (AoA) and amplitude distributions of the multipath signal and conducts the Jarque-Bera (JB) test to analyze the normality of the obtained distributions. A novel joint hypothesis testing algorithm is proposed to monitor changes in amplitude and AoA distributions of reflections and realize the human intrusion detection based on the analysis result. At last, it finds the reflection affected by the human intrusion most via the divergence evaluation and extracts the corresponding AoA to realize the intrusion direction detection. The comprehensive experimental evaluation demonstrates that WIDD provides human intrusion direction detection accuracy of about 0.943 and 0.931, under the scenario where the transmitter and receiver are separated by the glass and brick wall, respectively, outperforming the state-of-the-art solutions and shedding bright lights on ubiquitous human detection in practice.

Spray Characteristics of Standard and Alternative Aviation Fuels at Cold-start Conditions

The spray characteristics of standard and alternative aviation fuels created by a hybrid pressure-swirl airblast (HPSA) atomizer were investigated at conditions corresponding to engine cold start for an aviation gas turbine. Drop size and drop velocity of sprays from three different fuels, Jet-A (A-2), JP-5 (A-3), and a JP-5/farnesane blend (C-3) were investigated using a phase Doppler anemometry. The spatially resolved drop measurements were obtained at three axial locations downstream of the swirler exit plane and up to 30 mm in the radial direction from the spray centerline. Smaller , , and were observed when increasing the pressure drop across the atomizer swirler (). Increasing also increased the mean axial velocity. No definitive trend was observed with viscosity on drop size, while liquid–gas surface tension showed a weak correlation with drop size due to its small variation in value among fuels. The spray unsteadiness was examined using interparticle arrival time distribution functions. A semi-empirical model was also developed using a phenomenological three-step model for the atomization process of the HPSA atomizer and was used to predict drop-size values at engine cold-start conditions in this work and near lean blowout conditions presented in earlier work.

First implications of Tibet ASγ data for heavy dark matter

Extensive air shower detectors of gamma rays in the sub-PeV energy region provide a new and relatively unexplored window for dark matter searches. Here we derive some implications of the recently published Tibet AS$_\gamma$ data for decaying dark matter candidates. The available spectral information is already useful in obtaining competitive constraints, surpassing existing limits above 10 PeV mass for hadronic or massive boson final states. This is particularly true if accounting for a benchmark astrophysical background of Galactic cosmic rays in the (0.1-1) PeV range. By relying on the arrival distribution of the photons, we show that significantly better sensitivity can be attained, comparable or better than IceCube also for most leptonic final states. Full data exploitation requires however further information disclosure.

DOI estimation through signal arrival time distribution: a theoretical description including proof of concept measurements

The challenge to reach 10 ps coincidence time resolution (CTR) in time-of-flight positron emission tomography (TOF-PET) is triggering major efforts worldwide, but timing improvements of scintillation detectors will remain elusive without depth-of-interaction (DOI) correction in long crystals. Nonetheless, this momentum opportunely brings up the prospect of a fully time-based DOI estimation since fast timing signals intrinsically carry DOI information, even with a traditional single-ended readout. Consequently, extracting features of the detected signal time distribution could uncover the spatial origin of the interaction and in return, provide enhancement on the timing precision of detectors. We demonstrate the validity of a time-based DOI estimation concept in two steps. First, experimental measurements were carried out with current LSO:Ce:Ca crystals coupled to FBK NUV-HD SiPMs read out by fast high-frequency electronics to provide new evidence of a distinct DOI effect on CTR not observable before with slower electronics. Using this detector, a DOI discrimination using a double-threshold scheme on the analog timing signal together with the signal intensity information was also developed without any complex readout or detector modification. As a second step, we explored by simulation the anticipated performance requirements of future detectors to efficiently estimate the DOI and we proposed four estimators that exploit either more generic or more precise features of the DOI-dependent timestamp distribution. A simple estimator using the time difference between two timestamps provided enhanced CTR. Additional improvements were achieved with estimators using multiple timestamps (e.g. kernel density estimation and neural network) converging to the Cramér–Rao lower bound developed in this work for a time-based DOI estimation. This two-step study provides insights on current and future possibilities in exploiting the timing signal features for DOI estimation aiming at ultra-fast CTR while maintaining detection efficiency for TOF PET.

Real-time dispatching of air taxis in metropolitan cities using a hybrid simulation goal programming algorithm

Expected to begin its operations in the coming years, air taxi aims to provide everyday transportation services to customers in metropolitan cities. These vehicles offer urban air mobility (UAM) services using the electric vertical takeoff and landing (eVTOL) technology. This research is the first to present a hybrid simulation goal programming (HSGP) approach to dispatch vehicles in a centralized air taxi network. After each customer drop-off, the model makes real-time decisions on (i) whether the air taxi must become idle or pick up customers, and (ii) the station to which the air taxi should be dispatched (if the air taxi is operational). The feasibility of the HSGP approach is tested using potential air taxi demands in New York City (NYC) provided by a prior study. The results of the experimentation suggest that the minimum number of air taxis required for efficient operation in NYC is 84, functioning with an average utilization rate of 66%. In addition, the impacts of commuter’s “willingness to fly” rate, percentage of demand fulfillment, on-road travel limit, maximum customer wait time, and arrival distribution on the optimal number of air taxis, utilization rate, number of customers served, and cost incurred per customer are examined. Analyses show that the “willingness to fly” rate appears to have a linear influence on the number of air taxis and the efficiency, while on-road travel distance has an exponential impact on the performance measures. The HSGP algorithm developed in this paper can be used by any company that is interested in venturing into the air taxi market.

High-Resolution Ion-Mobility-Enabled Peptide Mapping for High-Throughput Critical Quality Attribute Monitoring.

Characterization and monitoring of post-translational modifications (PTMs) by peptide mapping is a ubiquitous assay in biopharmaceutical characterization. Often, this assay is coupled to reversed-phase liquid chromatographic (LC) separations that require long gradients to identify all components of the protein digest and resolve critical modifications for relative quantitation. Incorporating ion mobility (IM) as an orthogonal separation that relies on peptide structure can supplement the LC separation by providing an additional differentiation filter to resolve isobaric peptides, potentially reducing ambiguity in identification through mobility-aligned fragmentation and helping to reduce the run time of peptide mapping assays. A next-generation high-resolution ion mobility (HRIM) technique, based on structures for lossless ion manipulations (SLIM) technology with a 13 m ion path, provides peak capacities and higher resolving power that rivals traditional chromatographic separations and, owing to its ability to resolve isobaric peptides that coelute in faster chromatographic methods, allows for up to 3× shorter run times than conventional peptide mapping methods. In this study, the NIST monoclonal antibody IgG1κ (NIST RM 8671, NISTmAb) was characterized by LC-HRIM-MS and LC-HRIM-MS with collision-induced dissociation (HRIM-CID-MS) using a 20 min analytical method. This approach delivered a sequence coverage of 96.5%. LC-HRIM-CID-MS experiments provided additional confidence in sequence determination. HRIM-MS resolved critical oxidations, deamidations, and isomerizations that coelute with their native counterparts in the chromatographic dimension. Finally, quantitative measurements of % modification were made using only the m/z-extracted HRIM arrival time distributions, showing good agreement with the reference liquid-phase separation. This study shows, for the first time, the analytical capability of HRIM using SLIM technology for enhancing peptide mapping workflows relevant to biopharmaceutical characterization.

Masked Multiplexed Separations to Enhance Duty Cycle for Structures for Lossless Ion Manipulations.

The experimental paradigm of one ion packet release per spectrum severely hinders throughput in broadband ion mobility spectrometry (IMS) systems (e.g., drift tube and traveling wave systems). Ion trapping marginally mitigates this problem, but the duty cycle deficit is amplified when moving to high resolution, long pathlength systems. As a consequence, new multiplexing strategies that maximize throughput while preserving peak fidelity are essential for high-resolution IMS separations [e.g., structures for lossless ion manipulations (SLIMs) and multi-pass technologies]. Currently, broadly applicable deconvolution strategies for Hadamard-based ion multiplexing are limited to a narrow range of modulation sequences and do not fully maximize the ion signal generated during separation across an extended path length. Compared to prior Hadamard deconvolution errors that rely upon peak picking or discrete error classification, the masked deconvolution matrix technique exploits the knowledge that Hadamard transform artifacts are reflected about the central, primary signal [i.e., the true arrival time distribution (ATD)]. By randomly inducing mathematical artifacts, it is possible to identify spectral artifacts simply by their high degree of variability relative to the core ATD. It is important to note that the deweighting approach using the masked deconvolution matrix does not make any assumptions about the underlying transform and is applicable to any multiplexing strategy employing binary sequences. In addition to demonstrating a 100-fold increase in the total number of ions detected, the effective deconvolution of data from 5, 6, 7, and 8-bit pseudo-random sequences expands the utility and efficiency of the SLIM platform.

The TeV Cosmic-Ray Bump: A Message from the Epsilon Indi or Epsilon Eridani Star?

A recently observed bump in the cosmic-ray (CR) spectrum from 0.3 to 30 TV is likely caused by a stellar bow shock that reaccelerates preexisting CRs, which further propagate to the Sun along the magnetic field lines. Along their way, these particles generate an Iroshnikov-Kraichnan (I-K) turbulence that controls their propagation and sustains the bump. Ad hoc fitting of the bump shape requires six adjustable parameters. Our model requires none, merely depending on three physical unknowns that we constrain using the fit. These are the shock Mach number, M, its size, l ⊥, and the distance to it, ζ obs. Altogether, they define the bump rigidity R 0. With M ≈ 1.5-1.6 and R 0 ≈ 4.4 TV, the model fits the data with ≈0.08% accuracy. The fit critically requires the I-K spectrum predicted by the model and rules out the alternatives. These attributes of the fit make an accidental agreement highly unlikely. In turn, the R 0 and M derived from the fit impose the distance-size relation on the shock: . For sufficiently large bow shocks, l ⊥ = 10-3-10-2 pc, we find the distance of ζ obs = 3-10 pc. Three promising stars in this range are the Scholz's Star at 6.8 pc, Epsilon Indi at 3.6 pc, and Epsilon Eridani at 3.2 pc. Based on their current positions and velocities, we propose that Epsilon Indi and Epsilon Eridani can produce the observed spectral bump. Moreover, Epsilon Eridani's position is only ~6°.7 off of the magnetic field direction in the solar neighborhood, which also changes the CR arrival direction distribution. Given the proximity of these stars, the bump appearance may change in a relatively short time.

Quantum dynamics under continuous projective measurements: Non-Hermitian description and the continuum-space limit

The problem of the time of arrival of a quantum system in a specified state is considered in the framework of the repeated measurement protocol and in particular the limit of continuous measurements is discussed. It is shown that for a particular choice of system-detector coupling, the Zeno effect is avoided and the system can be described effectively by a non-Hermitian effective Hamiltonian. As a specific example we consider the evolution of a quantum particle on a one-dimensional lattice that is subjected to position measurements at a specific site. By solving the corresponding non-Hermitian wave function evolution equation, we present analytic closed-form results on the survival probability and the first arrival time distribution. Finally we discuss the limit of vanishing lattice spacing and show that this leads to a continuum description where the particle evolves via the free Schrodinger equation with complex Robin boundary conditions at the detector site. Several interesting physical results for this dynamics are presented.

Data-Driven Robust Aircraft Assignment to Minimize Delay Propagation

Flight delays have a significant impact on an airline's operating cost including increased expenses for crew, fuel, and maintenance. Propagated delays due to late arriving aircraft contribute to 40% of all flight delays as reported by the Bureau of Transportation Statistics. The robust aircraft assignment problem is to assign tail numbers on scheduled arriving flights at an airport to scheduled departing flights at the same airport with the objective of minimizing propagated delays. In this paper, we propose a new data-driven approach for the robust aircraft assignment problem by formulating it as a balanced assignment problem between incoming and outgoing flights flown by the same aircraft type at a single airport. We consider both deterministic and stochastic versions of the aircraft assignment problem. In the deterministic case, we prove the optimality of the First-in-First-out (FIFO) assignment policy under two different performance measures, justifying the use of the FIFO policy as a benchmark. In the stochastic case, we show that the FIFO assignment policy is no longer optimal and propose the rFIFO and stochastic assignment formulations based on the mean and empirical distribution of arrival delay, respectively. We propose a data-driven approach to estimate the assignment costs by using empirical observations of arrival delays from prior years' flight records to compute the empirical propagated delay distribution. We propose a data-driven clustering method to account for factors such as originating airport, time of day, and aircraft type that affect the arrival delay distribution. These empirical cluster based aircraft assignment costs serve as an input to our stochastic assignment model. These assignment costs are then used to derive the optimal aircraft assignment for the rFIFO and stochastic assignment policies for an out of sample data set from 2018 for Delta at Atlanta airport. We show that both the rFIFO and stochastic assignment policies derived from the data-driven approach perform better than the benchmark FIFO assignment. We also show that for all Delta airline flights at Atlanta airport from July to September in 2018, the stochastic assignment policy yields a roughly 40% improvement in total actual propagated delay over the actual airline assignment while reducing the fraction of delayed flights due to propagated delays from 6.58% to 2.62%, thus potentially saving approximately 14 million dollars in flight delay related annual operating costs. We conclude that incorporating the stochastic nature of arrival delays in solving the robust aircraft assignment problem, and combining it with a data-driven approach can potentially help an airline significantly reduce its operating costs due to flight delays, improve passenger convenience and experience, and help the environment by reducing emissions.

Ultrahigh-energy cosmic rays dipole and beyond

Recent data from the Pierre Auger Observatory has revealed the presence of a large-scale dipole in the arrival direction distribution of ultra-high energy cosmic rays (UHECR). In this work, we build up an understanding of the diffusive origin of such a dipolar behavior as well as its dependency on energy and astrophysical source assumptions such as extragalactic magnetic field strength and cosmic ray composition. We present a novel analytical approach for calculating the angular distribution of CR coming from a single source and discuss the regimes in which the steady-state dipole result is expected. We also present a semianalytical method for calculating the evolution with energy of the resultant dipole for an ensemble of sources. We show that a local source allows for a strong growth of the dipole with energy over a large energy range. The possibility of a transition from a dipolar to non-dipolar regime at the highest energies and its implications for the source density, magnetic field intensity, and cosmic ray composition are discussed.

Ballooning multi-armed bandits

In this paper, we introduce ballooning multi-armed bandits (BL-MAB), a novel extension of the classical stochastic MAB model. In the BL-MAB model, the set of available arms grows (or balloons) over time. In contrast to the classical MAB setting where the regret is computed with respect to the best arm overall, the regret in a BL-MAB setting is computed with respect to the best available arm at each time. We first observe that the existing stochastic MAB algorithms result in linear regret for the BL-MAB model. We prove that, if the best arm is equally likely to arrive at any time instant, a sub-linear regret cannot be achieved. Next, we show that if the best arm is more likely to arrive in the early rounds, one can achieve sub-linear regret. Our proposed algorithm determines (1) the fraction of the time horizon for which the newly arriving arms should be explored and (2) the sequence of arm pulls in the exploitation phase from among the explored arms. Making reasonable assumptions on the arrival distribution of the best arm in terms of the thinness of the distribution's tail, we prove that the proposed algorithm achieves sub-linear instance-independent regret. We further quantify explicit dependence of regret on the arrival distribution parameters. We reinforce our theoretical findings with extensive simulation results. We conclude by showing that our algorithm would achieve sub-linear regret even if (a) the distributional parameters are not exactly known, but are obtained using a reasonable learning mechanism or (b) the best arm is not more likely to arrive early, but a large fraction of arms is likely to arrive relatively early.

Passenger arrival distribution model and riding guidance on an urban rail transit platform

Existing methods to improve the operational efficiency and service level of urban rail transit (URT) systems focus on forecasting passenger flow in macro networks and optimizing metro vehicle operation strategies. Among the factors affecting passenger comfort in vehicles, the passenger arrival distribution is the most direct but is not sufficiently taken into account. This study provides a riding guidance method for passengers on the URT platform to improve the uniformity of the passenger distribution among carriages. First, a passenger arrival distribution model is proposed on the basis of the influences of the queue length at the door, the number of people in the carriage and the travel distance on the passengers’ choice of carriage. Second, taking the maximum spatial comfort degree of every passenger as an objective, a passenger riding guidance model on the URT platform is proposed. Hefei Metro Line 2 is taken as an example for the numerical case study, and the results show that the guidance method can balance the passenger flow distribution and improve the spatial comfort level of passengers when the passenger flow is approximately 65 to 135 persons per 5 min.

Strategic arrivals to a queue with service rate uncertainty

We study the problem of strategic choice of arrival time to a single-server queue with opening and closing times when there is uncertainty regarding service speed. A Poisson population of customers choose their arrival time with the goal of minimizing their expected waiting times and are served on a first-come first-served basis. There are two types of customers that differ in their beliefs regarding the service-time distribution. The inconsistent beliefs may arise from randomness in the server state along with noisy signals that customers observe. Customers are aware of the two types of populations with differing beliefs. We characterize the Nash equilibrium dynamics for exponentially distributed service times and show how they substantially differ from the model with homogeneous customers. We further provide an explicit solution for a fluid approximation of the game. For general service-time distributions we provide an algorithm for computing the equilibrium in a discrete-time setting. We find that in equilibrium customers with different beliefs arrive during different (and often disjoint) time intervals. Numerical analysis further shows that the mean waiting time increases with the coefficient of variation of the service time. Furthermore, we present a learning agent-based model (ABM) in which customers make joining decisions based solely on their signals and past experience. We numerically compare the long-term average outcome of the ABM with that of the equilibrium and find that the arrival distributions are quite close if we assume (for the equilibrium solution) that customers are fully rational and have knowledge of the system parameters, while they may greatly differ if customers have limited information or computing abilities.

Dynamic evacuation optimization model based on conflict-eliminating cell transmission and split delivery vehicle routing

This paper studies dynamic evacuation optimization in the context of a new cell transmission with the split delivery vehicle routing problem (SDVRP) of multiple modes of buses and cars. A bi-directional multilane conflict-eliminating cell transmission model (BCECTM) is proposed to simulate the mixed traffic dynamic evacuation process on links and at intersections. The method of SDVRP is applied to manage dynamic evacuation demands and optimize the routing of multiple modes. The mechanisms of vehicle transmission, traveling trajectory, and residual capacity updating are depicted based on the models of BCECTM and SDVRP; thus, a mathematical programming model aiming to minimize the total evacuation time is established by integrating the BCECTM and SDVRP. A genetic algorithm with a chromosome position-coding scheme is designed to solve the evacuation optimization model. Case studies show that the proposed model can reasonably assign evacuation demands that gather at some sites around the event to, and effectively evacuate people by, buses and cars so that the capacities of buses are fully utilized, and can generate optimal vehicle traveling trajectories to improve the evacuation performance. Furthermore, the evacuation and congestion degree under different demand levels and different arrival distributions are analyzed with meaningful conclusions.